Cognition and Instruction/Print version

Preface

There is a significant body of research and theory on how cognitive psychology can inform teaching, learning, instructional design and educational technology. This book is for anyone with an interest in that topic, especially teachers, designers and students planning careers in education or educational research. It is intended for use in a 13-week undergraduate course and is structured so students can study one chapter per week. The book is more brief and concise than other textbooks about cognition and instruction because it is intended to represent only knowledge that can be mastered by all students in a course of that duration. The book prepares students who wish to pursue specialized interests in the field of cognition and learning but is not a comprehensive or encyclopedic resource.

The need for brevity has forced difficult decisions about what topics to include. We have chosen to exclude giftedness, special education, learning disabilities, autism spectrum disorder, and related topics. These aspects of educational psychology, so important for teachers, deserve fuller treatment than can be given here. For similar reasons we have mostly excluded the important topics of classroom management and assessment of learning. The book has no coverage of Piaget's stage theory of cognitive development (or any other stage theory) as decades of research have qualified and limited its reach to the point where it contributes little to our current understanding of cognitive learning processes in educational contexts.

The later chapters in the book are dedicated to cognitive aspects of learning in the subjects of reading, mathematics and science. There are plans to add another chapter on writing. These chapters are intended for all students of cognition and instruction, not only those who will specialize in these subjects. Each subject-oriented chapter deals with cognitive phenomena that are particularly salient in one subject but also play a role in other subjects. For example, the barriers to learning presented by persistent, alternative conceptions acquired from prior experience have most often been studied in the context of science education but appear in many other contexts. Although there is no chapter on history and social studies, theory and research relevant to that subject is introduced in the chapters that deal with critical thinking, argumentation and learning from text and multimedia.

Theories of Learning & Development

This chapter is about the origins of and influences on cognitive psychology.

Origins in Philosophy

Nature Vs Nurture

Nature versus nurture has been the debate on psychological development between theorists for over 2000 years and is commonly seen as rival factors. The debate is whether children develop their psychological characteristics based on genetics, which is nature, or how they were raised and their environment, which is nurture. It is difficult to say whether one theory has more influence over the other but “as of now, we know that both nature and nature play important roles in human development.”

To break down each theory for a better understanding, nature refers to an individual's heredity, genetics, biological processes, and maturation. The coding of genes in each human cell determines the different physical traits humans possess. For example, height, hair colour, eye colour, etc., are gene-codes in a human's DNA. The theory of nurture refers to environmental contexts that influences development such as education, parenting, culture, and social policies. Examples of nurture are more abstract attributes such as personality, behaviour, and intelligence.

Genetic characteristics are not always obvious, however, they become conspicuous through the course of maturation. Maturation can only occur with the support of a healthy environment. The theory of nurture “holds that genetic influence over abstract traits may exist; however, the environmental factors are the real origins of our behavior”. Nature's partner is nurture and nature never works independently. A good example is in the comparison of fraternal twins who were raised apart from one another, they will most likely have a significant amount of similarities in their behaviour. However, the environment each twin was raised in will greatly influence their behavior as well. Today, the environment and the biological factors are seen as critical and emphasized as complex co-actions.

Behaviourism

Behaviourism is a psychological approach directed towards the individual's behaviour; many of these behaviours are learned through conditioning and modeling. Through experience, people develop their language, emotions, and personalities. Some theories that are relevant toward the behavioural development of people are operant conditioning, classical conditioning, and modeling.

Operant Conditioning

Operant conditioning is the type of learning that is determined and influenced by consequences. The consequences can be both positive and negative, as well as rewarding and punishing. In the context of operant conditioning, positive does not necessarily mean a good thing; it means the addition of something following an action. For example, a child does not make it home before their nightly curfew so their parents punish them with requiring them to complete more house chores. In opposition, a negative consequence is the removal of something following an action. An example of negative reward is when a child does significantly well in school, receiving high report card grades, resulting in their parents removing the amount of house chores the child have to complete that day. Rewards influence the increase of certain behaviours while punishment should reduce the amount of the behaviours.

One of the most well-known researchers in this field is B. F. Skinner. Skinner did work with several animal species and was very successful in his research. His perspectives were simple, but he believed that human beings were too complex for the classical conditioning approach (explained in the following section). One of his main studies was called the Skinner's Box, and found consistent results in rats, cats, and pigeons. The animals were put in the box with a button or lever to press, while hungry. The animals were rewarded intermittently whenever they pressed the button or lever. As a result, there was an increase in the behaviour (pushing the button) as they were rewarded. This has been proven in many studies, as well as in our daily lives. For example, look at how parents raise their children.

Role of Models

Modeling is one of the most commonly used form of teaching and is one of the most successful forms of learning. This type of learning works by imitation alone. Many people might also know of this by the term of vicarious learning; learning and developing behaviours by observing other people. When we enter new situations, for example the first time in a formal restaurant, we follow the cues of the people around us. This is just one form of modeling seen easily in everyday situations.

Children are the best at this, even when we do not always want them to be. Children will mimic their peers and parents, things they watch on TV and hear in songs. Alberta Bandura was one of the first major researchers in this field of study. He was working with children in an experiment called the Bobo Doll; in which children watched a model play with this doll, some in an aggressive way and others were neutral. After watching the video, the children were put in a room with a Bobo Doll and other decoy objects. More children were aggressive towards the doll and added novel actions into their play; such as using weapons and adding verbal aggression.

Conditioning and modeling are a few different approaches to the development of learning in the field of psychology. They have been studied for hundreds of years and are continually being explored for their accuracy and truths.

Cognitive psychology

Cognitive psychology focuses on mental activities and processes. This encompasses areas of mental activity such as learning, remembering, problem solving, and perception and attention.

Attention

Attention is a cognitive function that is fundamental for the human behavior. It is the ability of selectively concentrating on external or internal information. Attention “is the prerequisite to learning and a basic element in classroom motivation and management”.

For years, attention has been a subject of examination and there has been curiosity towards finding out where the origin of the sensory cues, signals, and the functions relate to attention.

Attention is a valuable skill most people possess, however it is a skill that oscillates. Attention can be performed unconsciously or voluntary. The level of concentrating is affected by one's surroundings and environment. There are also differences in attention such as selective attention: meaning one will select the most important information out of the given context. Also, there is divided attention: meaning separating ones focus in situations where two tasks are performing at the same time, in other words multi tasking.

Although paying attention may seem as easy as getting rid of distractions, focusing, organizing, and prioritizing ones thoughts, it is not that easy for everyone. Children who are affected by attention disorders such as dyslexia or attention deficit and hyperactivity disorder (ADHD) experience symptoms that cause difficulty in their learning development. Early signs of attention disorder in children can make their daily lives and learning more challenging than the average child.

Critical Thinking

Critical thinking is “reflective thinking focused on deciding what to believe or do.” It is the ability to think rationally and surely. When thinking critically, the goal is not to solve the problem but to obtain more knowledge and better understand the problem. The purpose of critical thinking allows people to evaluate information and authorizes them to make informed choices and decisions. Someone who possesses critical thinking skills are able to gather, interpret, and evaluate information to make informed decisions. They can construct arguments, solve problems systematically, see and understand the importance of ideas and the connections, and they can reflect on their own beliefs and values.

Critical thinking should not be mistaken for problem solving because it differs in two ways. When problem solving, the process involves solving well-defined problems from a specific domain. However, critical thinking usually involves better understanding of ill-defined problems in several domains. Lastly, critical thinking differs from how it is being evaluated. Most problems that involve problem solving are external states, while critical thinking involves internal states.

Information Processing Theory

In the early 1950s, researchers developed a model called the Information-processing model to understand how the human mind processes information.  Although there are other models such as the Modal Model, the Information-processing model is known to be the best and most researched. This model consists of three main branches: sensory memory, working memory and long-term memory.

Sensory memory processes information for a very short period of time from about 0.5–3 seconds. The process is so short; one can only remember five to nine discrete elements. An example of sensory memory is when one tries to remember a phone number for a brief period of time, just enough time to write it down. There is only a limited amount of information that can be processed in sensory memory because its main purpose is to screen the most relevant incoming stimuli at the given time.

After the process of sensory memory, the information will either be transmitted into working memory or be forgotten. In the process of working memory, “information is assigned meaning, linked to other information, and essential mental operations such as inferences are performed”. An example is when one is learning to drive a car; one must perform the task repeatedly until it become automatic, which leads to long-term memory.

Working memory and sensory memory are limited capacity for information, whereas long-term memory has no limitations. The purpose of long-term memory is to “provide a seemingly unlimited repository for all the facts and knowledge in memory” and is said to have the capability to hold millions of pieces of information at a time.

Constructivism

Constructivist theories revolve around the belief that learning is a constructive process. Humans generate knowledge and meaning from the interaction between their experiences and their ideas. New information is built upon prior knowledge, and people are constructing their own representations of knowledge based off that prior knowledge as well as new information.

Individual and Social Learning

Individual learning places the emphasis on learning in a more independent manner, while social learning shifts the focus to learning on a wider scale, through the social interaction between both peers and teachers. A large part of constructivist learning is that it acknowledges the uniqueness of each individual.

Social learning helps individuals learn in a way that individual learning cannot. Vygotskian theory includes the notion of collaborative learning among individuals, to share understanding of material. The zone of proximal development, according to Vygotsky, is "the distance between the actual developmental level as determined by independent problem solving and the level of potential development as determined through problem solving under adult guidance, or in collaboration with more capable peers". By using peer-to-peer interactions, students may better understand material through the support of classmates or those who are on the same learning ‘level’, than that of someone who has a higher skill level. An example of this would be that of a typical math classroom, where one student who is performing poorly in class, asks for clarification on certain methods and formulas from a fellow student who is performing better. The higher performing student understands how to communicate ideas more to the level of a typical student, hence the zone of proximal development.

Nature of Learning (Responsibility and Motivation)

The learners themselves hold a certain amount of responsibility when it comes to learning and understanding material. They must be involved with the learning process, even more so than the instructor. Acquiring and comprehending the material in their own terms is the responsibility of the student, not simply rote memorizing what they have learned. The only person that can pin point the strengths and weaknesses of a student, is the student themselves. The responsibility of making sense of information and trying to find sources of motivation ultimately falls on the shoulders of the student. In regards to the classroom environment, the concept of shared responsibility is a good way to encourage students to perform to the best of their ability. Focusing in a certain direction to give a clear purpose, and giving students the chance to reflect on themselves as well as to collaborate helps students in accomplishing their goals.

Motivation also builds upon the learner's responsibility, affecting their potential for learning and confidence of self. Hard-to-grasp, extremely challenging work has shown to often discourage the learner from understanding new information and work that is too easy often bores the learner. For this reason, it is important for teachers to find that sweet spot that challenges the learner just enough, and provides the buffer and motivation to learn new material.

Role Of Facilitators

Following a constructivist view, the role of facilitator is not the same as a teacher. Avoiding the lecture style of most teachers, the role of a facilitator is to encourage discussion and ask questions. The main difference here for the student, is to take part in the active learning process and not sit idly as the teacher speaks. Encouraging peers to interact with each other, take part in class discussion, and giving guided questions as well as other methods, all fall under the role of the facilitator. Creating rapport with the students and knowing when to give and when to stop scaffolding is essential in aiding the student to think for themselves without giving them too much assistance. For example, instead of blatantly giving away the answer to a math problem, a possible means of scaffolding could include asking the student to try a method they went over in an earlier class or possibly guide the student slowly through the problem and letting them solve a certain part before going onto the next.

To a certain degree, it is also important for the teacher to create a positive teacher-student relationship, as this can impact the learner's belief of self, which is especially critical for high-risk students. Frequent negative feedback from the teacher can often give the student a negative view of themselves, and as such, it is important to show the student what they did right, rather than what they did wrong.

Constructivism In The Classroom

Constructivism in classroom settings, usually follows the pattern of switching focus from the instructor to the students. The main value that constructivism follows is problem solving. The teacher acts as a guide to provide the students with the opportunities needed to understand material. There is an emphasis placed on the cultural backgrounds of students and the social interaction or collaborative learning among each other. Interaction discussions are usually facilitated and directed by the teacher, clarifying confusing concepts and materials to the students by acting as the overseer. Situated learning can also follow this form of facilitation, which can be defined as learning being applied within the context it is learned. For example, culinary students cooking in the kitchen as they listen to the instructor who oversees their work, rather than sitting in a classroom taking notes on the culinary arts.

Some methods of utilizing constructivism in classrooms are reciprocal teaching, cooperative learning, anchored instruction as well as encouraging group discussion and teamwork. Reciprocal teaching involves the creation of a collaborative group among 2-3 students, plus a teacher, and take turns discussing the topic at hand. This creates a zone of proximal development. Cooperative learning is similar in that higher skilled students help other students by working in their zone of proximal development. Anchored instruction involves creating lessons revolved around a topic of interest to the students. Doing this engages the student and encourages more thoughtful engagement in discussions when discussing a topic students feel strongly about.

Influences from Humanistic Psychology

Humanism is a more personal approach to learning which focuses on the learner's ability to self-actualize, as well as, their own natural desire to fulfill their potential.

Facilitation Theory

The facilitation theory was coined by Carl Rogers. His beliefs were that humans were naturally curious and that every human being is ‘good’ by nature. Learning is a process that is done through experimenting and interacting through activity. His facilitation theory views the teacher as the facilitator and not as a walking textbook. As a result of this, it is important that the teacher has the proper rapport and attitude when teaching students. Rogers states that there are three qualities, also known as core conditions, that are needed for proper facilitation. The first condition is called realness, which is the teachers' ability to act as themselves and not another persona. The second is trust, and the teacher's ability to actually care for the student. The final requirement is the teachers' ability to empathize and visualize themselves in another person's shoes.

Conclusion

There are many different types of theories involved in the learning and development process that all focus on different beliefs and views. These theories are primarily explained by the interactions of learners, the building of knowledge upon prior experiences, and the ability to construct understanding in an attempt to realize and accomplish learning within a classroom environment.

Glossary

Attention - the act or faculty of attending, especially by directing the mind to an object.

Behaviourism - A school of psychology that regards the objective observation of the behaviour of organisms (usually by means of automatic recording devices) as the only proper subject for study and that often refuses to postulate any intervening mechanisms between the stimulus and the response

Cognitive load - Refers to the total amount of mental effort being used in the working memory.

Collaborative learning - A situation in which two or more people learn or attempt to learn something together.

Constructivism - A theory of knowledge that argues that humans generate knowledge and meaning from an interaction between their ideas and experiences.

Modeling - A standard or example for imitation or comparison

Object permanence - knowing that an object still exists, even if the object is not in sight.

Operant conditioning - A process of behaviour modification in which a subject is encouraged to behave in a desired manner through positive or negative reinforcement, so that the subject comes to associate the pleasure or displeasure of the reinforcement with the behaviour.

Situated learning - Learning that takes place in the same context it can be applied in, such as workshops, kitchens, field trips to archaeological digs, etc. .

Zone of Proximal Development - is the difference between what a learner can do without help and what he or she can do with help

Suggested Readings

• Driscoll, M. (2005). Psychology of Learning for Instruction, 2nd ed, Chapter 10
• Hartley, P., Hilsdon, J., Keenan, C., Sinfield, S., & Verity, M. (2011). Learning development in higher education. Basingstoke, Hampshire: Palgrave Macmillan.
• Salomon, G., & Perkins, D. N.. (1998). Individual and Social Aspects of Learning. Review of Research in Education, 23, 1–24.

Learning and Memory

Learning and memory are fundamental behind understanding cognitive processing, but are often confused for one another. Although the relationship between the two are clearly related and very much dependent on each other, learning and memory are still two distinct topics that require appropriate attention in order to comprehend them. The following chapters will examine the concepts behind learning and memory, from the approach of cognitive psychology. In other words, our focus will be placed on how humans process information, through series of approaches, such as perception, attention, thinking, and memory. We first begin by presenting the theory of multimedia learning as a way to introduce and identify a link between learning and memory. We then move on to discussing how human thoughts work, by using the idea of information processing. The next chapters will examine in detail how memories are structured, as well as the cognitive processes associated with them. We believe that these concepts are imperative in understanding how to achieve meaningful learning. Finally, the chapter assesses the relationship between learning and memory as a means of improving the quality of learning and teaching.

Learning

Many theorists and psychologists attempts to determine the definition of learning and its processes. Three perspectives in particular have been widely recognized to view learning through a western outlook and have been major contributions to the study of learning and educational practices. The three are the behaviourist, constructivist, and the cognitive perspectives . The focus of this chapter will be to examine learning through a cognitive psychologist’s view, and in close association with the memory process. The human experience of learning becomes one that involves the active construction of meaning. But in order to construct meanings, human cognition first needs to understand how information is acquired and processed in memory. Researchers describes learning as how information is processed, encoded, and stored . In other words these three processes, are performed in sequence with how one perceives, learns, thinks, understands, and retains information. Information on these three processes will be presented in much more detail as we move further along this chapter. However, as an introduction, it is under the assumption of cognitive researchers that learning is first obtained through the senses, such as sight, hearing, and touch. This chapter will begin with Richard Mayer's theory of multimedia learning in order to determine how sensory inputs work hand in hand with learning and memory.

Working Memory

Figure 1. This is a FMRI scan of a brain during working memory task.

Many types of developmental disabilities can be traced at least partially to problems with the memory. Problems with working memory subsystems seem to lie behind the way in which patients with autism become confused over large amounts of information, and deficiencies in working memory are also implicated in attention deficit hyperactivity disorder. A number of other developmental disabilities, such as Williams Syndrome, Down syndrome, and dyslexia can also be connected with improper functioning of memory. Below we focus on autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) because the role of memory in these two disorders has been studied in detail, allowing us to use them to shed light on how the memory functions in practice.

Information Processing Theory

The traditional concept of memory saw it as a simple container that stored what the senses dumped into it for later use by the brain. With the advent of electronic data processing systems, the metaphors drawn from these have become the most popular ways to conceptualize memory. These metaphors are powerful and suggestive, but they can also be misleading, since the brain differs in many ways from a computer.

One of the main reasons for the use of data processing metaphors is that memory is a function that cannot be easily linked with specific parts of the brain. Thought is seen as information processing, and a key component of information processing is storage and retrieval. Information that is to be stored for the long term has to be encoded, processed to make it suitable for storage. The efficiency of this encoding can be enhanced by emotional arousal.The concept of encoding and decoding of memories suggests that they are not simply raw information but are constructed by the brain when recalled, and the construction may be influenced by the circumstances under which they were recalled.

Again reflecting the metaphor of an electronic computer, information processing theory saw memory as the interaction of several subsystems, each devoted to one specific task, that passed information one to the other as needed. The requirement for conscious attention by some processes means these systems have a limited capacity. The limited amount of memory affect learning and it caused the learning disabilities. The disabilities of grabbing on to memory is associated with autism and ADHD.

The Modal Model and Disability

The modal model (Figure 2), also known as the multi-store or Atkinson-Shiffrin model (from the researchers who first put it forward in 1968) is assumed by all varieties of information processing theory. It postulates different mental subsystems, each with a distinct function, that support and feed information to each other. The basically modal structure of the memory was supported by cases of brain damage that affected different parts of the memory unequally. Most versions of the modal model were divided into three major sections: sensory memory or sensory register, short-term memory, and long-term memory. As noted below, the concept of “short-term memory” is now obsolete. The unequal part of memory challenges students' ability to learn simultaneously, ability to grasp the knowledge.

Three-part Working Memory Model

Figure 3. The three- part working memory model.

It was obvious that something had to be carrying out the processes assigned to short-term memory. However, researchers gradually became frustrated with the concept’s inability to provide a model of how these processes took place. Thus, beginning in the 1970's, the “short-term memory” model was supported or replaced by a function labeled “working memory.” The “working memory” holds the information and images that the person in question is engaged with at the moment. Figure 3 presents the three-part working memory model.

There are many variations of this model, reflecting the uncertainty researchers have about how exactly it functions. However, it is generally agreed that the working memory is tightly linked with the long-term memory, since past knowledge has a very strong influence on conceptions in the present. It is also agreed that unlike the concept of short-term memory, which was thought to store information passively in an average of seven “slots” and transmit it unchanged, the working memory is active, not passive, making it central to the construction of meaning.

The most influential scheme for the working memory was put forward by Baddeley. This divided the working memory into three components: an executive control system, an articulatory loop, and a visuo-spatial sketch pad. This multi-component scheme is supported by a number of pieces of experimental evidence, such as the KF Case Study, where an accident severely impaired verbal processing while leaving visual processing almost intact. This strongly implies that verbal and visual processing are controlled by two different systems. It is also supported by the observation that visual and phonemic tasks can be carried out at the same time with relatively little impairment, showing that they do not depend on the same mental resources.

Central Executive

The central executive or executive control system has been compared to a director controlling the activities of two subordinates, the phonological loop and the visuo-spatial sketchpad. It oversees the functions of the working memory, selects information and strategies, and decides what the working memory will concentrate on. It coordinates performance on different tasks, decides among retrieval strategies, switches focus among different inputs, and interacts with the long-term memory to retrieve and work with information.

Despite its critical importance, little is known about the detailed working of the central executive. It has been criticized as “little more than a homunculus,” a humanoid “boss” that coordinates all the other functions of the system. Whether it carries out its various functions as a single coordinated system or a collection of independent subsystems is not clear.

Phonological loop

The phonological loop deals with spoken and written information. It is a passive short-term storage system for information that is received by reading or hearing. Information is stored in an articulation code, which means that written data must be converted before it can be retained. Aural data goes directly into the store.

The phonological loop is divided into two parts. The first is the phonological store or “inner ear,” governing speech perception, which can hold aural information (spoken words) for several seconds. The second is the articulation control process, or “inner voice,” which is in charge of producing speech, and which can rehearse and store input from the phonological store.

Visuo-spatial sketchpad

The visuo-spatial sketchpad or the “inner eye” deals with visual information and spatial concepts. It is a passive short-term storage system for visual and spatial information received through the eyes. It is responsible for situating a person in space, so that s/he can move through other objects without constantly colliding with them. Information is stored as images, which must be interpreted to retrieve specific details. It also creates and manipulates mental images, and turns material in the long-term memory back into usable information on spatial arrangement.

The visuo-spatial sketchpad appears to function even in individuals that have never enjoyed the power of sight, since such individuals have clear concepts of spatial distribution. This indicates that concepts of spatial distribution are independent of visual input. It has thus been suggested that the visuo-spatial sketchpad be split into two independent functions, one concerned with purely visual data, and another with spatial concepts.

Multimedia Learning

Developed by Richard Mayer, the multimedia learning derives from the concept that learning works effectively with the use of words and images. Multimedia learning draws upon three major assumptions: our working memory can only process a limited amount of received information at a given time; the way we process verbal and visual stimuli in working memory are independent of each other; information needs to be actively processed to make sense of the presented information .

Acquired from http://www.laval.k12.nf.ca/pub/?n=MUN6615.LearningEffects

Cognitive Load Theory

Cognitive load is a concept proposed by John Sweller who states that having a high amount of information at a given time, will exceed the capacity of the working memory , which composes of articulatory and acoustic components. A human’s working memory, is assumed to only have a limited capacity at a given moment, as it is continuously processing information. If the information received by the human brain exceeds the limit of what the working memory can temporarily hold, then it cannot be retained into storage. Because the working memory acts as a system for storing and processing new information, we face the challenge of transferring acquired information for long term memory, ultimately placing strain on learning, when there are exceeding amounts of incoming stimuli.

Dual-Coding Theory

Acquired from https://thinkypictures.files.wordpress.com/2015/08/tesskou_paivio_dualcoding1.jpg

Allan Paivio’s Dual-Coding theory separates audio and visual information, stating a human’s mind analyzes visual and verbal responses in separate independent codes . According to Mayer’s multimedia model, learning, primarily enters the human brain through words and images. In fact, visual imagery, when compared to verbal texts that require a person to generate a kind of imagery in one’s mind, provided a more reliable and retention in memory . Mayer’s research indicates that through the simultaneous use of images and words, learning becomes much more meaningful. In order to test this statement, many researchers conducted studies to find correlations for improved performance though the use of multimedia learning principles. A brief review of the research conducted by Billie Eilam and his colleagues will be examined as an example. Eilam conducted an experiment involving 150 college students, whereby participants were evenly divided into two groups. Each individuals received the same amount of cards required to perform a given homework. Group one received cards that were printed in texts, while the second group received information in both text and images, such as graphs. Results indicated that the latter group performed much more accurately compared to the first group . Experiments performed by Eilam and his colleagues, as well as other studies, were designed to determine and assess learning strategies as a means to improving student’s learning, in relation to how information is processed through the human’s memory system.

Active Processing

Active processing, is the last assumption that is based on the cognitive theory of multimedia learning. It states that the human mind processes information actively, in order to construct meaningful learning and retention of memories, through three main cognitive measurements: selection, organization, and integration . More specifically, humans are active learners because of their ability to process received input. How well people process incoming information however, depends on their ability to make sense of the materials they draw from and to make connections with information gathered, in order for meaningful learning to take place. This idea draws from Wittrock’s theory of generative learning, which states that humans make connections between prior knowledge and new incoming knowledge, leading to the creation of new understanding . It may be helpful then, to examine strategies or methods that help to foster active learning in people through paying attention, filtering, and organizing selected materials into coherent representations, thereby integrating it with previous and new information.

Information Process Model

acquired from http://www.slideshare.net/Snowfairy007/aqa-as-psychology-unit-1-memory

Cognitive psychology at its core carries the fundamental idea of information processing. More specifically, cognitive psychology compares how the human mind processes, much in the same way a computer processes. With the development of computers, the study of cognitive psychology adopted a concept behind computer simulations, which became a fundamental tool for understanding how cognitive processing in humans worked . The computer model is one that imitates the cognitive functions of a human mind. The similarities include receiving information from an exterior stimulus, organizing and encoding input in various ways, transferring data to storage systems, and retrieving of output when needed. Through the analogy of information processing approach, psychologists determined that human thoughts could only process a limited amount of information at a given time . Atkinson and Shiffrin (1968) proposed that human memories (like a computer) are formed through a series of channels. Atkinson and Shiffrin’s information processing model is divided into three central components that break down how human memory works: the sensory register, short-term memory, and long-term memory (which will be further examined in the later chapters below). Similar to a keyboard entering information onto a computer, the human mind initially receives information through what is called the sensory register, or in other words, sensory organs. Inputted information is then processed by the Central Processing Unit of a computer, equivalent to a human’s working or short-term memory. By then, information is either transferred for use, discarded or stored into long-term memory. For a computer, this stage of processing would take place on a hard disk in a computer . To begin with, the human mind transforms multiple forms of sensory information (e.g., visual and auditory stimulus) received from the environment.

Memory Structure

Memory structure is first introduced by Richard Atkinson and Richard Shiffrin in 1968. They created the modal model, which was also known as information processing model, to distinguish control processes and memory structures. Control processes are basically the specific processes that information stored, such as, encoding, retrieval processing. The human memory structure is consisted of three separate components, sensory memory, short-term memory and long term memory. Each component has a specific function, on the whole, memory structures allow us to process and move information around in our brain. One criticism that worthy to mention is that the modal model maybe not just a unidirectional flow, the actual information processing is more complex. Next, let's look at how sensory memory, working memory and long term memory interact and influence each other.

Sensory Memory

Sensory memory is a system that holds environment input in sensory registers so that perceptual analyses can work before that information fade away. Unfortunately, perceptual analyses take time and effort and the environment may change rapidly. The duration of holding information in our sensory memory is extremely short. In 1960, George Sperling first demonstrated the existence of sensory memory. In his experiment, participants were showed a slide of arrays of letters. The first study result illustrated that the length of time exposed to participants directly influenced their performance. Base on this result, he made two assumptions, first, subjects only saw limited amount of letter within the short period. Second, all the letters were registered, but lost. He then developed partial report method to test his assumptions . Participants only reported one of the rows letters after hearing a tone. If the tone appears immediately, participants recalled 3 of the 4 letters. The fewer letter were recalled with the delayed tone appeared. The result showed us that sensory memory storage and duration is very limited, although information were registered in our memory, they lost rapidly.

Working Memory

In The Magic Seven Study, George Miller argued that people can hold no more than 7 chunks in memory at one time. The only way for people to memorize more information is increasing the size of chunks and implementing information with meaning. It is interesting to mention that in Cowan's embedded processes theory, Cowan argued that "the magic seven" is not true, the real capacity of working memory is about four chunks, although each of the chunk may contain more than one item. Baddeley’s working memory model is consist of executive control system, articulatory loop and visual-spatial sketch pad. The executive control system has the similar role as brain in our body, it controls the other two systems and decides what kind of the information enters memory. Articulatory loop and visual-spatial sketch pad holds acoustic information and visual spatial information respectively.

Factors that influence working memory performance

Cognitive load theory is influenced and extended by Baddeley’s working memory model. It is worthy to mention that several factors may influence the working memory performance. Firstly, individuals have different background knowledge and capacity of working memory. If individuals are knowledgeable in certain domain, then they are more able to use the working memory efficiently. Secondly, the complexity of information is another constraint. Last but not least, the instructional approach is another factor, working memory performance is improvable if helpful and appropriate instruction is available. For example, learning to chunk information, or dividing the learning task. Furthermore, the amount of studies suggested that working memory maintenance is a critical step for long term encoding. As Baddeley once said, his attitude on this issue is that working memory activate many areas of the brain that include long term memory.

Long-Term Memory

Long term memory is different from working memory because it can maintain information for a long period of time. It could be days, weeks, months and years. Examples of long term memory include remembering the graduation day, or the experience of your first day at working. Theoretically, long term memory has unlimited capacity of storage, but people still lose memory due to unsuccessful long term encoding. Generally, long term memory is divided into 2 components: explicit memory and implicit memory. Explicit memory is known as memories that are available in our heads, the past events pop out in our mind sometimes.. It usually refers to the facts and declarative knowledge. The example would be that Vancouver is a city in Canada. While implicit memory is an unawareness memory that influence our actions and performance in daily life. This unconscious memory is about procedural knowledge, which is not just knowing about the facts, but knowing the process of performing the task. For instance, you are driving a car. Since we prior learned about the skill, we knew how to perform but we were not consciousness remembering it.

Cognitive development

physical development of brain

Human development had various aspects, physical development, personal development, social development and cognitive development. Development refers to certain changes that occur in different stages over the lifespan, here we are going to take a deep look of cognitive development. Cognitive development refers to our mental processes are gradually changing and becoming more and more advanced over the lifespan. People do not become mature once they reached a certain age, development takes time and happens gradually. Inside our brain, there are billions of neurons. Neurons are grey colour nerve cells that function in accumulating and transmitting information in the brain. These neuron cells are so tiny, they are about 30000 fit on the head of a pin. Each nerve cell includes dendrites and axon to make connections with the other nerve cells. A tiny gap, which called synapse, exist between each cell’s dendrite. Neurons transmit and share information by releasing chemical substances through these synapses. The numbers of neurons will be decreased if some neurons not serve as main function. Magically, if a child are deaf from birth, the auditory processing brain area will expect to process visual information rather than the auditory stimulation.

The cerebral cortex is the largest area of the brain which contains numbers of neurons, and it is covered under the outer. The cerebral cortex allows us to do the abstract thinking and complex problem solving. Every part of the cortex also has different function and different mature periods. The region of the cortex that control our physical movement usually matures first, then comes with our vision and auditory cortex. The Frontal lobe which takes charge of the high order abstract thinking processes always mature at last. Moreover, the temporal lobes which is responsible for the emotion development, language acquisitions and judgement will not completely mature until human body become physically mature. Although each part of the brain has its own function, they have to work collaboratively in order to complete complex functions, for example, Alice is reading a story. Her vision cortex is the first part to be stimulated and then sends the visual information to the other cortexes in her brain, finally, she is able to memorize and retell the story.

Cognitive Process

Cognition is a process of acquiring and understanding knowledge through people’s thoughts, experiences and senses. Memorization is a key cognitive process of brain at the metacognitive, as well as the cognitive process reveals how memory is created in long-term memory (LTM) . The logical model of the cognitive process of memorization can be described as shown in the diagram:

(1) Encoding process, which convert information to a form that can be stored in LTM; (2) Retention, this step stored the information in LTM; (3) Rehearsal test, this step checks if the memorization result in LTM needs to be rehearsed. (4) Retrieval process, which recalls the information from LTM; (5) Decoding process, this step is about information reconstruction; (6) Repetitive memory test, which tests if the memorization process was succeed or not by comparing the recovered concept with the original concept.

Encoding Process

Encoding allows information stored in the brain to be converted into a construction, which can be recall from long-term memory. Memory encoding process is like hitting “save” on a computer file, once file is saved, it can be retrieved as long as the hard drive is undamaged. The process of encoding begins with the identification, organization of any sensory information in order to understand it. Stimuli are perceived by the senses, and related signals travel to the thalamus of the human brain, where they are synthesized into one experience . There are four types of encoding: visual, acoustic, elaborative and semantic. Visual encoding is the processing of encoding images and visual sensory information. The creation of mental pictures is one example of how people use visual encoding. Acoustic encoding is that people use auditory stimuli or hearing to implant memories. Elaborative encoding uses information that is already known and connects them to the new information experienced. Semantic encoding involves the use of sensory input that has a specific meaning or be applied to a context. For instance, you might remember a particular phone number based on a person’s name or a particular food by its color.

Retrieval Process

Retrieval is a process of re-accessing of information previously stored in the brain in the past. In other words, it is the process of getting information out storage. When people are asked to retrieve something from memory, the information will be retrieved from short-term memory (STM) and long-term (LTM) memory. STM is stored and retrieved sequentially, while LTM is stored and retrieved by association. There are two types of memory retrieval: recall and recognition. In recall, the information must be retrieved from memories. In recognition, a familiar stimulation will provide a cue to let people feel that the information has been seen before. A cue might be an object, a word, a scene, or any stimulus that reminds a person of something related, and individuals recall the information in memory quickly according to the cue. Decision-making requires retrieval of memory, which contains two fundamental retrieval aspects during decision-making: automatic and controlled activation of memory representations. Take-the-best (TTB) is a strategy typically employed for decision from memory .TTB requires the sequential retrieval of attributes by the order of importance and stops information search as soon as a given attribute was allowed for making a decision. This sequential processing requires controlled retrieval from long-term memory, consequently, a repeated updating of working memory content . Manipulating automatic memory activation, which is the number of association with a retrieval cue, by varying the number of attributes to which a decision potion is associated .

Limitations of Memory

The limitation of memory means the brain’s storage capacity for memory is limited. This is similar to the space in an iPod or a USB flash drive. However, the capability of brain is difficult to calculate. First, people do not know how to measure the size of a memory. Like no one will know a 10 digits phone number will take how much space of people’s mind. Secondly, some memories involve more details and then take up more space; other memories are forgotten and that helps free up space. For instance, working memory refers to the temporary storage of information; it is also associated with conscious processing information within the focus of attention. Working memory and attention interact in a way that enables people to focus on relevant items and maintain current goals. However, working memory processing capacity and duration are severely limited when dealing with novel information. The importance of the learner organized knowledge base is primarily determined by its ability to effectively reduce the capacity limitation of working memory by encapsulating many elements of information into higher-level chunks that could be treated as single units in working memory . It shows the processing limitation of working memory significantly affect learning processes.

Metacognition

Metacognition can be defined as cognition about cognition, thinking about thinking. It refers to how people learn and processes information, and individuals’ knowledge of their own learning processes. There are two components of metacognition: metacognitive knowledge and metacognitive experience. Metacognitive knowledge refers to acquire knowledge about cognitive processes, knowledge that can be used to control cognitive process . While metacognitive experiences can refer to use of metacognitive strategy, which is the process of using cognitive activities to ensure a cognitive goal. Self-questioning is a common metacognitive strategy. For example, after students read an article, they will question themselves about the main ideas or concepts about the article. Their cognitive goal is to understand the article. Therefore, self-questioning is used to ensure that the cognitive goal of comprehension is met. Additionally, metacognitive strategy often occurs when cognitions fail, such as the recognition that students did not understand what they just read. Such an impasse is believed to activate metacognitive processes as the learner attempt to correct the situation.

Relationship between learning and memory

Compare to previous section, this section is about the relationship between memory and learning. There is an interaction between learning and memory, they depend on each other. Therefore, this section focus more on how memory processes interact with learning. Based on memory processes, people learn new information or knowledge and put them into their memory. Also, people recall their already known information from memory to relate with new information, to make new information meaningful, and in order to learn it effectively. Further more, based on knowing how memory works, this section also addresses the implementations of some strategies (such as chunking) on designing learning activities.

Interaction of Learning and Memory

First of all, defining of learning and memory would help us to understand their relationship better. Learning is the process of gaining new and relatively lasting information and behaviours. Memory refers to the process of recording and retrieving experiences and information.

Information Processing Model is a basis for the interaction of memory and learning. And the process of learning is quite similar to this model, people perceive new knowledge, identify and memorize it, and then encoding it into personal knowledge as encoding it into long-term memory . Also, the information processing model includes every components of how memory works. There are three main memory types in this model, which are sensory memory, short-term/working memory, and long-term memory. In sensory memory, information is stored shortly, also only 5-9 chunks can be hold for about 15-30 seconds in short-term memory. However, once the information transfers to long-term memory, it would be last yearly. There are two processes that happen between short-term/working memory and long-term memory, one is called encoding processes that refers to the process of moving information from short-term memory to long-term memory, and the other one is retrieval processes which is the process of information is delivered to working memory from long-term memory. Both of the processes play a significant role in learning.

Learning process is following the steps of information processing model, it also works as a mental process. To relate learning process with the information processing model, using learning how to drive a car as an example. First of all, a learner has to memory basic knowledge about driving, either road rules or names of car devices. The learner perceives knowledge of driving and car devices, then he encodes it into long-term memory. When the time the learner actually sits in a car and try to drive it, the basic knowledge of driving he encoded is retrieved into working memory to help him knows what he needs to do for driving a car. After he practices driving many times, he would turn the driving skill as a procedural knowledge which means knowing “how” into his long-term memory. As long as the learner’s driving skill gets more and more mature, the driving skill can be recalled unconsciously.

Memory limitations affecting Learning

Limited Attention in capacity

People require attention to learn. As mentioned in the previous section, human attention is limited in capacity. Hence, without attentions, people cannot learn effectively, which means learning without attentions is wasting time. For example, when a person is reviewing a history lecture while he is thinking what stuffs he needs to buy for holding a home party. For sure this person’s attention is allocated into two totally different fields, and he will not review the history lecture effectively because the limitation of attention in capacity. However, there are some strategies that can help people in general to deal with the limitations of attention, and they will be addressed lately in this section.

Forgetting Curve

Ebbinghaus identified the forgetting curve (Figure 1) idea in 1885. This curve addresses the regular pattern of people’s forgetting. The curve shows that we start to forget immediately and rapidly right after we learn, then the speed of forgetting slows down. To roughly talk about the bases of it, the curve shows that people can forget 50 percent of the knowledge’s content they just learned in an hour. Then, 8 hours, 24 hours, 6 days and 31 days are also the forgetting time points people generally have, and the percentage of the content people hold gets decreasing along with the forgetting time points. Consequently, people would totally forget the knowledge. Then, learning a knowledge is meaning less because it will be forgotten after all. Whereas, as long as we know the regular pattern and the certain time points of forgetting, we would have an appropriate strategy which will be addressed lately to deal with forgetting.

Implementations of teaching and learning

Chunking

As being mentioned previously, short-term memory can hold about 9 chunks for around 30 seconds, which limits information to be processing; also, attention is limited in capacity. In order to deal with these limitations, chunking is one of the best strategies. In 1956, Miller talked about people’s short-term memory is not sensitive to the chunks’ size, but the number of them. Chunks are defined as units of information that are related and partakes traits appears as a group.

As Collins and Quilian (1970) defined that the lowest level of the class of category's name conforms to the smaller categories, such as dog; and the highest level conforms to the larger categories, such as animal. Similar to the lowest level of the class of category, one view of chunking is to cut a big amount of information into couple of small groups. Taking memory numbers as an example. 5616289938, they may be meaning less to you. Let us put a dash line between them, 56-16-28-99-38, then we get five small groups of number instead of some random numbers. We can also think 56, 28,99 and 38 as ages, while 16 as a year. To make these number more meaningful, we can make a sentence like “my father is 56 year-old in 2016, I will be 28, and my grandmother is 99, my cousin is 38.” Now, these numbers are meaningful, and easy to remember and recall.

The other view is similar to the highest level of the class of category, which is to put and relate pieces of small information into couple of groups. For example, “concert”, “February”, “strawberry”, “Starbucks”, “mailbox”, “short-term”, “learning”, and “chunking”. To memory these words are not easy because they are meaningless to you; hence, it is hard to recall them after 30 seconds. However, by using chunking, we can put these words into two big groups, one is the words start with an "s", and the other one is the words start without an "s". Additionally, to make a relation between these words would help to memory them easier because they become meaningful, such as “I went to a concert in February. Before going, I had a strawberry frappuccino in Starbucks. When I went back home, there was a mail in my mailbox, it talked about how people using chunking to enhance their short-term memory and the quality of learning.”

Therefore, when students receive a big amount of new information or knowledge, they can cut them into groups, and make them relate to something is already known or meaningful. Consequently, students can learn effectively because the new knowledge is cut into appropriate units and put into a group with meaning. As an instructor, for example, instead of just giving random vocabulary, teachers can ask students to put vocabulary into different groups and make meanings for these groups. Additionally, asking them to use these vocabulary to make a logical sentence, in order to learn and memory them.

Managing Cognitive Demands

Studies done by Mayer and Moreno show additional ways in which learners can benefit by managing the demands on cognitive load during learning. Having distinguished 3 different types of cognitive demands, Mayer and Moreno suggest that student concentration on essential learning—the cognitive demands that are necessary for understanding the information— will benefit them more than concentrating on the demands of incidental processing and referential holding ^[17] . Referential holding is when one holds information in memory temporarily while other information is being processed (taking notes while listening to an instructor, for example), and causes attentional resources to become overtaxed. This study suggests that students focus more of their attention and resources towards essential learning, as spending more resources on referential holding and unnecessary incidental processing tends to lead to cognitive overload and overall poorer learning performance ^[18] .

Attentional Filtering

According to studies done by Bengson and Luck, attentional filtering is a high influence upon storage capacity in the visual working memory ^[19] . Similarly to Mayer and Moreno, this study suggests that students who filter out irrelevant information to make more storage room for the necessary information in the visual working memory perform better than students who do not ^[20] . A subsequent experiment was performed in which 3 groups of students were shown certain visual stimuli and were tested on how well they remembered them. The first group was asked to remember everything that they saw, the second group was asked to remember only specific subsets of stimuli, while the last group was simply told to “do their best.” Results showed that though the “do your best” and subset groups performed quite similarly, the group remembering everything had a much higher cognitive task to perform and were easily overwhelmed ^[21] . When applying the insights of this study towards instruction and learning, giving instructions that are specific and focus less on the whole and more on subset goal groups may be more beneficial towards students’ cognitive loads, keeping them from being overtaxed.

Reviewing of learned materials

After knowing the regular forgetting pattern, we come to find out doing review practices that follows along with the forgetting curve is an appropriate method to reduce forgetting. To extend this suggestion specifically, according to the forgetting curve, people start to forget immediately after they learn. Therefore, a quick reviewing can decrease the percentage of content we would forget. Thus, students better to review right after they learn the knowledge, for instance, reviewing the lecture content in an hour after the lecture. And before go to sleep, reviewing the content again. After around 24 hours, do the content review again, and try to come up some questions about it or do some practice assignments. Then, reviewing the content every week but not every day, in order to know it quite well and be available to retrieve it quickly when you need it.

Tests of learned knowledge

Recalling can help students to reduce forgetting. As an instructor, tests is a common strategy that asks students to recall the knowledge they have learned. Based on the forgetting curve, at certain time to give an either small test (such as quiz) or a big test (such as midterm) can effectively enhance recalling and reducing forgetting. For example, to give a quiz at the end of the lecture class, which helps students to quick review and restudy the lecture content. Also leaving a small practice assignment about the lecture taught today, and asking students to submit it the following day. After one week, to give another quiz about the lecture, which helps students to recall their knowledge of this content. After a month, to give a midterm which covers the lecture content to students, in order to test their understanding and recall their knowledge about this content.

According to the World Health Organization (WHO) it estimated 1 in every 160 children will be diagnosed with Autism Spectrum Disorder (ASD) and currently 39 million individuals are living with an Attention Deficit Hyperactivity Disorder (ADHD) diagnoses . Working Memory is a system used to implicate the process of encoding, decoding and maintenance of our memory (Figure 1)(specifically short-term memory) while , at the same time maintaining activity and accessibility . Research suggests developmental disabilities such as those as defined in the Diagnostic Statistics Manual of ASD and ADHD impact working memory. This chapter, within the framework of Baddely's working memory model attempts to understand the inner workings of these prevalent disorders.

Autism Spectrum Disorder (ASD)

Autism spectrum disorder (ASD) and autism are both general terms for a group of complex disorders of brain development and such classified as intellectual and developmental disability. These disorders are characterized, in varying degrees, by difficulties in social interaction, verbal and nonverbal communication, repetitive behaviors and difficulties in motor coordination and attention. Because of overlap and variability in symptoms, The DSM IV introduced the concept of autism spectrum disorder as opposed to a stand alone disorder.

Figure 4. Prevalence Rates and Incidence rates (U.S.)

While ASD occurs more often in boys than girls, early detection nonetheless is critical in diagnosis because proactive interventions have shown considerable improvements in areas such as language and social skills. Often this early detection is a result of statistically significant diminished capacities often referred to as impairments. Some early signs of impairment include: Communication (social), behaviors (verbal and non-verbal) and interests. While each pattern is unique, most common symptom is diminished capacity of language. DSM IV suggests three main types of ASD:

• Asperger's syndrome (AS)
• Pervasive developmental disorder, not otherwise specified (PDD-NOS)
• Autistic disorder (AD)

The DSM V while it made changes to ASD descriptions, further research should be considered when assessing the changes. Listed below are some of the common autism disorders.

Asperger's Syndrome (AS)

The mildest form of autism, Asperger's syndrome (AS), involves repeated interest, discussion on a specific topic. Children with AS often show great impairment in social skills and uncoordinated; however, above average intelligence has also been reported. High functioning Asperger syndrome (HFAS) if left unsupported can lead to depression and anxiety in later life.

Pervasive Developmental Disorder, Not Otherwise Specified (PDD-NOS)

Because of the generalized description, captures most children and is considered more severe than AS (but less severe as ASD). PDD-NOS symptoms include (but not exclusive) impaired language skills, social interaction and later age of onset. Difference of PDD-NOS from AS and Autism disorder (AD) include fewer repetitive behavior and variability of symptoms offers a challenge to diagnosis.

Autism Disorder

Children who meet more rigid criteria for a diagnosis of autism have autistic disorder. They have more severe impairments involving social and language functioning, as well as repetitive behaviors. Often, they also have mental retardation and seizures. Common symptoms while similar to AS and PDD-NOS also include absences of name recognition and use of single or two word phrases.

While ASD includes many subtypes and often the numbers can be underestimated because of variability, Figure 4 gives an overview of prevalence and incidence rates in the United States (1993-2003). This suggests ASD continues to be persuasive and increasing exponentially (compared to other disabilities). While ASD is the most common of the developmental disabilities, the second most prevalence learning disability is attention deficit hyperactivity disorder.

Autism Spectrum Disorder and Working Memory

Approximately seven percent of children suffer with literacy disorders such as Autism Spectrum Disorder (ASD) and ADHD Working memory is a fundamental function for the developmental process which is known to impact the neuro-cognitive domain with impairments Widely held beliefs on ASD and working memory suggest deficits in phonological loop processing, visuo- spatial challenges and inability to regulate executive functioning Controversial debate related to heterogeneity of ASD subjects and the various components of working memory function continue today. For example, a child with ASD may show attention to a specific object (e.g. zippers) while another child with similar diagnosis would not react to the same object (zipper). The second child may show interest in a bike instead. This suggests an impairment with the phonological loop. While ASD and working memory are complex, current research continues to focus on identifying specific impairments and its relationship to the different components of working memory when considering solutions in the instructional environment.

ASD and Central Executive

The central executive is the "most important component of working memory" because it is responsible for monitoring and coordinating the operation of the slave system (phonological loop, visuo-spatial sketch pad) and relates to long term memory

ASD's impairments in social interaction, verbal, non-verbal communication, and restrictive behaviors appear in early childhood and persist in later life. Hill & Frith (2004) (as cited by Cui et al.) suggest this is a result of executive dysfunction. Conflicting research suggests ASD dispute a relationship to central functioning because working memory may also be influenced by factors such as age, IQ, task measured which is often not accounted for in research literature. However, since Hill & Frith were able to use a battery of working memory tasks which aimed to isolate to Asperger syndrome in early-school-age children, (thereby removing the variables) were able to address these concerns and therefore it can be concluded there is a partial deficit in central executive.

ASD and Phonological Loop

The phonological loop is assumed to be responsible for the manipulation of speech based information It may be extremely difficult to study ASD and its relationship with the phonological loop because, as was mentioned, the heterogeneity of ASD subjects. Differences in each ASD individual with how they utilize the spoken and written language is unique; yet often when considering working memory and the phonological loop, non ASD individuals show similarities in learning. In spite of this variability, language impairments include decreased communication, phonology, semantics, and syntax. Fischbach et al (2013) conclude because of left-hemisphere brain deficits commonly found with ASD this may impact the ability of processing language. They add because of these deficits, compensatory effects in right hemisphere could lead to strengths in visuo-spatial processing (discussed below). While his compensation is important in that memory can adapt to brain disruptions, the challenge is that the left hemisphere does not advance functioning. It is important to note, as most research on ASD suggests, because of the changes in early development, phonological store is greatly impacted in reaction time among adolescents when studying speech in phonological short term memory (PSTM). Comparisons with typically developing (TD) subjects, the level of cognitive load during the phonological loop processing for ASD is significantly associated with reaction time and accuracy. This suggests perception of speech impacts access to speech. Controversy remains with this assertion when Williams et al (2014) while studying visuo-spatial memory argue no association with impairment of verbal storage and ASD.

ASD and Visuo-Spatial Sketch Pad

In working memory, the visuo-spatial sketch pad is assumed to be responsible for manipulating visual images. Prospective memory (PM) are highly prevalent in daily life and range from relatively simple tasks to extreme life-or-death situations. Examples include remembering to pick up milk at the grocery store after work or remembering to attach the safety harness when climbing buildings. This ability of the PM to remember to carry out a task (Williams et al, 2014) conclude that when considering time based tasks, ASD subjects because they show "diminished capacity have difficulty with processing visual storage", an important component of working memory and the visuo-spatial sketch pad (Sachse et al., 2013), when considering high functioning ASD (HFASD) such as Asperger syndrome while they did not find verbal memory impairment, conclude because visual motor information is impaired spatial working memory (SWM) "was impaired because of differences in cortical networks which led to higher number of working memory errors". Combining all aspects of working memory (central executive, phonological loop and visuo-spatial sketchpad), Because of the variability in ASD, researchers looked at various tasks specific to the working memory components with specific age populations (early school aged). Because of matched IQ, HFASD had significant disadvantages around visuo-spatial sketchpad implicated by partial deficits in central executive.

Unlike ASD and working memory implications, ADHD has very different etiology on working memory.

Attention deficit hyperactivity disorder (ADHD)

According to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition, DSM V, it states the diagnostic features of ADHD. People with ADHD would show a persistent phenomenon of Inattention and/or hyperactivity-impulsivity that affect development and/or normal functioning. (Reference table 1)

Inattention: 6 or more symptoms present for children who are below 16 years of age, or 5 or more symptoms must be presented for adolescents older than 17; these symptoms of inattention have been present for at least 6 months, and they are inappropriate for developmental level:

Hyperactivity and Impulsivity: 6 or more symptoms present for children who are below 16 years of age, or 5 or more symptoms must be presented for adolescents older than 17; these symptoms of hyperactivity-impulsivity have been present for at least 6 months to an extent that is disruptive and inappropriate for the person’s developmental level:

• Often fails to give close attention to details or makes careless mistakes in schoolwork, at work, or with other activities. • Often has trouble holding attention on tasks or play activities. • Often does not seem to listen when spoken to directly. • Often does not follow through on instructions and fails to finish schoolwork, chores, or duties in the workplace . • Often has trouble organizing tasks and activities. • Often avoids, dislikes, or is reluctant to do tasks that require mental effort over a long period of time. • Often loses things necessary for tasks and activities . • Is often easily distracted • Is often forgetful in daily activities.

• Often fidgets with or taps hands or feet, or squirms in seat. • Often leaves seat in situations when remaining seated is expected. • Often runs about or climbs in situations where it is not appropriate (adolescents or adults may be limited to feeling restless). • Often unable to play or take part in leisure activities quietly. • Is often "on the go" acting as if "driven by a motor". • Often talks excessively. • Often blurts out an answer before a question has been completed. • Often has trouble waiting his/her turn. • Often interrupts or intrudes on others

In addition, the following conditions must be met:

• Several inattentive or hyperactive-impulsive symptoms were present before age 12 years.

• Several symptoms are present in two or more setting, (such as at home, school or work; with friends or relatives; in other activities).

• There is clear evidence that the symptoms interfere with, or reduce the quality of, social, school, or work functioning.

• The symptoms are not better explained by another mental disorder (such as a mood disorder, anxiety disorder, dissociative disorder, or a personality disorder). The symptoms do not happen only during the course of schizophrenia or another psychotic disorder.

Sub-types of ADHD

There are three sub-types of ADHD that categorized by the different categorize of ADHD.

• Predominantly Hyperactive-Impulsive Type: in order to fulfill this sub-type, in the past six weeks, the person has filled the entire requirement for symptoms of Hyperactivity-impulsivity, but not the symptoms of inattention

• Predominantly Inattentive Type: In this sub-type, the person has filled the entire requirement for symptoms of inattention, but not the symptoms of Hyperactivity-impulsivity.

• Combination Type: In this sub-type, the person has filled both requirement for the symptoms of Hyperactivity-impulsivity and inattention. This is the most common type of ADHD.

With these definitions of ADHD and ASD in mind (including symptoms), it is important to consider its relationship with working memory.

Attention Deficit Hyperactivity Disorder and Working Memory

Figure 5.The above brainscan of brains shows the differences between adult with and without (Left) ADHD

People with ADHD usually accompany with some difficulties on their working memory, when we focus on the brain structure of the ADHD children, we could see that their brain structures are usually differ from children without ADHD, Several brain regions and structures, such as pre-frontal cortex, striatum, basal ganglia, and cerebellum tend to be smaller than people without ADHD. The overall brain size from ADHD children is generally 5% smaller than children without ADHD (Figure 5).These brain regions are closely related to how our working memory works, especially the pre-frontal cortex, thus with a smaller brain size, ADHD children’s working memory would perform poorly.

ADHD and Central Executive

The central executive seems equally impaired in both subtypes. A research used the Chessboard Task to test whether the subjects could maintain and reorganize visuospatial information, thus the Central Executive has been tested in this research. The result shown that ADHD children score lower than the normal students, nevertheless, the result of ADHD children improved when they received high level of reinforcement but not the control group .

In another research, the researchers used The Digits Backward, to test their capacity to store and manipulate information, and The Dual Task, to test their ability to coordinate two separate tasks. The result shown that ADHD children repeated fewer digits than the controls in The Digits Backward task and gain lower score in The Dual Task, these tasks show that central executive functions are critical for the variance in goal-setting skills in children with ADHD .

ADHD and Phonological Loop

ADHD children performed similarly in the Phonological loop tests with normal children, their score in The Digits Forward and The Word Recall tasks are similar. These tasks tested whether subjects could repeat the digits in a correct order. This result is consistent with the results of several earlier studies showing that deficits in the phonological loop are not characteristic of children with ADHD .

There is a research accompanied the ADHD children with Specific language impairment, also suggested that ADHD children have less impact in phonological loop. ADHD-C children with SLI scored significantly lower than those without SLI and normal children. Which support the hypothesis that Phonological loop are not the characteristic of ADHD children .

ADHD and Visuo-spatial Sketchpad

ADHD-I children and ADHD-C children who have motivational deficits, they have a destructive effect on their visuo-spatial working memory performance, according to The Chessboard Task, their score are lower than the control group . In Visuo-Spatial Test, it measures the ability to remember the number filled matrix, the result shown that children with ADHD performed more poorly than the control group . Nevertheless, High reinforcement can improve the working memory performance in both ADHD groups, but not the control group .

There are some minor differences between different subtypes ADHD. In the task of the Hopkins Verbal developmental Test–Revised (HVLT-R), The official Norwegian research versions, and the Brief Visuospatial Memory Test-Revised (BVMT-R), these tasks measure the performance of Auditory or verbal and visuospatial ability. The results shown that there are more impairment about developmental and delayed memory in the ADHD-I children when we compared the result with the ADHD-C children .

ADHD and ASD Developmental Implication

There are several behavioral strategies and treatments could help the ADHD patients, in order to improve their behaviors. For example a good and effective Classroom management could change the behavior of ADHD students,a more structured classroom, provide closer attention to students, and limitations of distractions could help to change the behavior of ADHD, these modifications may not have an effective assessment, but they usually included in the treatment plans. Some behavior therapies can be implemented to teachers and parents through some training programs, like Parent Management Training, Operant-conditioning usually involved in these programs, a positive reinforcement (consistent rewards for achieving goals and idea behavior) and positive punishment ( provide a negative consequence after the present of an undesired behavior). Teachers learn classroom Management as a technique to change behavior, Token economy ( student earns rewards when performing desired behaviors and loses the rewards when performing undesired behaviors), daily feedback and structured classroom activities

However,a research in 2013 shown that working memory training like the Cognitive training could only provide a short term improvements, and there are only little evidence that those improvements are permanent. Also in 2014, researchers analyzed that the current evidence for the accuracy of cognitive training for treatment of ADHD symptoms is not completed.

Conclusion

The purpose of this chapter was to provide insight on appropriate and effective implementations of learning, through the understanding of the mechanics of memory. This chapter begins with an introduction to multimedia learning and provides an idea as to how learning is more effective through the use of words and images. It presents the topics of multimedia learning, which includes theories of cognitive load, dual-coding, and active processing. The next key topic discusses the information processing model, which explores the process of human memory, usually referred to as the memorization of information. Three main memory structures are said to be sensory memory, short-term/working memory and long-term memory. Each structure has specific nerves required in order to function properly. This processing model also provides a foundation for the learning process. Moving on, the idea behind cognitive process focuses more on encoding process and retrieval process which occurs amidst short-term memory and long-term memory. By understanding how these two processes work, we can then discern how to make information meaningful, and how to access information when required. Furthermore, by examining the systems of short-term memory and long-term memory, it provides us with an idea about how we acquire knowledge. Forgetting curve and limited attention capacity tells people the challenges of learning. By recognizing the challenges faced in learning, use of strategies such as chunking, reviewing, and tests, as well as teaching strategies (mentioned in this chapter) are ways that can help people deal with these challenges. Teachers can apply these strategies on students in order to help them learn to be more efficient and effective, or students can use these implementations on their own. By the end of this chapter, the hope is to foster a better understanding and knowledge about memory and the underlying processes behind it, while providing insight on the appropriate implementation of learning.

Glossary

Active processing: refers to the idea that meaningful learning takes place only when humans actively organize, integrate and build connections with prior and new knowledge.

Acoustic: relating to sound or the sense of hearing.

Attention: the capacity of focusing on a stimulus.

Articulatory loop:holds acoustic information

Chunks: defined as units of information that are related and partakes traits appears as a group

Cognitive load: total amount of load that can be placed on the working memory

Cognitive development:a gradual changes in our mental processes of becoming more and more advanced over time.

Decoding: convert a code message into intelligible language.

Dual-Coding theory: a theory proposed by Allan Paivio that suggests that the human memory detects visual and verbal responses as separate systems.

Ebbinghaus’ forgetting curve: a curve presents memory is decreased as time goes by.

Elaborative: worked out with great care and nicety of detail.

Encoding: conver information or an instruction into a particular form.

Executive control system:controls the other two systems and decides what kind of the information enters memory.

Information processing model: theory proposed by Atkinson and Shiffrin which compares sequence of computer processing to that of humans.

Learning: active process of acquiring new information

Learning process: the journey of learning, works as a mental process

Long term memory: It can maintain information for a long period of time. It could be days, weeks, months and years.

Memorization: a process of committing something to memory.

Memory: the process of recording and retrieving experiences and information

Metacognition: awareness and understanding of one's thought processes.

Multimedia learning: a type of learning model based on the belief that materials presented through images and words improve understanding, than in words or pictures alone.

Recalling: to retrieve the information from long-term memory.

Retention:the continued possession, use , or control of something.

Retrieval: a process of getting something back from somewhere.

Reviewing: to relook at and rememory the knowledge that has been learned.

Self-questioning: examination of one's own actions and motives.

Semantic: realting to meaning in language or logic.

Sensory memory is a system that holds environment input in sensory registers so that perceptual analyses can work before that information fade away.

Two views of chunking： One view is to cut a big amount of information into couple of small groups. The other view is to put and relate pieces of small information into couple of groups

Visual-spatial sketch pad: holds visual spatial information

Aural data – Data that is relating to or perceived by the ear.

Intellectual disability- A disability characterized by significant limitations in both intellectual functioning and in adaptive behavior, which covers many everyday social and practical skills. This disability originates before the age of 18.

Developmental disability- A diverse group of chronic conditions that are due to mental or physical impairments.

Impaired language skills- A language disorder that delays the mastery of language skills in children who have no hearing loss or other developmental delays.

Variability- How spread out or closely clustered a set of data is.

Impairments- In health, any loss or abnormality of physiological, psychological, or anatomical structure or function, whether permanent or temporary.

Mental retardation- A condition diagnosed before age 18, usually in infancy or prior to birth, that includes below-average general intellectual function, and a lack of the skills necessary for daily living. When onset occurs at age 18 or after, it is called dementia, which can coexist with an MR diagnosis.

Psychotic disorder- Severe mental disorders that cause abnormal thinking and perceptions.

Executive dysfunction- A disruption to the efficacy of the executive functions, which is a group of cognitive processes that regulate, control, and manage other cognitive processes.

Cognitive load- the total amount of mental effort being used in the working memory.

Diagnostic Statistical Manual (DSM)- The standard classification of mental disorders used by mental health professionals in the United States. It is intended to be used in all clinical settings by clinicians of different theoretical orientations

Heterogeneity- A word that signifies diversity.

Pre-frontal cortex- The cerebral cortex which covers the front part of the frontal lobe.

Striatum- Also known as the neostriatum or striate nucleus, is a subcortical part of the forebrain and a critical component of the reward system.

Basal ganglia- A group of structures linked to the thalamus in the base of the brain and involved in coordination of movement.

Cerebellum- The part of the brain at the back of the skull in vertebrates. Its function is to coordinate and regulate muscular activity.

Frontal cortex- Cortex of the frontal lobe of the cerebral hemisphere

Motivational deficits- Motivation is defined as the product of expectancies and values.

Statistically significant- The likelihood that a result or relationship is caused by something other than mere random chance.

Executive functioning- A set of mental skills that help you get things done. These skills are controlled by an area of the brain called the frontal lobe.

Cortical-Consisting of cortex,the outer layer of the cerebrum.

Suggested Readings

Burt, B., & Gennaro, P. (2010). Behavior solutions for the inclusive classroom: a handy reference guide that explains behaviors associated with Autism, Asperger's ADHD, sensory processing and other special needs. Canada: The Donahue Group. • Eysenck, M. W., & Keane, M. T. (2001). Cognitive psychology (4th ed.). New York: Psychology Press.

• Mccabe, J. (2010). Metacognitive awareness of learning strategies in undergraduates. Mem Cogn Memory & Cognition, 39(3), 462-476.

• Miller, M. D. (2011). What College Teachers Should Know About Memory: A Perspective From Cognitive Psychology. College Teaching, 59(3), 117-122.

Long-Term Memory

When a student studies for tests and memorizes class material, where does the information go? Long-term memory remains absolutely necessary and important in learning, as all information that a student learns is remembered, or stored in either short- or long-term memory. While both short-term memory and long-term memory remain important for storage purposes, they can also influence people's learning, how they perceive things, and how they build up the meaning in what they perceive. Learning and memory constantly influence one another, as one's memories or prior knowledge of certain concepts, subjects, or items can enhance learning. In this chapter, we will describe the components, functions, and framework of long-term memory based largely on the widely accepted information processing model. We will also link this framework to cognition, exploring the many ways in which information reaches long-term memory and is stored and retrieved. Lastly, we will discuss newer and established models which describe other views on long-term memory.

Overall structure and functions of long-term memory

Long-term memory has the supposedly limitless and permanent capacity for all sorts of information that one experiences within a whole lifetime. Having long-term memory is necessary for all learners. Understanding how it works, its makeup, and processes within it can help learners to better understand their own learning.

Our long-term memory contains vast amounts of information comprised over long periods of time, and unlike short-term memory (discussed in a different chapter), does not require constant repetition to make it last. Information stored in the LTM is recalled or reconstructed, rather than rehearsed or repeated. Importantly, LTM is often broken down into categories of knowledge which include declarative knowledge, procedural knowledge, and conditional knowledge.

Declarative knowledge or memory (also sometimes referred to as semantic knowledge) refers to knowledge that we typically can explicitly articulate, whereas procedural knowledge usually refers to implicit skills and processes that we have no little or no trouble performing but find it difficult to express explicitly (the later sections on production rules and the ACT-R theory explain these in more detail). Conditional knowledge means knowing in what kinds of conditions or situations to deploy declarative and procedural knowledge. The table beolw shows some concrete examples of each. 

Declarative knowledge	Procedural knowledge	Conditional Knowledge
Mobile phone is a portable telephone.	How to make phone calls.	When to pick up the phone and hang up.
Cars usually have four wheels.	How to drive a car.	When to lock the seatbelt and unlock it.

Table 1. Examples of declarative knowledge, procedural knowledge, and conditional knowledge

Building blocks of cognition

The “building blocks of cognition” are five mental constructs hypothesized by many theorists that work together to form the foundation of all of the mental frameworks and information that is stored in the long-term memory. Essentially, they are the components of LTM. Although many of these components may share similar features, each is slightly different than the next. The first three concepts that we will examine are linked closely to declarative knowledge, and the last two are usually considered parts of procedural knowledge.

Concepts

What are Concepts? Concepts are theorized to be ways in which we break down and categorize mental structures into relatively elemental chunks and groupings with meaning that then can be used to make sense of any new incoming information. They are deemed to be “conceptually coherent chunks of knowledge” that can be triggered and called upon when one is prompted to retrieve information, and they are usually categorized as declarative knowledge. For example, when talking about a concept of cats, you might refer to a category of animals that share similarities with one another: they are all small and furry; they use “meowing” to communicate. Cats may have different hair colors: white, black, brown, and so on; they may be domestic cats or feral cats. However, they all belong to the category of cats.

Concepts that are based off highly common/prominent events are called prototypes. For instance, the best representative or the prototype of a basketball league of North America might be the National Basketball Association. It is believed that concepts, along with the other four components of the “building blocks of cognition”, work together to formulate the foundations of what we know to be long-term memory, supporting the acquisition and development of language functions, factual knowledge, and object recognition--many of the very core aspects of long-term memory.

What are Concepts composed of? There are two main theories that are considered with regards to how conceptual development occurs. First, some theorists believe that concepts are abstract, mental structures in the brain, which are formed separately from the sensory-motor systems from which the information in these structures is received. In contrast, the other main theory, which has been supported by neuroimaging technologies (such as fMRI), is that concepts are formulated in accordance with the sensory-motor component and that they are stored within long-term memory as multi-modal structures.

There are three widely agreed upon categories of which we sort our conceptual information into; matter, processes and mental states. The idea of processes means that we store mental information pertaining to a series of interrelated events that occur of which we would expect to see a particular result. An example of processes could be dropping something from any height--the forces of gravity will not allow for it to be suspended in space and will act upon it to bring it back down to the earth. Mental states refers to a category essentially designated for internal states and emotions, such as recognizing when you feel upset, happy, or unsure about something.

How do we formulate Concepts? There are three established ways pertaining to how we develop and formulate our concepts. First is the conservative focusing strategy proposed by Bruner, Goodnow, and Austin. Individuals who use this strategy are able to select appropriate stimuli according to the relevant attributes surrounding the concept of which they are confronted with. Others favour the focus gambling strategy, where it is believed we gain all of the knowledge we need about a stimulus at a single period of time, all at once. Individuals who choose to follow this strategy will, in fact, take less time to attribute a stimulus than those who chose conservative focusing strategy. However, they will be more likely to make mistakes as they are making their attributions out of speed, not thoroughness. The final possible strategy one can utilize is called scanning strategies, where individuals will attempt to put multiple hypotheses to the test at one given time. Although this is also a time-efficient strategy for attributing stimuli, the testing of these multiple hypotheses is ultimately a greater cognitive demand than testing one at a time, and thus can detrimentally impact an individual’s abilities to process and remember information.

Propositions

Propositions are the mental concepts in which most theorists widely believe that we store linguistic information and the majority of our declarative knowledge. Propositions are known to be the absolute shortest statement to which meaning can be attached, yet are inherently more complex than concepts as they build upon the preexisting concepts in order to form meaningful statements and assertions how these particular concepts are related. In order to be a proposition, the statement made must be able to be judged to be either true or false (in other words, a declarative statement of knowledge). Here is an example of a sentence that contains two propositions: “Luke bought the expired ticket.”

Figure 1. An example of propositional network

1- Luke bought the ticket. (The event happened in the past.)

2- The ticket had expired.

It is believed that propositions sharing common characteristics or qualities are linked together within propositional networks, which can be activated through the encoding or retrieval of information related to a specific proposition. If we apply the same propositional network in a new sentence: “Luke bought the ticket which had expired”, we will find that the two sentences have the same meaning. An image representing this idea can be found on the right.

Schemata

What are schemata? Schemata are believed to be mental representations of an individual’s general cause and effect knowledge. Any and all knowledge that we gain is organized in the schema, which is responsible for the subsequent encoding, storage and retrieval of information. Schemata are formed through the interaction of the external conditions and the individual’s own prior knowledge. The image on the right can be a representation of schemata for knowledge about mammals.

Figure 2. Schemata: Knowledge about mammals

The schemata have been compared to the mental equivalent of scaffolding. In other words, the schemata that we form will provide supports for us when we find ourselves in novel situations or learning new information.

How are schemata formed? Possessing pre-existing schematic knowledge on a certain topic has been linked to improved memory on retaining new information when attempting to recall newly encoded information. This is believed to occur as it allows for new information to be more rapidly assimilated into the brain (and thus into the activated schema). The information that is encoded in our schema is sorted into what are known as slots; specific mental “categories” of sorts, into which our knowledge is encoded, stored, retrieved and ultimately how it is perceived overall. When a schema has developed and has been proven to be a common occurrence of events or concepts, it will then likely become a part of our long-term memory where it will continue to serve as the foundation for our recollections and any future schematic information that may be encoded. This process is termed schematic instantiation.

Productions

Productions are “if-then” statements that serve as a set of action rules, which govern all of our procedural knowledge. Here is an example of if-then productions: “If the traffic light turns from green to yellow, then slow down”. The productions are instantaneous, automatic mental concepts that are learned to be second nature to humans after repetitive exposure to a common sequence of events. They provide a set of production rules and expectations for these events, and, like propositions, are organized in interactive groups known as production networks. Often by activating one production, other productions will be triggered, reacting in a series of cognitive processes and actions until the ultimate goal is accomplished. A later section offers a more detailed discussion of production and production rules as a theory of memory.

Scripts

Figure 3. A child's script for a hotel check-in

Scripts are the mental concepts that work as the underlying framework for all our procedural knowledge. It is commonly agreed that scripts are vital to our social understanding of the world around us, and largely work to provide information governing social situations and events, specifically who does what, when do they do it, to whom they do it and why. People use scripts in many kinds of events such as checking into hotels. Scripts develop over time and with continuous exposure to recurring events that are all essentially similar in nature. For instance, you might develop your own script of how to check into a hotel over time, and it can help you to organize, remember things, as well as react to the possible upcoming events in the situation. The figure to the right is a child’s script for a hotel check-in.

Implications of these building blocks for instruction

It is incredibly important for all educators (currently employed and future alike) to ensure that they are knowledgeable about each individual component of the building blocks of cognition, and how all of these mental concepts work together to facilitate learning, acclimation of knowledge and development, in addition to retrieval and the retrieval processes. By doing so, they can ensure that all of their students are fully utilizing these mental processes (such as by teaching “review lessons” prior to the new curriculum in order to activate previous productions, schemata propositions to facilitate the encoding of the new information, as well as prepping for an easier retrieval later on) in order to reap all of the benefits out of their education. By obtaining knowledge about the inner workings of these mental processes, educators will be able to better understand how learning occurs and how best to assist their students while encoding novel stimuli and information.

Encoding: How information reaches long term memory and how it is stored and retrieved

This section is a brief discussion of aspects of encoding that pertain to long-term memory. For a detailed discussion on encoding, please see the next chapter.

Information reaching long-term memory: The modal model

Figure 4. A depiction of modal model

The modal model is one of the most widely accepted models that describe how information is perceived from the environment and travels through a series of cognitive functions before it reaches the LTM. It is a general depiction that recent research has put together of the sequence in which information is transferred from our senses to the short-term memory, ending with long-term memory. Based on this model, information is assumed to be processed through each of the three “lower” memory systems, each its own separate function. This model provides a significant distinction between each of the different memory functions, and the processes between each (more details of this model are discussed in a later section outlining different theories of memory).

Storing information

Encoding is the process of transferring information from the working memory into the long-term memory, and is highly important due to its significance towards how well something is remembered. Below are some of the different encoding and processing methods that are well-known and well-used.

Rehearsal

Referring back to the modal model, rehearsal is the process in which information is kept in the short-term memory, usually through constant repetition. Maintenance rehearsal usually employs the process of constant repetition and recycling information (also known as rote memorization), but it is considered a more shallow method of encoding as the information is usually kept active for only a short amount of time, and decays quite rapidly once repetition is ceased. Elaborative rehearsal is a more meaningful mode of encoding, in which to-be-learned information is given meaning by being related to previously learned information. Though this form of rehearsal uses more cognitive resources, it is better for long-term retention and makes use of deeper encoding activities.

Elaboration

Several elaborative encoding strategies exist, all which make new information easier to process or remember. One well-known and most-used elaborative encoding strategy is the mnemonic, a process which engages more sophisticated coding by pairing together new information with well-known information. This strategy typically makes use of rhymes, hand gestures, acronyms, and many others. For example, you could use the acronym “SEG” to remember your shopping list of steak, eggs and garlic. Other strategies include mediation, a simple strategy of connecting a new piece of information to something more meaningful, and imagery, which involves tying together a corresponding image to something to be remembered. You can also use your imagination of these things that relates to a familiar place such as your house. Imagine a strong smell of garlic when opening your living room door, a box of cracked eggs next to the door, and a piece of juicy steak on the dining table. Using this strategy, you can remember the items by taking an imaginary walk from your living room to your dining room.

Associated theories

Levels of processing theory

Influential constructivist views, especially theories from Craik and Lockhart, remain significant to this day. Their levels of processing theory is most reputable. According to this theory, students benefit most from performing cognitive analyses on the to-be-learned information—memory of the information is retained naturally after these processes. However, the retention of the information is highly based on the methods in which it was processed. According to theory, the more deeply the information is processed and the more meaning is given to the information, the better it is retained, while shallower processing of more superficial details tends to make the information forgotten much faster. It is theorized and widely proven that participation in more meaningful, rather than mundane tasks, helps students to better remember the information learned. Providing students agency and choice are also beneficial towards retention, as studies done by Jacoby and many others show how having students make decisions (especially difficult ones) recall more of the task than if they made simpler decisions, or none at all.

Dual-Coding Theory

This theory, proposed by Allan Paivio, argues that knowledge is held in long-term memory either visually or verbally, or both. This is supported by some scholars and psychologists, who agree that when information is processed and stored both in image as well as verbal forms it is mostly easily remembered. For an educational implication based on this theory, it may be helpful to teach students by offering, for instance, a graphic display of a human brain along with textual information when learning about the brain’s features and regions. This theory shares some foundation with Richard Mayer’s Cognitive Theory of Multimedia Learning, which will be discussed next.

Cognitive theory of multimedia learning

Richard Mayer has been exploring combinations of images and words, finding that appropriate ones can deliver the most effective instruction, especially for older students. This theory is based on three tenets: a) ideas from the Dual-Coding Theory; b) the notion that the working memory has very limited capacity for storing imagery and verbal information, meaning instructions should be presented in a way that optimizes the amount of cognitive load put on students’ working memory systems, which is also referred to as the Cognitive Load Theory; and c) the notion that learning entails organizing and integrating information.

Information retrieval

Spread of activation

Since there can be a vast amount of information stored in long-term memory. Retrieving or recalling the right piece of information at the right moment may be difficult at times. It happens through a process known as spreading activations, which means that when one piece of knowledge is currently on our mind, other related pieces of information can be activated as well, through the interconnected network of information in our long-term memory. For example, if Ben is thinking, “how wonderful it would be if it stopped raining right now”, this might then trigger the thought of needing to check the weather forecast to see if rain would affect his field trip a week later, which could then remind him of contacting his travel mates to pick him up on that day.

Reconstruction

Because certain pieces of memory, such as events that happened a long time ago, may be difficult to recall, our cognitive system might use any relevant clues we can remember and reconstruct these pieces of memory through logic, which might produce memories that are not identical to the exact occurrences but are logical and reasonable. For instance, if we went for a picnic near a lake with friends 10 years ago, we might be able to recall the trip but not remember the purpose of it, and we might say that it was a hiking trip around the lake instead, which shares some similarities as the original event but is not identical.

Forgetting

If information is not accessed for a long time, we may eventually no longer be able to retrieve it. This could happen through either decay or interference, which mean weakening of the information signal and having other conflicting information interfere with the piece of memory that we are trying to recall, respectively. For example, we might no longer remember what T-shirt we wore at a concert because it has been a few years since then, or if we think it was a blue one but a friend recently mentioned that it was in fact green. One neurological explanation of this is that our brain cells and the connections between them can become weak and even die if we do not use them enough.

Despite the processes of decay and interference, knowledge can be stored in the long-term memory for extremely long periods of time, especially with appropriate kinds of prompts and other ways of remembering information. These include techniques mentioned earlier, such as using mnemonics and elaboration.

Expertise and automaticity of skills

Explicit or declarative knowledge can be acquired and built through many processes such as instruction, experience, and adopting cognitive strategies to remember information (mentioned earlier).

Some scholars have argued that declarative knowledge can be transformed into procedural knowledge as one becomes more skillful at a task with practice and experience, essentially meaning that the deployment of explicit knowledge becomes so automatic that it turns into an implicit skill. For example, when we try to wrap a gift for the first time, we might try to articulate each step of the process, such as: find a piece of wrapping paper of the right size for the gift; wrap it around the gift; cut the excess paper; use tape to secure the wrapping. These steps become automatic as we perform the task over and over, essentially eliminating the need to give extensive consideration to each step individually. More detailed examples of this can be found in the later section on the ACT-R theory.

Long-term memory and learning: Fostering higher encoding processes

Higher encoding processes are typically activated when one encodes more complex information, and higher encoding processes usually help more towards higher educational/learner goals. Instructors should try to foster such processes. As shown earlier, students tend to perform much better the more elaborately they encode the to-be-learned information. Through methods such as activating prior knowledge and guided peer questioning, instructors can activate relevant schemata in students and provide opportunities for comprehension and asking thought-provoking questions. Activating prior knowledge helps to prepare learners for new learning activities: a base of already-known information can help to guide the new to-be-learned information. For retention, instructors can encourage students to practice certain tasks until they gain automaticity. As much as possible, instructors should involve students more in their learning to encourage active, rather than passive learning.

The functions of long-term memory: Assessment and research

Memories gathered over a longer period of time have a greater chance of being retained long-term, but the quality of the memory is just as important as quantity. Quality can refer to sensory information being gathered by the individual during the experience, like smelling popcorn at the movie theater, and can have a bidirectional relationship between quality components, like smelling popcorn and thinking of the movies or being at the movies and remembering the taste of popcorn.

The majority of research done in this field focuses on self-evaluation or individual memory testing, both of which have fair parameters of error, though functional magnetic resonance imaging devices have been used to noninvasively view the activity of an individual’s brain. An experiment was done using this technique by Anderson, Fincham, Qin, & Stocco to find the link between procedural execution, goal setting, controlled retrieval from declarative memory and image representation construct, and the brain’s cortical regions. The findings of this experiment showed that each of these four areas lit up a different cortical region on the imaging device. This evidence seems to show that different areas of the brain handle these different areas, but critiques on the technique highlight that we still do not know why this activity occurs and what connections are being formed in the mind to cause the array of activity. Despite limitations, experiments of this variety do give us greater insight into our brain activity than we previously had, and show just how different information can stimulate different areas of the brain, so we know that they are not all active all the time.

Other changing and growing theories of memory

Network models

Figure 5. An example of a network model

Network Models could be compared to mind mapping or a brain-storming web as information is represented by a web-like pattern, generally moving from the general to more specific information or categories. This would be similar to the way in which a small child slowly develops the ability to differentiate between different animals that have four legs and fur, learning that a dog and cat have different classifications. Networking models are one of the more simple ways to organize small units of information when they related within the topic to other pieces. This model has been used directly in teaching--“Mind mapping directed the students’ attention to plan, monitor, and evaluate their learning processes, which helped them to obtain metacognitive knowledge and transfer their understanding to solve novel problems and situations.”

The Connectionist Model

Figure 6. A general model of what a Connectionist Model might look like

The Connectionist Model is a ‘brain metaphor’ taking on the traditional computer metaphor used for information processing, storage, and retrieval model; it is also referred to as the parallel distributed processing model. This model includes the concept of understanding based on context; an example of this would be having a shape with a straight line on the left, with a ‘3’ shape on the right. In the series ‘12 |3 14’ this would be seen as the number thirteen, but in the sequence ‘A |3 C’ it can be read as the letter ‘B’. It is because of the adaptability to context and ability to combined cognitive tasks with a physical attribution that the connectionist model was developed to better encompass these dynamics. This theory looks at the human thought processes from a multitude of parallels as the human brain is able to consider multiple thought directions in a time and in a way that a computer wouldn’t think to compare or connect. As mentioned previously, other models have a store-retrieval aspect of recovering information where the pattern of information connections is stored and recovered when needed. Alternatively, the Connectionist Model theorizes that the elements of the pattern or connections are stored as the strengths of their connections, to be retrieved and reconnected. On this topic, Vickers and Lee had an important point: “ connectionist accounts of semantic or meaningful information are based on conceiving of meaning as activation of a limited number of features, at least at the input layer.” This means that this theory works best if the information has depth over just memorizing facts.

Production-rule-related theories of memory

In the study of the human cognitive system, productions (or sometimes referred to as production rules) are rules for reaching a particular goal or solving a problem. They are commonly considered components in our long-term mermory (see the Productions in Cognitive Psychology section below). Essentially, each production can be considered one single guiding step in the thinking process. It can commonly be represented as a prescription of what actions to take in what kinds of conditions – a “condition-action” or “if-then” sequence. For instance, a production within the overarching goal of frying an egg could be depicted as:

IF the goal is to fry an egg,

and the raw egg has been removed from its shell,

and the pan has been heated to reach the right temperature,

THEN place the raw egg in the pan,

In this situation, the production guides the course of action depending upon the condition(s). Once the conditions have been met (the egg has been removed from its shell; the pan has reached the right temperature, etc.), the rule becomes applicable and the action (putting the egg in the pan) is performed.

Key features

Important features of productions include that, as mentioned previously, each production can be thought of as one rule or step, and the learning of which can happen separately from acquiring other productions. Also, due to this nature, when an elaborate and complex skills or cognitive function/process is acquired, it likely means that the entire series of productions that constitutes the skill is learned – connected subgoals are strung together to achieve an overarching goal. For instance, in the egg frying example, preceding the cooking process could be another task such as locating the nearest grocery store and going there to buy eggs, which is a subgoal in itself in the overall goal of cooking the egg. Of course, the number of productions in a process depends on its complexity.

Another important feature is that production rules are abstract in nature and can apply across different task situations of similar nature. For example, the aforementioned productions for frying the egg could also be applied to frying vegetables, which would involve the same contingency on the condition of the pan being hot enough and then the procedure of putting the vegetables in the pan.

In addition, productions can be specific to a domain of practice, such as within algebra in mathematics, or relatively general, such as pertaining using a vacuum cleaner.

Productions in cognitive psychology

Typically, in cognitive psychology, a dichotomy of declarative knowledge (or declarative memory) versus procedural knowledge is used to distinguish between the types of knowledge, experience, or skill that we all possess in long-term memory. Declarative knowledge refers to ideas or propositions that can be explicitly stated or articulated whereas procedural knowledge simply refers to skills or actions that can be performed to achieve a goal. Procedural knowledge is often difficult to express in words. In this sense, this dichotomy of declarative versus procedural can also be referred to as explicit versus implicit knowledge or memory.

With this context in mind, productions usually fall under the implicit, procedural knowledge category. In fact, production rules are often described as the contents of procedural knowledge or as the “embodiment of the skill”, because they are individual steps for guiding a course of action or cognition. Essentially, in simpler terms, productions are about “how to do things”, which is what procedural knowledge is about.

In general, with practice and more experience, a skill becomes more automatized, meaning that the productions that constitute the skill fire faster and more consistently. As this happens, the performer becomes less conscious of each individual production and gradually comes to perceive the sequence of firing productions as a single fluid action.

Evidence for production rules

In making the argument that production rules are psychologically real, Anderson asserts that the first piece of evidence is that production rules are apt at describing multiple aspects of skills and cognitive tasks in progress. That is, they provide a logical and plausible explanation of how tasks are performed. Another significant piece of evidence Anderson cites is that using production rules we are able to predict aspects of one’s behavior as a skill or task is being performed. For example, when we observe the condition of a pan becoming hot, we can then expect to see him/her putting food into it (the conditional action).

The ACT-R Model: A model of cognition and long-term memory based on production rules

A highly prominent theory which reflects the application of production rules is the Adaptive Character of Thought-Rational (ACT-R) theory, John Anderson's theory of human cognition that uses production rules as the building blocks of cognitive processes. The central argument posited by the ACT-R theory is that a complex cognitive skill comprises a large number of individual “units of goal-related knowledge”.

History of ACT-R

The theory originally stemmed from the Human Associative Memory (HAM) theory (one of the creators of this theory was also John Anderson, the creator of the ACT-R theory) that explained certain aspects of human memory and knowledge. It involved the notion of declarative knowledge but did not deal with procedural knowledge.

Using that as a foundation, John Anderson then proposed that procedural knowledge consists of production rules. After fine-tuning a few variants of his theory, he established the original ACT theory in 1983, which was aimed at explaining a wide range of cognitive processes.

Subsequently, after taking into account more evidence and emerging data on cognitive skills, Anderson believed that an element of rational analysis in the cognitive process should be integrated with the somewhat “mechanistic” nature of the original ACT theory, and therefore he created the ACT-R (R for rational) theory, which he felt was an improvement over the original due to its greater adaptive and selective ability towards the environment.

The theory’s initial focus was on human memory and cognition. Much of its most prominent application and development has been in computer model tutors (intelligent tutors). Briefly, these are computer software which can guide learners/students in problem solving by referring to production-rule-based models that generate solutions to such problems. These computer tutors are mainly developed and used in domains such as mathematics and science. More details of ACT-R’s applications as tutoring systems will be discussed in a different chapter.

Key tenets of ACT-R

There are three fundamental ideas that frame the theory: a) the representation of these knowledge units; b) their acquisition; c) their deployment in cognitive processes. These are discussed below.

Representation of knowledge

One central tenet of the theory is that cognition involves both the element of declarative knowledge (which is propositional, semantic knowledge, as mentioned earlier) and procedural knowledge (which is represented as production rules), and that the two work closely together in cognitive processing.

Declarative knowledge is encoded, stored, and represented in chunks, or individual units of human memory that resemble schemas (knowledge structures). Each chunk contains propositions or descriptive features about the subject item, stored in slots, including what larger category it belongs to. Chunks can be represented either in a textual or graphical format. For example, a chunk about frying an egg might be textually represented as: frying an egg is a type of cooking skill; requires the egg to be removed from its shell prior to cooking; requires heat. To the right is a possible graphical representation of a chunk.

Figure 7. Graphical representation of a chunk

On the other hand, procedural knowledge is represented as a set of productions. As previously discussed, the productions can take the form of an interconnected series of subgoals that are aimed at reaching an overarching goal.

The relationship between the two types of knowledge is that the chunks of declarative knowledge structures provide the conditions and courses of action necessary for productions to happen. For instance, in order to cook eggs, one must possess the knowledge chunks for buying them, removing them from their shells, and preparing the pan, etc. Without one of these, there will be a gap in the procedural knowledge. Therefore, having more chunks of knowledge implies more available production rules and better procedural knowledge. It can be also considered that declarative knowledge can be transformed into procedural knowledge, as will be discussed in the next section.

Acquisition of knowledge

Declarative knowledge is acquired in a fairly straightforward way, either from the perception of information or ideas from the environment or directly from instruction (being given information). Since declarative chunks of knowledge are required in order to inform productions, this tenet of the ACT-R theory implies that having the knowledge to perform a cognitive or procedural task basically entails gathering all individual chunks of information that the task needs – the task is a “sum of its parts”. Therefore, complex tasks require the collection of many chunks.

The acquisition of production rules in procedural knowledge, on the other hand, is slightly more difficult and less straightforward, since they cannot simply be told or articulated. Essentially, they are learned only as declarative knowledge is deployed. This means learners acquire production rules when they do tasks, not simply when they are given declarative information. It is key to note that this deployment can only occur in the appropriate contexts and conditions for the productions to take place. When the conditions for performing a task are appropriate, goal-oriented cognitive activities can take place, in which declarative chunks are put into action (or “executed”) in succession. In this way, it can be considered that they are essentially converted into production rules to guide the person’s actions towards the goal. With practice, this process of conversion can be improved or strengthened in terms of speed and accuracy. Thus, providing opportunities for practice and feedback is one highly conducive way to foster the acquisition of production rules.

Deployment context of knowledge

This aspect concerns ACT-R’s explanation of how our cognitive structure is able to summon the right type of knowledge for a certain context of task or problem-solving. This is the function of rational analysis – the “R” part of the theory’s name. The process of rational analysis identifies two elements in order to determine the right chunks and production rules to be activated in the mind: a) the chances that such knowledge has worked well in such a situation in the past; b) the chances that such knowledge is likely to work well in the situation at hand. Combining these two factors, this selective process recognizes the likelihood of a piece of knowledge being appropriate and applicable in a given task context.

In essence, this also implies that the human cognitive system maintains a record of what kinds of knowledge have been appropriate in what kinds of tasks, although this is likely to be a subconscious process in the mind. Thus, the theory’s explanation of this aspect basically describes a statistical process.

Summary of ACT-R’s theoretical aspects

In short, according to the ACT-R theory, declarative knowledge is encoded by perception of information in one’s surrounding environment (including the instructions that a student receives from teachers, parents, and peers, etc.); procedural knowledge is developed as a result of learning to deploy such declarative knowledge (often many units of it in succession) in the context of performing a tasks or solving a problem; and the selection of the right type of knowledge to deploy in a given situation happens according to the cognitive system’s estimate of how likely a piece of knowledge would be useful and appropriate.

Applications and empirical evidence for the ACT-R theory

Among a number of ACT-R’s applications thus far, a pair of experiments conducted by Anderson and his colleagues yields significant empirical evidence that supports the theory. These experiments studied how university undergraduates worked out most efficient routes from starting points through various mid-way points to final destinations on a map (of the city of Pittsburgh, Pennsylvania), taking into account factors such as cost and time. The experiments were done by monitoring students as they looked at the map on a computer screen and clicked on the locations/mid-way points they wanted to move to or pass through in order to reach the final destinations.

The findings from the subjects were then compared to the “thinking processes” and solutions produced by computer models based on the ACT-R theory (using sets of production rules) to solve the same navigation problems. The table below shows some examples of the model’s production rules used to determine routes in this navigation task:

[IN ORDER TO] COMBINE-ROUTES
IF	the goal is to find a route from location1 to location2, and there is a route to location3, and location3 is closer to location2,
THEN	take the route to location3, and plan further from there.
DIRECT-ROUTE
IF	the goal is to find a route from location1 to location2, and there is a route from location1 to location2,
THEN	take that route.

Table 2. Examples of the production rules to determine routes in the navigation task

The ACT-R model’s way of thinking was compared to that of the undergraduate students one step at a time – each single choice of route (each single production) made by the model was put alongside each choice made by each student (the mid-way points they clicked on). The results showed that the ACT-R model’s route decisions matched those of the students “67% of the time”, and even if they did not, they matched students’ second or third top choices, closely paralleling the way human subjects behaved cognitively.

In addition, another important finding was that the computer model’s latency (number of seconds taken) in making route choices was very similar to the decision paces of the subjects. Even though this finding was a relatively general correlation, it likely supports the ACT-R theory’s ideas regarding time required by the human cognitive system to make judgments based on production rules in performing cognitive tasks (consider and evaluate different route choices before making decisions).

The final result which is consistent with the ACT-R theory was that, with practice over the span of the experiments (about one week), the human subjects improved in their speed of optimizing route planning, likely supporting the principle of strengthened production rules in improved task performance.

Anderson notes the importance of this map navigation activity and its evidence in supporting the ACT-R theory, arguing that it involves a real-life task in which people need to consider real factors and consequences such as cost and time, as opposed to basing the experiments on abstract, academic problems such as mathematical ones, where there is little implication related to true situations. In addition, such a task of finding different routes to reach destinations involves more than one solution, meaning that solutions are of different degrees of success, which makes this a more realistic test of whether the ACT-R model is true to the human cognitive system.

Aside from these experiments, other highly significant empirical support for the ACT-R theory includes the work that has been invested in Intelligent Tutoring Systems (ITS), which is explored in detail by scholars such as Ritter and his colleagues. Although not within the scope of this chapter, further discussions regarding computer tutoring systems will be carried out at length in a different chapter.

Instructional implications of the ACT-R theory

Based on the aforementioned tenets and features of the theory, Anderson et al. provide a list of principles for designing tutoring systems, some of which may also be applicable to instructional design in general, including ideas such as: a) representing a skill as a set of productions;b) clarifying subgoals (productions) in solving a problem; c) provide instruction specific to certain problem contexts while also promoting transferable production rules; d) focus only on necessary production rules to reduce memory load; e) provide instruction of appropriate granularity depending on how fine-grained production rules need to be in a task; f) retracting instructional assistance appropriately as learners gain competence.

You can go to the Chapter of Problem Solving, Critical Thinking, and Argumentation (2.5 Cognitive Tutor for problem solving) and Learning Mathematics (4.5 Cognitive Tutor for teaching algebra) to get more detailed information of Cognitive Tutor and its effectiveness.

Criticisms of the ACT-R theory and responses

Since the ACT-R theory maintains that acquiring or understanding a skill (or cognitive task) simply entails learning the individual productions that constitute it, it has faced the criticism from a constructivist learning point of view that the understanding of knowledge or skills is constructed by the learner him/herself, rather than achieved in a pre-specified manner.

This, coupled with the theory’s idea that learners’ answers or solutions to a problem should conform to or be pigeonholed into certain sets of production rules, has given it a somewhat behavioral-oriented approach to cognition, stifling elements such as metacognition. Anderson et al. respond by stating that the ACT-R’s approach shares similarities with a behaviorist one in terms of how instruction should focus on breaking down a skill or task into components, but they argue that the ACT-R represents the task in a more abstract way (likely more transferable between contexts) than typical behavioral methods.   

In addition, John Anderson has acknowledged that, since a key educational or instructional design implication of the theory is to foster the acquisition of individual production rules in order to accomplish a task, the primary emphasis of the theory could be considered to be efficiency in learning. Unsurprisingly, this priority might seem questionable to those who value learning depth or richness rather than efficiency or speed. Anderson’s response is that depth in learning can simply be interpreted as enriching declarative and production (procedural) knowledge, which entails practice and feedback.   

Glossary

Assignment of meaning

When meaning is assigned to a perceived stimulus

Automaticity

The development of a skill to an automatic level where it becomes an implicit process that does not require much thought

Cognitive Theory of Multimedia Learning

Mayer’s theory based on the Dual-coding Theory, the notion that cognitive load must be managed in learning, and the notion that learning entails organizing and integration information

Concepts

A way of sorting mental information into meaningful categories and structures; A “building block of cognition”

Conditional knowledge

Knowledge of different strategies and when and why to use them; The knowledge of “knowing why”

Declarative knowledge

Factual knowledge such as knowing capital cities and algebra formulas; The knowledge of “knowing what”

Dual-Coding Theory

Paivio’s theory that providing information in both visual and textual format may benefit learning

Episodic memory

Memory that is specific to each individual’s personal experiences

Essential learning

Cognitive demands that are necessary for understanding the to-be-processed information

Extraneous cognitive load

Anything that causes cognitive load outside of the original cognitive task

fMRI

Functional magnetic resonance imaging. A neuroimaging technology that is able to monitor brain activity by detecting changes in blood flow to activated areas of the brain

Forgetting

memories cannot be recalled

Incidental processing

Cognitive demands that are useful for understanding the to-be-processed information, but not entirely necessary

Intrinsic cognitive load

The cognitive load required of any given task

Long-term memory

Memory that is developed over days, months, years and/or decades of time. The permanent accumulation of memory developed over a lifetime

Procedural knowledge

Knowledge of how to complete daily tasks, such as driving a car, skiing, or making coffee; The knowledge of “knowing how”

Prototype

An extremely common/prominent concept

Recall

When information previously stored in short- or long-term memory is remembered

Reconstruction

When information previously stored in short- or long-term memory is reconstructed at recall, but not remembered exactly

Referential holding

When one holds information temporarily within working memory while other information is simultaneously being processed

Rehearsal

Cognitive repetition which allows information to remain active in short- or long-term memory

Retrieval

The act of transferring information out of long-term memory and into working memory

Scaffolding

A temporary framework of supports while an object (or schemata) is “under construction” that is taken away when completed and the support is no longer needed

Schema or Schemata

Cognitive structure(s) that help organize knowledge and guide thinking, perceptions and attention

Semantic memory

Nonspecific memory of general concepts and procedures; Not related to specific individual events or experiences

Sensory register

A cognitive function within the working memory in which perceived input is stored to receive meaning

Spreading activation

The recall of an idea triggered by the recall of another associated idea

Encoding and Retrieval

In this chapter, the cognitive processes of encoding and retrieval and their role in learning will be explored. Encoding refers to the process of converting information in working memory to knowledge in long-term memory. Retrieval refers to the processes that allow learners to access information stored in their long-term memory and bring it into their conscious awareness / working memory. The functions of both of these cognitive processes as well as common examples and strategies of how to more effectively encode, retain and retrieve information for different purposes and contexts will be considered.

Encoding Processes

We will discuss two key aspects of encoding. First, we will look into the processes from which information is translated into memory, and secondly, the strategies which can be used to aid this process. A portion of information we attempt to learn is automatically encoded; the rest of the information (in order to be learned and stored) involves a conscious effort to transfer the information to the long-term memory. The way in which we remember information, and recall it from our memory, depends greatly on the way it was originally encoded.

A common representation of memory systems

Before we are able to decode information, it must first be placed into our long-term memory, which is referred to as encoding . There are several strategies that students can use in order to successfully encode the information that is being learned. When encoding simple information, three distinct strategies can be used. Elaborative rehearsal, defined as “any form of rehearsal in which the to-be-remembered information is related to other information”, is a deeper encoding strategy than maintenance rehearsal, which is simple repetition of information . Mediation is a simple elaborative encoding strategy that involves relating information that is difficult to remember with something meaningful . Another commonly used strategy is mnemonics, in which new information is paired with already learned information. This gives meaning to the new information, which allows it to be more memorable . In order to encode more complex information, other strategies such as activating prior knowledge, KWL and concept mapping are used, because they can encode more semantic information, creating "deeper" connections to related concepts and personal understanding. .

Encoding Simple Information

The information we attempt to learn varies in complexity. In most cases, the complexity information affects how the person attempts to learn. Some information is simple (e.g., 'Sandra is 10 years old') while other information is more complex and requires critical thinking to be fully understood (e.g., a newspaper article about a political event). Different types of information require different strategies for learning and encoding, so it is important for the learner to choose the correct strategy. In this section we will discuss strategies to use for remembering simple information.

Rehearsal

The first strategy for simple encoding is rehearsal. An example of rehearsal is a student studying for a test. We will discuss two types of rehearsal: Maintenance rehearsal and Elaborative rehearsal. Maintenance rehearsal is a shallow form of processing and is most beneficial for simple tasks such as remembering a phone number. Maintenance rehearsal involves repeatedly focusing on a piece of information to keep in short term memory. During maintenance rehearsal, information can be easily lost if the rehearsal process is interrupted, so it is not the best method for remembering information. Because the information rehearsed often does not make it into long-term memory, the information cannot be recalled later on making it insufficient for encoding complex information. Maintenance rehearsal is useful for remembering information in the present moment. If the information is more complex or needs to be recalled later on, elaborative rehearsal is useful. Elaborative rehearsal involves relating the to be learned information to other information. Elaborative rehearsal helps encode complex information because it requires the learner to relate the new information to their existing knowledge which helps build connections and strengthen understanding. Learners who relate new information to prior knowledge are more likely to remember information and to retrieve it later on . Studies have shown that the long-term retention of information is greatly improved through the use of elaborative encoding .

Mnemonics

Mnemonics

Mnemonics are strategies to use for learning unfamiliar concepts. They increase the probability of encoding unfamiliar information. Mnemonics involve pairing unfamiliar concepts with familiar concepts to increase the chance for remembering a concept. It involves putting information into a more easily remembered or more meaningful format. Bruning et. al describes mnemonics as strategies for remembering information that create more elaborate coding of new information and stronger memory traces . Mnemonics can include familiar strategies such as stories, rhymes, and songs.

Although research suggests that mnemonics are widely used, theorists have questioned their value. A common criticism is that they encourage rote memorization and may not aid higher order skills such as comprehension or transfer. There are also varying views about whether mnemonics promote long-term learning. Mnemonics are purely designed to enhance recall and do not facilitate higher order learning, so criticisms surrounding their ability to in higher order learning may be irrelevant. Mnemonics are designed to aid the remembering of unfamiliar concepts, and they are especially useful in lower level learning such as fact-learning. Lower level learning in turn affects higher order concepts. Higher order learning is facilitated if an understanding of basic concepts is previously attained, so it is arguable that mnemonics in fact do affect higher order thinking. It can be argued that mnemonic strategies also promote long-term learning as most people remember the acronym for the colours of the rainbow for the majority or their life. Carney and Levin conducted a study to test the usefulness of mnemonic strategies through tests such as matching, recognizing and comprehension analysis measures. The results showed that the participants who used mnemonic strategies had significantly better results than students who used their own preferred methods. Mnemonics may also have some positive effects such as increasing motivation to study. In one study, students reported on a survey that having acronyms on a review sheet made it easier for them to remember course content and made them start studying earlier. Other studies show that students think that some mnemonics are easier, faster, more enjoyable, and more useful than rote rehearsal and that mnemonics can reduce test anxiety. Whether mnemonics strategies assist with long-term learning or learning past rote-memorization, they have some clear benefits.

Despite some criticism surrounding the usefulness of mnemonics, they are beneficial when applied the correct way. Mnemonics help with remembering difficult concepts, but they should not be used in replace of primary study tactics. Mnemonics should not be implemented to help overall learning or to enhance comprehension, but they can be used to aid the recall of new or difficult information. Mnemonics help some students improve their recall of the amount of factual information when using mnemonics by two to three times, and using mnemonics can also make learning more fun and easier for some students. Specific strategies for encoding can help in the retention of information and can may lead to more successful comprehension. Mnemonic techniques described in this section include the keyword method, acronyms, and acrostics.

Keyword method

The most popular mnemonic strategy is arguably the keyword method. The keyword method aids in the retention of vocabulary words especially in learning foreign languages. The keyword method involves localizing a keyword, or similar word to the foreign word in order to simplify it. Seeing the keyword or similar word associated with the foreign word activates the unfamiliar word and primes the formation of an image in a learner’s mind. This technique involves the learner focusing on a native language keyword that sounds similar to the to-be-learned word. The keyword method is implemented by generating a sentence to link the keyword with the to-be-learned word, or by using an illustration or a visual image. For example, if the to-be-learned word is the Spanish word carta, the English-speaking learner could use the keyword cart and then construct a meaningful interaction between the keyword and the definition. Some criticisms suggest that the keyword method is not useful when there is not an obvious keyword. On the other hand, one study demonstrated that two or three hours of training with the keyword method can lead to a 70% increase in recall with German language vocabulary suggesting the keyword method is very beneficial.

Acronyms

Acronyms are a popular mnemonic strategy involving the first letters of word lists. The first letters of each word in the set are taken and combined to form a new word – called an acronym. Many students use this mnemonic strategy in their work without being aware they are engaging in a mnemonic strategy. Common examples of acronyms include using the letters BEDMAS to remember the order of operations in completing a math equation, and ROYGBIV to remember the colors of the rainbow. If you have ever used these words to cue your memory, you have engaged in a mnemonic strategy to aid your encoding and retrieval of simple information. Each letter also serves as a retrieval cue for the target items.

Acrostics

Acrostics are similar to acronyms but involve using a sentence to help remember a segment of letters. The first letters of a list of words serve as the first letter in a new sentence or phrase. A commonly used acrostic is using the sentence “every good boy deserves fudge” to remember the lines of the treble clef: (E, G, B, D, F) (3). An acrostic created with the key terms used in the Encoding Simple Information section are shown in the figure below.

Acrostic Example for the Key terms in the Encoding Simple Information Section

Highlighting

Highlighting

Highlighting text is one of the most common study strategies used by students. Highlighting involves selecting important text in a passage and marking it for later reference. Commonly, students use highlighting as a learning tactic to help with future studying when they intend to come back to the material. Five simple steps for approaching highlighting are: (1) familiarizing yourself with the general topic of the text, (2) reading each paragraph slowly and carefully, (3) identifying and marking the main points, (4) revising your understanding of the text based on the information you found, and (5) applying this information to memory. Learning to highlight text properly requires high levels of reading comprehension, as well as problem solving techniques and critical thinking. Students must learn to identify what concepts are important, relevant and appropriate to the information they are learning. The process of highlighting a text should pose some difficulty to students in order to be beneficial because focusing your attention on the important material facilitates deeper encoding of the text meaning.

Highlighting engages meaningful processing of text that includes reading the text, activating prior knowledge, selecting important information from the text, linking this information to the previously read text, and constructing a representation of the text meaning. Each of these steps strengthens the encoding process, so this information is further processed in the working memory. Marking parts of sentences, or individual words also keeps the student’s focus on the important information. There are many different theories as to why highlighting may be beneficial to learning. The cognitive processes used when deciding which of the text to mark results in students thinking more about the material leading to deeper processing of the text meaning when compared to reading alone. Actively choosing which text to mark and which meanings are important changes the way that students read and re-read the text which makes the information more important and memorable .

While there are many hypotheses as to why the cognitive processes involved in highlighting may be beneficial to learning, some studies have shown beneficial results from highlighting, while others have not. One research study that supports the benefits of highlighting compared participants who read highlighted information with participants who read non highlighted information. The study found that the participants reading and rereading highlighted information improved their recall of the text when compared to students who read only the plain text. One research view that opposes the helpfulness of highlighting argues that most students do not know how to highlight information correctly which reduces its benefits. Students may highlight information that is not relevant if they do not focus their attention on the text long enough to determine what information is the most meaningful. This draws student's attention away from the important information, and acts more as a distraction increasing cognitive load and inhibiting deeper processing. Another view opposing highlighting's benefits states that highlighting has a placebo effect. In other words, students may believe highlighters are effective simply because they have always relied on them. This belief can backfire when students become over confident and comfortable with highlighting and do not give the process much thought. Students that are overconfident may also assume that they already know the information when they reread it which causes them to skim and reduce deeper processing.

Encoding Complex Information

Craik and Lockharts popular levels of deep processing theory suggest that the level to which an item is cognitively processed largely affects its memorability. Their theory suggest that memory traces are records of analyses carried out for the purposes of perception and comprehension, and deeper, semantic, processing results in more durable traces. Semantic encoding refers to encoding the meaning of a concept and can lead to a deeper level of understanding and more successful encoding. Typically, items encoded using semantic operations are better remembered in a subsequent memory test than items encoded using shallow operations. If the semantic base or meaning of the new information is the focus of processing, then this information is stored in a semantic memory code and is remembered. However, if only superficial aspects of the new information are analyzed, then the information is not deeply encoded and not easily remembered. In Craik and Lockhart's terms, memory depends on depth or processing. An observation from experimental and everyday settings is that if we learn an item using semantic encoding, memory is enhanced when compared to using "shallow" operations, such as attending to its structural features. Deep processing is defined as processing centered on meaning. Shallow processing refers to keying on superficial aspects of new material. An example of a shallow processing is highlighting words in a passage as discussed for encoding simple information; whereas, reading a passage and putting it into your own words is deep processing. Putting an essay into one's own words requires thinking about the meaning of the content and carefully analyzing and comprehending the material. In general, theorists agree that deep encoding results in more elaborate memory traces, and that this in turn affects later memorability.

Activating Prior Knowledge

Prior knowledge is the pre-existing knowledge a student has for a particular topic. Activating prior knowledge allows students to engage in the material by relating the to-be-learned information to information they are already familiar with allowing them to make inferences and build connections. This facilitates encoding and guides recall for the new information. Additionally, relating new information to personal experiences enhances the possibility that the information is elaborated or recalled in the future. Students of any age can engage in prior learning which benefits the encoding and retrieval of information for all levels of learners. Activating prior knowledge can involve various instructional strategies designed to stimulate students' relevant knowledge in preparation for a new learning activity. For example, Van Blankenstein et al. reported that students who activated prior knowledge before self-study recalled more information after the study session compared to students who did not activate prior knowledge. Additionally, students who activate related prior knowledge before engaging in learning encode more information than students who activate irrelevant knowledge. This highlights the importance of selecting appropriate instructional strategies to help students select relevant prior knowledge. Activating prior knowledge is a simple and effective learning strategy because it involves any teaching method that helps students relate what they already know what they are about to learn. Examples include group discussions, experiments, review sessions or personal writing reflections. The following section discusses some examples of instructional strategies in more detail.

KWL comprehension strategy

The Know-Want-Learn (KWL) strategy was created by Donna Ogle in 1986. Ogle created the strategy to help teachers adopt more student centred instructional procedures. The KWL was originally intended to support and improve reading comprehension but has since been adopted by several areas of study.

The KWL strategy follows constructivist theories about information activation and recall. The different steps of the strategy (Know, Want, Learned) activates students prior knowledge, helps students recognize their current schemas, and links newly learned information with old, solidifying and strengthening this information. The purpose of the KWL is for students to construct their own understanding of what they know and make meaning of new information. The KWL strategy provides an interactive learning experience that teaches students to recognize what they don’t know about a topic; a beneficial metacognitive skill for learning. Through the steps of identifying what they know, what they want to learn, and what they have learned, the KWL teaches students how to be active and take charge of their own learning.

Using the Know-Want-Learn Strategy

KWL Chart Example

The KWL strategy is often represented in the form of a KWL chart and follows this three step procedure:

1. “What do I know?” The first step is the “Know” phase. Before new information is brought into the classroom, students are asked to recall what they already know about a specific subject. This step can be collaborative; the students brainstorm and share information about their prior knowledge as a group and the teacher records this information in the first section of the chart . The teacher’s role in this portion of the strategy is to facilitate and stimulate the discussion and not to correct students' ideas of what they believe they know about a subject. This portion of the procedure works to activate the students' prior knowledge and any previous domain related schemas students already may have. After the initial brainstorm, students' are then asked to organize their ideas into logical categories. This step works to chunk information and link ideas together. Once students learn to make information categories, this skill can be applied to all areas, aiding in their formation of schemas and reading comprehension.

2. “What do I want to learn?” The second step is the “Want” phase. After prior knowledge is activated and recorded in the first section of the chart, students are then asked what they want to learn about a subject. Questions are recorded in the second column of the chart. This step furthers the brainstorming process because it requires learners to think deeper about what they know, recognize what they don't, and identify what interests them. Asking the students what they want to learn also promotes the students' personal involvement in the process of learning which may increase their interest in the subject of study.

3. “What have I learned?” The final step is the “Learned” phase. After new information about the topic is presented, students are asked to think about what they have learned. This step requires students to reflect and think about the new information to make connections to prior learning and address any misconceptions they may have about the subject. The students' incorrect knowledge can be clearly identified by comparing the first and last column of the chart, allowing students to recognize their misconceptions and correct their understanding of the subject. Moreover, by presenting all of the information visually, students are able to see and link new concepts with their prior knowledge, which aids in deepening their understanding of what they have just learned.

Research and instructor feedback on KWL

The KWL strategy has been found to be effective and helpful in all grades and subjects , is easily adjustable to fit multiple age groups, and works effectively to reinforce new information with old. Longer, more demanding lessons can be divided and reflected upon in smaller chunks to minimize cognitive load and difficulty. Although the KWL strategy was originally formatted as a learning comprehension tool, researchers have found the KWL approach to be beneficial to learning and comprehension in several different areas of study. After implementing KWL, increased academic achievement has been reported in areas of learning such as reading, math, science, language, and the development of metacognitive skills. For example, a study of grade 6 math students found that those who had undergone mathematics instruction with the KWL format performed statistically significantly better on their knowledge tests than those who did not use the strategy. This application of the KWL strategy resulted in increasing the academic achievement in the participants.

Teachers report positive effects when the KWL strategy is incorporated into their lesson plan, including notably positive feedback from the students who are instructed in the use of this tool. Primary research continues to support the KWL as a learning comprehension strategy and suggests that it outperforms many other comprehension tools and it continues to be preferred by learners

Concept Mapping

Concept Map

A concept map is a learning strategy in which relevant information about the topic of study is visually organized according to the related concepts. Concept maps show the relationships and interactions between ideas relating to an area of knowledge. These graphical representations enable students to encode the meanings of the concepts more deeply and with better understanding. Some more common forms of concept maps are Venn diagrams, tree diagrams, flow charts, and context diagrams. Concept maps can be used and adapted to fit many different subjects of learning.

When a student constructs a concept map they must consider the possible relationships between concepts. This activates their prior knowledge and schemas. By working out the connections between concepts new information is added to the student's knowledge schemas. Students must think critically to identify logical relationships between concepts which allows them to link new ideas to old schema and therefore reinforce learning and strengthen encoding of the new material Concept maps require students to think deeply about the information they are learning, in order to identify the main points. By building a concept map, students learn how to represent what they know and how to organize information in a logical, sense making way.

Use of Concept Maps There are many different ways that concept maps can be used academically. Students can individually make concept maps while they are learning. This would help students in their learning process by supporting their ability to identify key concepts of an area of study and how they relate to each other. Students would be able to grasp a deeper understanding of the material throughout the period of study. Concept maps could also be used after students have received instruction as a reinforcement strategy. For example, students could fill in a blank diagram as a means of formative assessment of their understanding. Students could also use concept maps as a method of studying to promote recall. Lastly, concept maps can be used by instructors as a teaching aid. Diagrams and visual representation of new ideas are useful tools that could help teachers in communicating and clarifying information to students. This may be the most effective use of concept maps as the instructors have a clear understanding of the information they are trying to deliver.

Research Findings Research studies show that the use of concept maps can help students learn how to organize information, enhance their academic performance, and increase their knowledge retention abilities. This is because the process of forming a concept map relies on encoding strengthening procedures such as deep thinking, organizing, and relating old information to new. For example, a study comparing the retrieval effectiveness of information practiced in either concept maps or in paragraph form. found that as a retrieval activity, both formats gave similar results. This suggests that concept maps are just as effective as paragraph writing for retrieval. It is worth noting, however, researchers in the study reported that the participants preferred the paragraph retrieval format to the concept mapping strategy.

Retrieval Processes

In the first section of this chapter you learned about the encoding process and its role in constructing memories. In this section we look at the retrieval process and its use in reconstructive memory. Retrieval refers to the means by which memories are recalled from long-term memory. The process of retrieval is a complex but essential process which involves converting memories into conscious experience. Many elements can affect the efficiency of retrieval such as the environment present at the time of retrieval and the learner’s study tactics. For example, whether the learner studied information for recognition or recall plays a large part in how well information is remembered. Empirical evidence suggests that students who expect recall tests which are primarily essay based focus more on the organization of information. On the other hand, students who anticipate multiple choice recognition tests focus on separating concepts from one another. Retrieval of stored information is an essential part of accessing prior knowledge and demonstrating understanding through assessment tasks. However, problems with retrieval such as improper recall of memories can impede the learning process. This section will focus on reconstruction of memories and information, give specific examples and definitions, provide an insight into the research in this field, and examine the errors that can arise during the process of memory reconstruction.

Storage and Reconstruction of Memories and Information

When information is taken into the brain during encoding only select, key-elements are stored in long-term memory. This storage is aided by the structural help of schemata, mental frameworks that help organize knowledge. To illustrate this point, think of how you recognize that a dog is a dog. Your schema for "dog" may include, four legs, barks, has a tail, and so on. Some people may include in their schema for "dog" that they are pets, while others may include that they can be dangerous and can bite. The individual components that make up a schema work together in constructing one's perceptive of that schema. When we want to retrieve certain information for recall, the schemata will be activated and the stored pieces of information will be combined with general knowledge, thereby reconstructing the memory into a whole. Therefore, reconstructive memory can be defined as the way in which the recall process reassembles information by building upon the basis of limited key details held in long-term memory with the general and domain specific knowledge in one’s repertoire. The reconstruction of memory allows our minds to deal with fragments of information, which is far easier to handle than taking on every piece of information we come into contact with all at once. The reconstruction of memory is not a fully accurate system of retrieval; mistakes can arise out of the reconstruction process that can distort the original information.

To illustrate the concept behind memory reconstruction, imagine a jigsaw puzzle and the box that holds its pieces. The individual puzzle pieces come together in creating a unified image but are stored as individual units within the box. When the pieces are reconstructed in a meaningful way, starting with one piece and it being connected to another piece and so on, the entire image comes together as a unified whole image. The completed puzzle is now a single entity and now too big to fit into the box. In order to have the puzzle stored properly in the box, it needs to be deconstructed and have its individual pieces put back into their original container. The idea here is that memories and information are deconstructed for easy storage, yet have the ability to be reconstructed in collaboration with general and domain specific knowledge in order to become a single unit of meaningful information.

Bartlett's Research on Memory Reconstruction

The question of how memories are recalled has been under debate for many years: is recalling information from memory a reproductive process or a reconstructive process? After several experiments regarding memory reconstruction, many cognitive psychologists agree that remembering is a reconstructive process. One experiment that widely impacted this debate was done by British psychologist Frederic Bartlett and was expressed in his book Remembering:A Study in Experimental and Social Psychology. The experiment involved a group of students who read a short story from an entirely different culture; the fact that the story was from a different culture was to ensure that the material was not too familiar to the students. At various lengths of time since the original reading, students were asked to reproduce the story to the best of their abilities. Two years after the original reading, one student was asked to reproduce the original story. The only pieces of information the student could reproduce were the names of the two main characters in the story, Egulac and Calama. After some thinking, the student was able to connect several other aspects of the story to the vivid names that she originally remembered. Although these aspects did not match the original story exactly, it was clear that they were inspired by the original content. This experiment shows that remembering can be an active process. By combining key points of interest from long-term memory with prior knowledge we are able to produce a whole product (memory) that closely matches the original experience. This experiment supports the reconstructive nature of memory because the student started with a main point of reference, then actively tried to make connections, ultimately reconstructing the original story, or at least a story that resembles the original).

Errors in Reconstruction

The work done by Bartlett sets the stage for addressing the errors that can arise during memory reconstruction. As stated earlier, the student in Bartlett’s experiment was able to reconstruct her memory of the story, but the reconstructed memory did not exactly match the original content. Bartlett's experiment demonstrates that remembering is a reconstructive process and therefore vulnerable to errors making this not fully reliable source of information about the original experience. Two main sources of error in memory reconstruction are confabulation and selective memory.

The first source of error in memory reconstruction, Confabulation, is the unintentional fabrication of events displayed as real memories in one's cognition. It is a common problem affecting those who have suffered from brain injuries or psychological diseases. Confabulation occurs when the key pieces of information in long-term memory, the pieces that start the reconstruction process of producing the memory, are lost. This loss can be caused from brain trauma or disease. The brain makes up for this loss of information by coming up with new information that seems accurate, resulting in the invention of a confused memory. Confabulation can range in severity depending on the individual and their medical condition.

The second source of error, Selective memory, is the active repression of negative memories. Alternatively, selective memory could be described as the active focus on positive memories. This causes errors in the reconstruction of memories because the recall process is disturbed. When a person actively represses negative memories those memories will be forgotten. The forgotten material will not be recalled because even the proper cues will not connect to the repressed material.

Recalling Specific Events

While reconstruction of memories occurs when people try to retrieve memories from general information and memory storage, retrieval of specific bits of information- like specific life events- occurs under a slightly different process. In this section the recalling of specific events will be looked at. We will discuss the role and function of episodic memory has and examine a phenomenon known as flashbulb memories.

The Role of Episodic Memory

Episodic memory is defined as the "storage and retrieval of personally dated, autobiographical experiences". Appropriately named, this type of memory focuses on life events, like recalling childhood events, where you vacationed last summer, and even what you had for breakfast last Sunday. These types of memories are retrieved with the help of associations that link the event to a specific time or place. Robin, Wynn, and Moscovitch studied the effects of spatial context on the recall of specific events. These researchers were interested in whether actually being in the context or simply hearing auditory cues about the context will enable the recall of events. Robin and colleagues found that locations, compared to people, served as a better tool for recall when participants were asked to either imagine or recall an event- although both were better when they were highly familiar It is interesting to note that Robin et al. found that even when there was no location specified for the scenarios provided, the participants were much more likely to generate a spatial context than a person. The researchers state that "participants spontaneously added location information to the person-cued events when none was specified" . Furthermore, when spatial cued events were compared against person cued events, it was discovered that the recall of memories was much more vivid and detailed. Thus, the researchers concluded that spatial cues were much more effective for accurately recalling specific events. This study portrayed how the location and time of various events is a salient factor for retrieval of episodic memories.

There is an ongoing debate among psychologists whether episodic memory and semantic memory, which is defined as a "memory of general concepts and principles and associations among them", are different types of memory. Researchers are investigating brain activity in people with amnesia who are no longer able to retrieve episodic memories. A study on individuals with Alzheimer’s Disease, a type of dementia characterized by progressive degeneration of the brain, found that people with amnesia have significant impairments in all domains of episodic memory.. The greatest impairments were evident in acquisition of memory, delayed recall and associative memory

Research on people with amnesia inspired many psychologists to investigate the functions of implicit memory; this type of memory is an automatic and unconscious way of memory retention. It is interesting to note that oftentimes our memories are not available to our conscious mind for recall, but can still influence our behaviour due to a previous event. Early theorists believed that the "inability of such individuals to transfer verbal materials from [short-term memory] to [long term memory] played a critical role in their amnesia". However, this view was not adequate, as it became evident that individuals suffering from amnesia were not impaired in all kinds of long-term verbal memory. Further studies have revealed that individuals with amnesia have the ability to use implicit memory when completing various tasks, like spelling, suggesting that there is no division between semantic memory and episodic memory.

Flashbulb Memories

Flashbulb memories are another type of memory for recalling specific events. This type of memory is incredibly specific and is tied to events with an emotional relevance to the individual. For example, individuals may experience flashbulb memories when remembering an emotional event, such as the 9/11 terrorist attacks on New York City. Although flashbulb memories may be considered to be perfect accounts of the event or events that have occurred, research has discovered something quite on the contrary; that flashbulb memories are not actually as accurate as previously assumed. This gives rise to the debate on whether flashbulb memories are a "special class of emotional memories", or whether they should be categorized as ordinary autobiographical memories.

Relearning

Relearning is a process of reacquiring lost or forgotten information, while using less time compared to the initial attempt at learning the same material. This process demonstrates how parts of memories are stored long-term without our awareness. In support of this is how much faster we can relearn seemingly lost information compared to first tries of learning it. A good example would be the case when a learner memorizes a random set of words and then after some time, when it is impossible to recall any of it, he or she repeats the process. The comparison between the amount of time that was needed to memorize the words for the first and the second time would demonstrate that the second attempt was shorter in duration. In the next section, we will look at similar experiments in the past.

History of Research on Relearning Methods

Hermann Ebbinghaus was one of the first researchers to examine relearning methods in his work. He practiced relearning by memorizing nonsense syllables to the point when he could repeat them without an error. After some time, when the memory of it was completely gone, he relearned the same set of syllables and compared the number of attempts made during the initial and subsequent sessions. The fact that the second try required less time to succeed in recalling suggested that some information retained after initial session.

However, relearning methods remain understudied in modern memory research, and more widespread approaches like recall tests have taken their place. One reason for that is an apparent insufficiency in measuring any visible savings while relearning complex materials, which usually require deeper understanding alongside the sheer memorization.

Distributed versus Massed Practice

Although it is unclear how exactly relearning occurs, research indicates that the way in which learners practice their studies has a major impact on both learning and relearning. There are two ways that practice that can lead to quite different learning outcomes. One is distributed practice - a certain amount of study sessions which take place regularly over time (e.g., working on improving a skill for several weeks or years). The opposite is massed practice, where learners make a one-time intensive effort working on a task (e.g., preparing for a test overnight).

Subsequent retention of information proves to be more successful when using a distributed practice method. At the same time, if the goal of studying is to pass a test or just use certain knowledge once or twice, massed practice might be a better choice. Thus, the purpose of the learning activity could influence which of these types of practice learners adopt in a given activity.

A number of non-experimental studies had examined the effect of distributed practice on mathematical knowledge retention. In particular Bahrick and Hall (1991) analyzed how much the subjects remembered from school algebra and geometry classes after 1 to 50 years. Results of the study indicated that the more different-level classes of the same subject that a student took in school (which means that he or she was exposed to certain amount of repetition of the same material), the better the student’s memory of the subject was.

Massed practice can be beneficial too, in particular while meeting two conditions. First case is when the goal is not understanding, but displaying a particular behaviour, which would generate stimulus-response linkages. Second example is when it is used by an expert who already holds sufficient amount of knowledge in the field.

Relearning after Brain Injury

Another interesting domain, where relearning occurs as a necessity, is the cases of people forgetting sometimes not only declarative, but even simple procedural knowledge that we all have been trained to perform since early childhood. When brain injury results in dysfunction between different parts of the brain, motor and cognitive functioning suffers. In that case, damage can cause problems in performing even regular every-day behaviour. Observational learning appears to be one of the most useful relearning tactics for individuals with such injuries. When watching others performing a needed activity, patients form a mental representation of it. If accompanied by sufficient reinforcement, such practice can produce positive results for patients who are capable of focusing their attention on the object and who can plan and execute their own behaviour.

Testing as Retrieval Practice

When thinking of a test, most students will only consider its outcomes in the form of a grade or a conclusive estimation of their abilities and knowledge, while research proves that testing can be a solid learning tool itself. Depending on the desirable outcomes, tests can be designed and implemented into the curriculum in much more useful ways than just for assessment purposes.

Testing Effect

The principle of the testing effect states that if being tested during the time of study by undergoing smaller tests and quizzes on the material, students will perform better on their final test. Under certain conditions tests can provide much more positive impact on students' future retrieval of information, than spending the same amount of time on rereading the material. That standard tends to be confirmed even if no feedback follows the test and performance on the test itself is not perfect. Thereby, after initial studying of the material, it would be more beneficial to undergo some tests on it, than rereading the text again.

However, better effect takes place if detailed feedback for the test is provided or if performance on it was successful. Research indicated that the number of successful tries increases long-term retrieval effect respectively. Even better conditions are provided when those testing practices are distributed across several days and take place repeatedly.

Several reasons form the basis of tests providing more positive impact on students retrieval outcomes than simple rereading of study material. One of such reasons is practice on the retrieval, when learners have an opportunity to work on their abilities to find and extract needed material out of their memory under small pressure of a challenge. Also, if there is a resemblance between practice and final tests, such actions will put retrieval processes into right context, which provides additional connections between encoding and decoding conditions.

Research on Testing for Retrieval

Despite the fact that students usually assume that the primary goal of being tested is to be evaluated afterwards, cognitive psychologists have been aware of tests’ ability to enhance retrieval for a long time. Several research methods were used to verify this. First, it required the students to learn new material and then take or not take a test on it before the final exam. Results proved that those who took the additional test performed better on the final one. With such a method some researchers have questioned whether positive results depended on test itself or they were caused by additional reminders about the material in the test. Additional research was conducted that required students to either take a test after initial learning or to restudy the material without taking a test. Final tests again showed that students who took the additional test performed better on the final one. As for the nature of the material being tested, equally beneficial results were found for remembering words, texts and illustrations. Overall, there were conducted numerous studies which supported the conclusion that tests reinforce learning outcomes.

Retrieval efficiency may be improved by Roediger et al.'s "testing effect". This theory involves using tests related to the material being studied in an attempt to improve overall learning for a final test. A study on the benefits of this type of retrieval practice examined whether the benefits of retrieval practice could transfer to deductive inferences. The results showed that the students in the testing condition produced better final-test recall of the content but no enhancement in multiple choice recognition questions. Most teaching occurs through direct instruction and tests are only implemented to measure progress and determine grades, however, the testing effect shows that tests can be used as a learning strategy to improve encoding and retrieval of information. This is known as "assessment as learning".

Classroom Contexts/Strategies

This section includes examples and discussion of various strategies and learning contexts that relate to encoding and retrieval. Many of the strategies include examples of both encoding and retrieval practices in overlapping or iterative processes, as in scaffolding, studying strategies or peer tutoring; these concepts are presented in some cases without referring to encoding or retrieval, specifically. The section on Storytelling addresses both processes more thoroughly and presents several separate examples. Each strategy must be considered in context, and provides unique advantages for supporting different aspects or types of learning (memorization of factual information, conceptual change, gaining procedural knowledge).

Self-explanation

Theory Self-explanation is a useful independent strategy in which students verbalize their thoughts to facilitate clearer, conscious, and more organized understanding. For instance, if a student were to tackle a math problem using the self-explaining technique, they would work through the problem explaining each step, what they would do to solve each step, and why they would do it. If they find that they are not able to explain why they did it, they might go back and look for an explanation from another source. In the same way that we are able to learn by teaching others, self-explanation works by breaking down the material to one's own level of understanding, effectively teaching oneself.

Research For further understanding, an article by Roy & Chi differentiates between high-quality self-explanations and low quality self-explanations. The former describes students who have shown a more critical understanding of the material by being able to demonstrate reflections of their learning through assumptions, comments and integrated statements. The latter describes students who simply restate what they’ve read. Being able to recognize the two is important because those who participate in high-quality self-explanations are not only able to produce better post-test results, but are also more likely to be good students as opposed to poor students (these students were tested prior and classified according to their scores). Roy & Chi also looked at another study that shows four different types of self-explanation- two that are successful and two that are unsuccessful. Principle-based explainers can connect what they learn to the principles of the topic and anticipative explainers make predictions prior to reading and connect it to relevant material from the past, successfully. Most learners in the study fall into the unsuccessful type category, which includes passive explainers and shallow explainers. They concluded that learners vary in their abilities to self-explain, and these variations can predictively estimate the quality of the results a learner produces.

Application Wylie and Chi describe different forms of self-explanation that can be categorized by placing them under one or more of the utilized methods. One of the methods used included open ended methods, the first being one in which students are asked to further connect and ensure understanding of the material by relating it to prior knowledge and explaining what they just read aloud. Another similar open ended method used computers for students to express their understanding of the material rather than vocalizing it. On the other end of the spectrum were some less open ended methods that required students to pick their explanation of why they answered incorrectly off a multiple choice list. Both extremes have advantages and disadvantages, with open ended methods being too unrestrictive yet allowing students to freely assess themselves, which can allow new and different ideas. On the other hand, menu type methods can be too restrictive, but eliminate the irrelevant or incorrect explanations students can make.

Scaffolding Instruction

Scaffolding learning is another classroom technique that is very popular with educators. It involves a step by step process in which the educator continually provides support for individual students as they progress in their understanding of the topic. The teacher works around the pace of the students to further their knowledge development. There are implications to this, which includes the lack of time and far too large classroom sizes for this to be a feasible task. With that said, given enough time and small enough classroom sizes, providing scaffolding instruction could yield extremely effective learning outcomes.

In an article by Kabat-Zinn (2015) , he discusses the downfalls of scaffolding. While scaffolding, in the moment, can be a great way to support students, it may become detrimental eventually as students may become dependent on the support they have received thus far. In other cases, scaffolding instruction does not carry the burden of leaving a sense of dependency. In a study done by Ukrainetz (2015), students who struggled with reading comprehension participated in a text comprehension program in which they were given practical and explicit strategies to improve their skills. It discusses ways in which students successfully transition from being supported by their speech language pathologists to being supported by their own knowledge.

Studying

There are many types of studying strategies that are taught to students- although oftentimes, students tend not to use strategies at all. In this chapter, different strategies will be looked at along with the population they work best with. It will analyze and study students as individual groups in relation to the study techniques they use. Motivation and social support from peers and adults including teachers, tutors and parents will also be seen as a factor in the effectiveness of various study techniques. We will look at studying in relation to individual groups rather than studying as a whole. Additionally, study techniques can be broken up and categorized according to different subjects and different forms of testing.

Peer Tutoring

Theory Peer tutoring is a method of learning in which classmates teach and learn from each other through one-on-one direct instruction. Many schools, particularly secondary schools, have implemented this strategy as whole classes. Its intentions are directed at students to be able to process material deeply enough to be able to teach it, and for tutees to be able to learn in an environment without pressure. Typically, tutors are better performing students, likely those who have previously taken the class that they are tutoring. Some of the challenges of peer tutoring, as stated in an article by Mynard & Almarzouqi include the fact that students, especially those in high school, may not necessarily get along and thus coordination of all the students becomes difficult. Additionally, there are no guarantees that tutors and tutees will consistently show up for class. There is also a fear among professional educators that students don’t possess adequate information or ability to effectively teach another one of their peers.

Research One study by Korner & Hopf looked specifically at cross-age peer tutoring in physics, in which the tutors were in grade 8 and the tutees were in grade 5. Using a pre-test post-test design, they had three main groups in which each consisted of tutors, tutees or tutors and tutees, and two mentoring groups that would guide them through the material prior to tutoring. Results saw that no matter which group was tutoring which, all groups showed positive effects on tutors, mentors and tutees, particularly when the students took part in the active role of tutoring. In their review of literature, they also consider past studies where "They emphasized positive effects concerning students’ achievements, attitudes toward the subject matter, and self-concepts not only for the tutoring students, but for the tutees as well." Peer tutoring increased a variety of interpersonal skills such as teamwork and taking on a leadership role. In the same way, another study found that peer tutoring benefitted vulnerable minority students who came from low income and/or poor socioeconomic families more so than if they were to adhere to traditional means of teaching. The difference is that peer tutors and tutees are able to form relationships that students and teachers cannot. The impact, given that the system is organized, structured and clearly understood, is most likely to be positive on both tutor and tutee's sense of academic achievement and self-efficacy.

Application Being a fairly new method of learning, peer tutoring is still somewhat in its initial stages of development. School systems vary among a variety of factors including different levels of schooling, private and public schools, different countries, and so forth. For this reason, there are a variety of ways peer tutoring can be implemented in classrooms.

An article by Ayvazo & Aljideff discusses Classwide peer tutoring (CWPT) and its structure in inner-city elementary and secondary schools. The first step in CWPT is to train the tutors. Teachers first instruct the tutors in their expertise, and the tutors are to then practice tutoring what they have learned. As this is happening, teachers will move from student to student, assessing them and providing critique to allow students to correct themselves. Next, they are given a performance record sheet to check off the skills that they have done well, and cross off the skills they need to continue to work on. Throughout this, students continue to learn lessons about interpersonal growth, such as how to appropriately receive and give feedback to their peers. Following this, students become ready to undertake their roles as they take on being both the tutee and tutor. This turn taking is advantageous because it allows the students to reap benefits from both roles, as they also learn to become better learners and teachers. It also eliminates feelings of inferiority or superiority, as all students are given the opportunity to teach each other, rather than deeming some students more qualified than others to teach.

In universities, a study by Brandt & Dimmit utilizes writing centers at school to be a setting for peer tutors who are separated by their specific studies. Tutors go through a screening process in which they must complete a number of specific, selected courses, have at least a 3.5 GPA., and fulfillment of other criteria stated by the university. As tutors, they are taught to teach by scaffolding the learners, rather than straightforward direction. They teach a student-centered approach and encourage tutors to understand why these methodologies are used. The methods used at these writing centers seemed to be well organized in terms of their hired tutors and study formats. The beliefs that the writing centers had to ensure that tutors were genuine in their use of student-centered approaches greatly facilitated the success to this program. A peer tutoring system does not simply work when it's implemented; it must be planned thoroughly and made clear to all participants what its intentions are. The effects of this peer tutoring method depended on approach and clear guidelines being followed.

It's apparent that peer tutoring techniques fare especially well in schools with at-risk students, for it allows these students to work with peers whom they most likely have more valuable and meaningful relationships with. Additionally, for antisocial students, it creates a starting point of interaction- which can oftentimes be the most difficult part of making friends. Given that an effective method of peer tutoring is used, it is unlikely that it will have a negative effect on students and likely that it will create a positive impact on students’ self-confidence, academic achievement, peer relationships, and interpersonal skills.

Note Taking, Summarizing, and Rereading

Theory Because strategies while studying are dependent on the motivation and effort of an individual, rather than their peers and teachers, they play a major role in the development and academic achievement of a student. Habits and perceptions on studying that students pick up in their younger years are likely to carry on throughout their lives. The effects of note taking can differ as it can occur during lectures or while reading. Similarly, summarizing material may have different outcomes, dependent on whether you are recalling material or directly referring to the material as you summarize. The effectiveness of these strategies, including note taking, summarizing, rereading and highlighting, depends on a number of different factors, some of which will be looked at as we analyze the literature.

Research A study by Dyer & Ryley looks at the effects of note taking, summarizing, and rereading individually and collaboratively as study strategies. Each student is given an envelope with instructions along with a passage, telling them that they are to do a random combination of taking notes, reading, summarizing, and/or doing an unrelated task. Students who were able to spend more time reviewing and studying the passage through note taking or rereading had better post-test results than those who summarized the material by recall without reference to the material. On the other hand, those who did an unrelated task after reading had the lowest performance scores. A meta-analysis by Ludas (1980) focused on the accumulated studies on note taking and the effect it has on recalling information. Previous studies have shown note taking to be either positive, or having no difference, but never negative in results. Note taking is optimal in suitable environments, such as lectures that are slower-paced, as opposed to note taking during videos. During quick-paced lectures, one might simply write exactly what they hear, rather than thinking about what they're writing. Time is also a factor when looking at the efficiency of note taking- in that 15 minutes is the proximal time for one to effectively listen and take notes that are remembered.

Application Note taking, summarizing and re-reading are strategies many students use as they are often the first things taught about studying. They are very much self-explanatory, although it is important to mention the impact that technology has on these strategies, as they can all be done on laptops, computers and tablets. All in all, it is evident that activities that allow more review of the material taught result in better sustainment of what is learned. Note taking requires rereading and comprehending text in order to understand what we are reading in our own words, thus it requires constant review. Note taking in lectures provides students material that is written to their understanding to review, given that the class provides an optimal environment for note taking.

Stories and Storytelling

Try to think of an experience in which a teacher told you a story. For me, I am thinking about my professor for my engineering thermodynamics course which deals with heat transfer. This particular professor was also an employee at a local engineering company, and he always brought stories from his workplace into the classroom. When we were learning how to use Excel to make calculations, I can remember him vividly telling us about how Excel is widely used in his engineering company. This brought meaning and relevance into what I was learning in his class. When I later went to work at an engineering company, I remembered that professor’s story when I was required to use Excel for my job. Storytelling is a commonly used learning/teaching strategy that teachers use which can enhance students' ability to encode and recall of information. Storytelling as its name implies is "the telling or writing of stories" according to its dictionary definition . A story is also defined as a narrative that can provide connection between abstract and concrete concepts.

The terms story and narrative can be used interchangeably, and a narrative is defined as "a story that is told or written" which is "a recounting of a sequence of events". Narratives generally consist of the following components: "1) a storyteller or narrator; 2) a geographic, temporal, and social context in which the story is set; 3) a set of occurrences that unfold in a specific sequence; 4) an audience with certain qualities for which the narrative must be customized; and 5) a message, intent or moral of the story, that the narrative is trying to convey". Often stories are used to pass down wisdom, illustrate a point or moral, explain a particular event, or use thinking and emotion to create mental imagery. The key components of stories often include "characters, objects, location, plot, themes, emotions, and actions". The following sections are going to discuss why storytelling is an effective learning/teaching strategy because stories are easy to comprehend and remember when compared to other learning materials.

Storytelling as an Effective Learning/Teaching Strategy

Using storytelling as a learning strategy offers many benefits for students. The use of stories has physiological impacts on the learner’s brain when compared to a presentation of information. Normally, only the language and comprehension areas of the brain are engaged during a presentation of information. However, with the use of storytelling to present knowledge additional areas of the brain including those that involve text, sensation, smell, vision of colors and shapes, and sound become engaged. This widespread brain activation allows learners to create richer memories that include images with color, three-dimensions, and emotions. In addition, when a person listens to a real-life story told by another person, physiological changes occur in the brain to increase engagement between the speaker and listener.

Additional benefits of using storytelling as a learning strategy include the following. Stories are the most powerful form of communication because they increase and maintain social capital.(p. 112) Social capital is defined as "the stock of active connections among people: the trust, mutual understanding, and shared values and behaviors that bind the members of human networks and communities and make cooperative action possible".(p. 4) In an educational context the use of stories also supports student's curiosity and imagination, stimulates interest for learning, encourages discussion, humanizes information, promotes decision making, and provides a structure for remembering course material. Storytelling also promotes knowledge transfer when used as a knowledge sharing tool. When I think back to the story of my engineering professor and his stories about using Excel in a workplace, I can see how his stories aided my knowledge transfer. To understand how storytelling enhances learning and knowledge transfer when used as a learning strategy, the next section discusses the cognitive theory related to stories and storytelling.

Cognitive Theory of Storytelling: Encoding and Recall (Retrieval)

This section will discuss how stories and storytelling relate to encoding, retrieval, and contextually rich learning.

Encoding

Wyer (2014) holds a more extreme view of the power of storytelling and claimed that all knowledge is actually encoded as stories. (p. 2) Research in narrative-based learning provides some insight into how learners encodes stories. Glaser et al. (2009) state that learners may encode various part of a narrative or story selectively. This has implications for learning in that the main facts should be presented in the parts of the story that learners are more likely to encode and remember. These parts of the story include "exposition scenes, trials of the protagonist to resolve problems, and the results". (p. 434) Exposition scenes refer to background information provided in the story. The protagonist is the leading or main character in the story. The emotional content of a story is hypothesized to allow for enhanced encoding of the story because learners pay more attention. The emotional content of the story arouses learners and is related to positive or negative emotions. An example of emotional content is illustrated in some movies or documentaries that zoom into the faces of people while adding emotional music in the background. The emotional content of stories is enhanced by engagement, discussion, authenticity due to personal relevance, empathy with characters, anticipation, and surprise that can be included in stories. (p. 23) When using narratives for communicating science to non-expert audiences, Dahlstrom (2013) states that the cognitive pathway for encoding a narrative is different from the cognitive pathway that is used to encode scientific evidence. Stories or narratives are also thought to be encoded more deeply using semantic processing because learner's find the information presented in stories to be more personally relevant. (p. 21) Stories could also be theorized to be encoded by the episodic memory system because the episodic memory is multi-modal and codes for sights, sounds, smells, etc. The episodic memory might also encode the story if the listener identifies with the protagonist in the story, so the listener would code the information as if they were experiencing the events directly.

Recall (Retrieval)

Recall can also be improved if storytelling is used for student's learning. The power of storytelling to aid learning and memory was actually first demonstrated in 1969 by two researchers named Bower and Clark at Stanford University. Bower and Clark (1969) found that when students created a meaningful story around a lists of words to remember, they recalled their words better than their peers who did not use a story to learn the words. The use of storytelling allowed the students to increase learning through thematic organization and to decrease interference between the different list of words. They also found that their immediate and long-term recall was greatly improved through the use of storytelling. An explanation for why recall is improved through the use of storytelling when compared to presenting only facts is that stories are thought to elicit emotional reactions. The best stories incorporate emotions so the learners can imagine and associate themselves with the story. Because emotions are neural activators, the learner can remember or recall the story because they have sensory associations with the story. Some memories of stories are recalled extremely vividly if the story is extremely surprising or significant. (p. 22) This type of recall is called a flashbulb memory as discussed in this Wikibook chapter.(p. 22)

Another explanation for why stories are easier recall or retrieve by learners is that the knowledge is initially encoded more thoroughly. This is because stories or narratives provide students with reasoning for why what they are learning is relevant in the "real world" which makes the information personally relevant. (p. 21) This "self-referencing" through the use of stories or narratives is facilitated by teachers who use stories with learner as the main character and offer opportunities for learners to compare their differences to the story characters by asking learners how their experiences relate to the story. (p. 22) The exercise at the beginning of this section asked you to remember a story. Most likely along with the story, you could recall the details of the content that the story was created for. Just like I remembered my engineering professor giving a story about his workplace, and then I recalled that this lesson was based on us believing that there is a large need to learn Excel in order to work at an engineering company. This story was personally relevant to me because I wanted to have the skills necessary to work at an engineering company.

Contextually Rich Learning

Understanding narratives or stories as methods for contextually rich learning helps to explain the encoding and retrieval of knowledge for storytelling. Stories or narratives are considered a method for contextually rich learning which involves some combination of "authenticity, activity, problem solving, collaboration, discussion, comparison and contrast" (p. 12). Contextually rich learning also "may help learners encode new knowledge in memory better as well as how that knowledge can be retrieved better under these conditions" (p. 12). The use of narratives for learning supports encoding and retrieval because the narratives provide a framework to help learners organize knowledge. Stories help to organize content for learners because they have "predictable plots, characters, components and resolutions" (p. 21). The authentic context that stories promote for learners allows them "to match mental models that students already have from simply interacting with the world" (p. 21) This aids the student's recall of the information presented as stories which improves their learning (p. 21).

Examples of Storytelling for Teaching and Learning

Given the benefits using stories or narratives to aid the encoding and retrieval of knowledge which leads to increased learning of material, it is beneficial to understand how storytelling can be implemented by teachers. Before technologies such as chalkboards, overhead projectors, and PowerPoint presentations, teachers “shared their knowledge through stories” (p. 1). Teachers can use stories from personal experiences, current or historical events, fiction, textbooks etc. Case studies, role play, or even having students share their own stories can also bring storytelling into classrooms. Teachers still utilize these methods of storytelling, but now there are several modern methods to incorporate storytelling into teaching and learning. Beyond films and websites that use technology to bring stories to learners, games or digital games used for learning often incorporate storytelling. Given the benefits of emotion in stories aiding learners’ ability to encode and recall of information, games also utilize this principle. According to Prensky (2001), one element of games that engage players or learners is that games utilize representation and story which gives emotion. Another modern form of storytelling is called digital storytelling . Rule (2010) provides the following definition for digital storytelling: "Digital storytelling is the modern expression of the ancient art of storytelling. Digital stories derive their power by weaving images, music, narrative and voice together, thereby giving deep dimension and vivid color to characters, situations, experiences, and insights" (p. 1).

According to the founder of the StoryCenter, digital storytelling is defined as a process for "gathering of personal stories into short little nuggets of media called digital stories" (p. 1). Digital stories are considered to be a form of multimedia because more than one medium is used to create digital stories. Digital stories generally consist of "what you hear and what you see", and "those two elements are juxtaposed to create yet a third medium". What you see may consist of video and still images, and what you hear may consist of voice-overs, sound effects and music. The creative element of digital storytelling comes when combining what you hear and what you see. Rule (2010) states:

"Deep dimension and vivid color are added to characters. situations, and experiences by the creative choices made around what images are placed next to each other, what transitions, are chosen to lead from one to the other and for how long, and what audio tracks overlap one another causing sounds to mix and blend" (p. 1). Digital stories are also generally told with first person voice using "I" to tell the story. Third person voice such as "he, she, it, and they" is generally not used . This helps to put the learner into the main character of the story.

Lambert (2010) developed these steps for creating a digital story:

1. Owning Your Insights

   • Storytellers should "find and clarify what their stories are about" (p. 14).
   • Storytellers should also clarify what the meaning of their story is

2. Owning Your Emotions

   • Storytellers should "become aware of the emotional resonance of their story (p. 17)
   • “By identifying the emotions in the story, they can then decide which emotions they would like to include in their story and how they would like to convey them to their audience." (p. 17)

3. Finding the Moment

   • Often thought as the moment of change in a person's story

4. Seeing Your Story

   • Thinking of visuals such as images to use in your digital story, special effects

5. Hearing Your Story

   • Recorded voice-over of the storyteller, tone of voice, layers of sound, music or ambient sound

6. Assembling Your Story

   • Compose script and storyboard

7. Sharing Your Story

   • audience

Simply put, digital storytelling is a form of narrative that creates short movies using simple media technology. There is not much research about the encoding and recall of digital storytelling compared spoken or written storytelling. Katuscáková (2015) did find that digital storytelling offers the same benefits for knowledge transfer when compared to traditional storytelling. Three different groups of undergraduate students were taught about a Knowledge Management topic using a PowerPoint presentation, an oral story, and the oral story used in a digital storytelling format. Using pre- and post-testing the students who were taught using a story whether it was oral or digital demonstrated the same amount of increase in knowledge transfer as compared to the group being taught with the PowerPoint presentation. Knowledge transfer relates to the student's ability to recall the knowledge contained in the digital stories. Digital storytelling also offers the opportunity for learners to put themselves in the story and emotional content is often included in digital stories. Because digital stories are a form of multimedia, the principles of multimedia design for learning materials should be considered to reduce any extraneous cognitive processing. Some of these relevant principles include redundancy which means to "not add on-screen text to narrated animation" and temporal contiguity which means to "present corresponding narration and animation at the same time".

The use of digital stories by teachers and learners can vary depending on the context. Using pre-made digital stories as a teaching/learning strategy to present content to students is being used in many areas including math, science, art, technology, and medicine. In a math and geometry classroom, teachers play short digital stories for the students to watch and learn from as part of a lesson as a teaching/learning strategy. The digital stories used in this classroom were implemented to teach a lesson on fractions . Petrucco et al. (2013) did not demonstrate an increase in math performance after using the digital stories, but they found evidence that the digital stories stimulated student’s interest in learning about fractions. What is the content of the stories in this case and how do they related to learning goals? Another example is that digital stories created by patients can be watched by nursing students to help them develop empathy for patients. Digital stories can also be created by students as a form of personal narrative to tell their own story or the digital story can be made by students to explain an event such as something historical. Students can use video editing software such as Windows Movie Maker, iMovie, or browser based applications such as WeVideo to create their digital stories.

Glossary

Confabulation

The unintentional fabrication of events displayed as real memories in one's cognition.

Distributed Practice

a certain amount of study sessions which take place regularly over time (e.g., working on improving a skill for several weeks or years).

Elaborative rehearsal

Relating the to be learned information to other information.

Encoding

the process of transferring information from short- term memory for storage in the long-term memory of the learner.

Episodic Memory

Storage and retrieval of personally dated, autobiographical experiences.

Extrinsic Motivation

A drive to complete a task based on outside factors such as prizes and rewards.

Alzheimer's Disease

A type of dementia characterized by progressive degeneration of the brain.

Implicit Memory

An automatic and unconscious way of memory retention.

Intrinsic Motivation

A drive to complete a task based on personal interest or belief.

Learning strategies

Tactics, which the learner can apply to material in order to remember it more efficiently.

Maintenance rehearsal

Information is repeatedly rehearsed in order to keep it active in short-term memory.

Massed Practice

Practice where learners make one-time intensive effort of working on a task (e.g., preparing for a test overnight).

Mnemonics

Study tactics, which aid learners in the retention and retrieval of information.

Motivation

Behaviours and thoughts that drive individuals to perform.

Prior Knowledge

The pre-existing knowledge a student possesses surrounding a particular topic.

Reconstructive Memory

The way in which the recall process reassembles information by building upon the basis of limited key details held in longterm memory with the general and domain specific knowledge in one's repertoire.

Retrieval

The process of re-accessing information once it has been encoded in the brainThe Keyword method:'

A two- stage procedure for remembering materials that have an associative component.

Scaffolding

A process first put forward by Lev Vygotsky, in which learners are supported step by step at their own pace to reach their learning goals.

Schemata

Mental frameworks that help organize knowledge.

Selective Memory

The active repression of negative memories, or the active focus on positive memories.

Self-Regulated Learning

Learning that gives the learner freedom to control their own pace.

Semantic Memory

Memory of general concepts and principles and associations among them.

Testing Effect

The influence that taking tests makes on learning and retention of information.

Zone of Proximal Development

The time in which students are most likely and able to learn material and as they move further from this time, it will become harder to learn.

Suggested Readings

The self-explanation principle in multimedia learning.

Wylie, R., & Chi, M. H. (2014). The self-explanation principle in multimedia learning. In R. E. Mayer, R. E. Mayer (Eds.) , The Cambridge handbook of multimedia learning (2nd ed.) (pp. 413-432). New York, NY, US: Cambridge University Press. doi:10.1017/CBO9781139547369.021 Siegler

The effects on students' emotional and behavioural difficulties of teacher-student interactions, students' social skills and classroom context.

Poulou, M. (2014). The effects on students' emotional and behavioural difficulties of teacher-student interactions, students' social skills and classroom context. Br Educ Res J British Educational Research Journal, Vol 40(6), 986-1004. http://dx.doi.org/10.1002/berj.3131

Mayer, R. E. (1980). Elaboration techniques that increase the meaningfulness of technical text: An experimental test of the learning strategy hypothesis. Journal Of Educational Psychology, 72(6), 770-784. doi:10.1037/0022-0663.72.6.770

K-W-L: A teaching model that develops active reading of expository text.

Ogle, D. (1986) K-W-L: A teaching model that develops active reading of expository text. Reading Teacher, 39(6), 564-570. http://dx.doi.org/10.1598/RT.39.6.11

Scaffolding Voluntary Summer Reading for Children in Grades 3 to 5: An Experimental Study.

Kim, J. S., & White, T. G. (2008). Scaffolding Voluntary Summer Reading for Children in Grades 3 to 5: An Experimental Study. Scientific Studies of Reading, Scientific Studies of Reading, 12(1), 1-23.

Memory and Retrieval

Bartlett, R.H. (1932). Remembering: A Studyin Experimental and Social Psychology. Cambridge University Press.

Irish, M., Lawlor, B. A., Coen, R. F., & O'Mara, S. M. (2011). Everyday episodic memory in amnestic mild cognitive impairment: A preliminary investigation. BMC Neuroscience, 12doi:10.1186/1471-2202-12-80

Lanciano, T., Curci, A., Mastandrea, S., & Sartori, G. (2013). Do automatic mental associations detect a flashbulb memory?. Memory, 21(4), 482-493.

Sociocognitive Learning

Social Cognitive Theory

Albert Bandura Psychologist

Albert Bandura's social cognitive theory views learning as occurring within a social context and regards humans as self-organizing, proactive, self-reflecting and self-regulating. Social cognitive theory categorizes the factors in human development as environmental, behavioral, and cognitive. It portrays development as emerging from the dynamic interplay of these three types of factors. Building on Bandura's earlier focus on observation and modeling as a source of learning, social cognitive theory describes how the belief in one's competence to succeed at a task, known as self-efficacy, strongly affects learning outcome.

Reciprocal Determinism

Reciprocal Determinism

Bandura considers his model of reciprocal determinism as a way to explain how an individual’s behavior both influences and is influenced by both personal characteristics and the social world. Bandura’s reciprocal determinism model also explains that learning is the result of interacting variables. His model involves three components, personal, behavioral, and environmental factors that interact and influence each other. These three components are considered to function as interdependent rather than autonomous determinants, thus maintaining the fact that the they are conditional of each other. Personal factors include beliefs and attitudes of the individual. To apply this to a learning environment, one would say that the personal beliefs and attitudes of the learner would affect their own learning. If they were previously rewarded for a certain behavior in a certain situation, for instance, they are more likely to repeat that scenario. The behavioral component of learning can consist of responses one makes in a given situation such as one's response to a low test score with either frustration or an increased effort. Finally, environmental factors such as roles played by parents, teachers and peers can have an effect on an individual’s behavior and self-beliefs, which consequently impact their learning. Given the importance of this three components of Bandura’s model, we focus on the personal factors such beliefs about the self, and how it can affect behaviors and the interpretation of environmental cues. The model of reciprocal determinism will thus be considered in each section of this chapter.

Self-efficacy

self efficacy factors

Since self-concept and self-efficacy, though distinct constructs, are related in their conception and in their effects on student achievement, consideration is given first to the literature on self-concept as a basis for observations on self-efficacy. Self-concept is generally viewed as an assessment of self-worth deriving from comparisons with the past performance of self and the performance of others. Self-efficacy tends to be conceptualized as a context-specific assessment of one’s competence to perform a specific task. Self-efficacy theory suggests that feelings of self-efficacy have their origins in experiences of success or failure that arise through attempts to master actual tasks. In brief, Self-efficacy is how the individual perceives ones own abilities and the level of confidence for achieving goals from the perceived abilities. There are three domains of self-efficacy that differentiates in: task difficulty, generality of one's self-efficacy (self-efficacy in one domain is not consistent with self-efficacy in another domain), strength of one's efficacy judgments. Within those three domains, there are four factors that Bandura stated to effect self-efficacy. These factors are enactive mastery, vicarious experience, verbal persuasion, and physiological and effective state. Enactive mastery is related to the knowledge that an individual has obtained from previous experience. For example, if an individual has achieved mastery in math they are more likely to have a high self-efficacy. Achieving mastery contributes to the individual’s perception of ones ability in completing a task. Vicarious experience is watching others and learning from what was watched. For example, if an individual watches a classmate or teacher demonstrate an equation on the board they may feel their ability to the problem on their own has increased. There will be more discussion related to this in the section entitled enactive and vicarious learning. Another factor is verbal persuasion which is having an individual convince another that they are capable of completing a task. Having another person or classmate tell another that they have the ability to do well on a task or encourage them, might boost their confidence and their perception of their ability on a task. The final factor, physiological state, can effect the individual’s self-efficacy. For example, if an individual is tired due to a lack of sleep, their perception of their ability to complete a math task might be low. Even though they normally have high self-efficacy in math. These four factors as well as others affect the individual’s self-efficacy. As self-efficacy is closely related to the concept of reciprocal determinism in ways that the personal, environmental, social aspects influence self-efficacy and vice versa, this part of the chapter will look closely at the different aspects and implications of self-efficacy and factors that will correlate with each other.

Agency

Agency refers to simply the capacity of a person to act in any given environment. When it comes to learning, agency and performance are closely related, since agency involves the individual's willingness to engage in academic tasks. Agency is characterized by number of core features that operate within human consciousness and influences the nature and quality of one's life and learning. Social cognitive theory distinguishes among three modes of agency: direct personal agency, proxy agency that relies on others to act on one's behest to secure desired outcomes, and collective agency which is exercised through socially coordinated and interdependent effort. As defined by Bandura, efficacy beliefs form the foundation of human agency as people need to believe that they can produce results by their own actions, individuals who have agency are intrinsically motivated to perform and may need very little or no external incentives; Bandura (2007) refers to this subjective operative capabilities. For example, a person with high self‐efficacy would be confident in his/her ability to perform a given task successfully. In order to fulfill and maintain the confidence, the person would exert greater effort in completing a difficult goal‐related tasks if he/she feels confident that the task would be successfully completed. Individuals with high self-efficacy, need to believe that challenges can be met and overcome. Self-efficacy beliefs usually affect cognitive functioning through the joint influence of motivational and information-processing operations. For example, this dual influence is illustrated in studies of different sources of variation in memory performance. The stronger people's beliefs in their memory capacities, the more effort they devote to cognitive processing of memory tasks, which, in turn, enhances their memory performances. However, efficacy in dealing with one's environment is not a fixed act or simply a matter of knowing what to do. People are neither autonomous agents nor simply mechanical conveyors of the environmental influences. Rather, they make causal contribution to their own motivation and action, which involves a generative capability in which cognitive, social, and behavioral skills must be organized into integrated action. Perceived self-efficacy helps to account for such diverse phenomena such as changes in coping behavior produced by different modes of influence. The stronger their perceived self-efficacy, the higher the goals people set for themselves and the firmer their commitment. These include the temporal extension of agency through intentions and thought, self-regulation, and self-reflection about one's capabilities, quality of abilities, and the meaning and purpose of one's life pursuits. In causal tests, the higher the level of induced self-efficacy, the higher the performance accomplishments and the lower the emotional arousal. Among the mechanisms of personal agency, none is more central or pervasive than people's beliefs about their capabilities to exercise control over events that affect their lives. Self-efficacy beliefs function as an important set of proximal determinants of human motivation, affect, and action. So far, the discussion has centered on efficacy activated processes that enable people to create beneficial environments and to exercise control over them. Judgments of personal efficacy also affect selection of environments. People tend to avoid activities and situations they believe exceed their coping capabilities, but they readily undertake challenging activities and select social environments they judge themselves capable of handling. They operate on action through motivational, cognitive, and affective intervening processes. Some of these processes, such as affective arousal and thinking patterns, are of considerable interest in their own right and not just as intervening influences of action. Those who argue that people do not exercise any control over their motivation and action usually emphasize that external events influence judgments and actions, but neglect the portion of causation showing that the environmental events are partially shaped by people's actions. In the model of reciprocal causation, people partly determine the nature of their environment and are influenced by it. Self-regulatory functions are personally constructed from varied experiences and not simply environmentally implanted. Among the mechanisms of human agency, beliefs of personal efficacy is also very pervasive and other factors serve more as guides and motivators, as they are rooted in the core belief that one has the power to produce what one desires. Do beliefs of personal efficacy contribute to human functioning? If it was otherwise people would have little incentive or motivation to act or to persevere in the face of difficulties. This core belief affect whether individuals think in self-enhancing or self-debilitating ways, how well they motivate themselves and persevere in the face of difficulties, the quality of their emotional well-being and their vulnerability to stress and depression, and the choices they make at important decisional points. The critique for this theory comes from this aspect since self-efficacy beliefs operate in concert with goal systems of self-regulation in contrast to the focus of control theory on discrepancy reduction. As evaluated by 9 meta-analyses for the effect sizes of self-efficacy beliefs and by the vast body of research on goal setting, contradicts findings that belief in one’s capabilities and personal goals is self-debilitating.

Outcome Expectation

Studies of the relationship between self-beliefs and performance tend to draw on this or related theories and usually endorse the notion of reciprocal determinism at a theoretical level which can also set the basis for self-efficacy level. However, attempts to model this mutual influence of self-beliefs and performance are few and are focused on the relationship between self-concept and performance. Comparisons are made between those who overestimate how well they will perform (over-estimators), those who underestimate their level of performance (under-estimators) and those who have an accurate perception of their performance level (accurate estimators) to determine how the three groups differ. If differences exist then recommendations can be made to improve the accuracy of self-estimates, and thereby improve the efficacy of such measures. A key consideration is what differentiates those that are able to accurately self-assess from those that produce erroneous self-assessments. Feedback is also a very important factor in building outcome expectation and self-efficacy. Athanasou (2005) identified three key sources of feedback used by people in deriving self-estimates: social messages, personal factors and situational factors. Social messages were sources of information derived from interaction with others. Three types of social messages influenced self-evaluation: comparisons we make of ourselves with others, feedback we receive from others, and the social and cultural stereotypes. Results from the above study indicated four main areas of feedback sources, and a positive relationship between ability and accuracy of self-estimates. Learning goal orientation and use of feedback were positively related; however their effects on accuracy of self-assessment were contrary to those hypothesized. Analyses indicated a positive relationship between ability and accuracy of self-assessments. However, over-estimators recorded higher levels of confidence, learning goal orientation and usefulness of feedback than the other groups.Most studies report the relationship between estimates of ability and actual ability to be only moderate. Thus the reciprocal determinism of self-efficacy and performance seems to be without direct empirical support, probably because the longitudinal, repeated-measures data often considered necessary for this purpose are not available. It is possible, though, to model reciprocal effects with cross-sectional data. In the analyses reported in an article, the authors achieved this using a structural model in which the mutual influence of self-efficacy and performance in mathematics is represented as a feedback loop. This model was estimated in each of 33 nations on the basis of data on the mathematics self-efficacy and mathematics achievement of 15-year-olds. First, the reciprocal determinism of mathematics self-efficacy and achievement was supported in 26 of the 30 nations, providing empirical support for this proposition as an explanation for the observed relationship between mathematics self-efficacy and achievement. The model was a good fit to the data in 30 nations and was supportive of reciprocal determinism in 24 of these, suggesting a fundamental psychological process that transcends national and cultural boundaries. Such evidence can suggest the link between culture which is an example of environmental factors correlated to self-efficacy and performance. Taken together, these findings provide persuasive support for Bandura's contention that self-beliefs and performance iteratively modify each other until the individual comes to a realistic appraisal of his or her self-worth or competence relative to the (mathematics) tasks at hand.

Goal Orientation

According to Locke and Latham (2002), ‘A goal is the object or aim of an action, for example, to attain a specific standard of proficiency, usually within a specified time limit'. Elliot (1997) sees goals as cognitive representations that guide individual behaviour by focusing on specific outcomes. These definitions have a common thread that they suggest goal‐setting is based on purposeful conscious human behavior. Thus, a goal is that which an individual hopes to reach or attain through purposeful behavior. Goal orientation refers to the mental framework that influences how people approach situations of achievement in terms of interpreting the situation and motivation to achieve. Past research suggests that goal orientation may be treated as either an individual trait or a situational characteristic. Button, Mathieu and Zajac (1996) claimed that goal orientation has both the dispositional and situational components. College students who hold a strong learning goal orientation are more likely to pursue challenging activities and to exert greater effort when presented with a difficult class, topic, or activity. this mastery pattern is adaptive in an academic setting and leads to a higher level of achievement. There are two types of goal orientation: performance orientation, where the aim of completing a task is to gain favorable judgments of one’s performance; and learning orientation, where the aim is to gain knowledge. Theoretically these orientations produce different behaviors. Individuals with a performance orientation are more likely to avoid challenges and pressure because that might increase the likelihood of failure and consequently be judged negatively by others. For people with performance orientation, their aim is on the performance and external reinforcement components such as positive feedback and judgment on their work or grades in school and taking risks that will result in negative feedback or bad grades lower their motivation to challenge tasks. In contrast individuals with a learning orientation seek out challenges and maintain their motivation even under difficult conditions, for them, failure is also a form of useful feedback. For learners with learning orientation, the process itself is also reward for learning and the result of succeeding or not does not effect them very much because they are more focused on gaining the knowledge which ironically often results in good external feedback and results as well. Button et al., (1996) concluded from their investigations that learning and performance goal orientations were not mutually exclusive, each goal orientation represent a different end of a continuum. Self-efficacious students are better goal setters, because of their willingness to set “close” rather than “distant” goals and the ability to set one's own goals; also it has been shown that these students have an enhance self-efficacy. This also implies that student‐initiated goals and related achievement can be important to the subsequent establishment of challenging goals being applied to complex situations. In other words, perceptions of higher levels of control and goal commitment (self‐efficacy beliefs and a willingness to engage in important goal tasks) influence an individual’s willingness to set difficult goals[11].

Task Engagement

Self-efficacy is linked with the initial task engagement, persistence of task engagement, and successful performance. In self-efficacy, first setting the goal from the level of self-perceived performance expectation leads to how the student will approach and engage in a task. There seems to be two aspects to task engagement: the first is the willingness or the level of motivation to engage in a given tasks and the second aspect would be the actual attitude and behavior of engaging in the certain tasks. One’s ability and willingness to establish challenging yet achievable goals is necessary to evaluate options, make decisions, plan and achieve meaningful accomplishments. A willingness to take on important goal‐related tasks and have positive self‐efficacy beliefs were associated with those who reported a readiness to set difficult goals. This suggests that an individual, who experiences a general sense of autonomy, may likely extend this perspective to specific situations. Inversely, an individual who experiences a low general sense of autonomy may perceive less autonomy in specific situations. A sense of having autonomy, for example, through the opportunity to choose, is related to confidence in one’s ability to complete a task successfully. Individuals, who perceive a margin of control in their lives, might take on difficult goal‐related tasks, since they likely feel confident in affecting outcomes. An individual’s sense of having some control in life as supported by choice is positively related to a sense of self‐efficacy and a willingness to engage in important goal tasks. By its very nature, goal‐setting invokes task effort that may include planning in order to increase the probability of success. Goal‐setting is thus a key component in self‐regulation (Locke & Latham, 2002) and can facilitate learning. Results suggest that before males engage in challenging goal attainment they must perceive themselves as self‐efficacious, whereas females are inspired by tasks that are important to them. If the tasks are important, so are the goals, regardless of their difficult nature. One’s ability and willingness to establish challenging yet achievable goals is necessary to evaluate options, make decisions, plan and achieve meaningful accomplishments. For example, in two studies, one with undergraduate university students and the other with high school students, Sideridis (2001) found the important task of maintaining a high GPA contributed to normative beliefs in the goal, importance of effort, intention to achieve the goal and positive study behaviors such as organizing and planning, which resulted in satisfaction over the long term. These studies suggest the saliency of goal‐setting and self‐efficacy in academic achievement. They also imply that student‐initiated goals and related achievement can be important to the subsequent establishment of challenging goals being applied to complex situations. The literature indicates that an individual’s sense of having some control in life as supported by choice is positively related to a sense of self‐efficacy and a willingness to engage in important goal tasks.

Persistence

Persistence is defined as the act of perseverance in spite of obstacles and frustrations. Although the persistence of an individual can be respective to a variety of factors, it is found that the level of self-efficacy in an individual amounts to the extent of persistence in an individual. As self-efficacy refers to the degree of confidence of one’s ability to succeed at a task, the strength of one’s perceived efficacy accompanied by motivation highly corresponds to the extent to which they persist in a given task. In an observational study made by Hackett and Betz (1981), it was hypothesized that efficacy expectations are associated to the degree of persistence that lead to success in an educational setting. Their study ultimately found that both level and strength of self-efficacy for educational requirements were generally related to persistence and successful academic outcome in students . Motivation is another determining factor that contributes to an individual’s persistence. A logistic regression analyses and general linear modelling approach was applied to predicting persistence and academic success in students. In both cases of academic motivation on persistence and academic success, it was proven that amotivation was the single significant motivational predictor in the final models . These results are associated with the level of self-efficacy of the participants as the level of their motivation also seems to branch from the level of their self-efficacy.

Case Study: In another study done by Taylor and Betz (1983), self-efficacy was measured in relation to the tasks required in career decision making. This study was aimed to investigate the theory of self-efficacy beliefs tied with academic success and persistence in students who were considering careers in the science and engineering field. It was discovered that college students’ efficacy expectations were dependent on the degree of their career indecision; students who were indecisive about their career path were less confident in their ability to complete the tasks required to make career decisions, and those who had decided on their career path experienced the reverse. The expectations of self-efficacy in completing their education for their specific technical/scientific careers were acquired at the beginning, at the end, and two months following a ten week academic course in career planning. The strengths of individual self-efficacy was then assessed by having students give an estimate of their level of confidence in ability to complete these requirements and duties for career performance. Other correlations that were used to measure the relationship between self-efficacy and academic success included the individual’s Math PSAT scores and high school rank and it was found that self-efficacy for technical/scientific educational requirements appeared to be related to objective measures of mathematical aptitude and high school academic achievement. According to Bandura, performance accomplishments are hypothesized to be an influential factor in self-efficacy; based on this notion, the subjects’ knowledge of their previous academic performance and aptitude test scores may have had a part in determining their efficacy expectations . On the other hand, the relationship between measured and perceived ability did not correlate, which in turn suggests that the appeal of studying both efficacy expectation and objective ability as they can contribute to the understanding of career-relevant outcomes. Further work can be done in determining a causal connection between self-efficacy and particular academic behaviors with factors such as objective ability and incentive for performance can be considered in this context.

As much of previous studies on self-efficacy were based on the examination of targets problems, such as phobias, and performance criteria, like behavioral avoidance tests, this particular investigation looked at self-efficacy in various different levels and sets of academic behaviors. The expectations were not confined to an educational setting, but branched out into the consideration of occupational fields titles. The fact that significant relations were found with such variable and nonspecific factors suggests that “self-efficacy may be a relatively robust and flexible model that may help to explain complex as well as relatively discrete behaviors” . Overall, this study resulted in the confirmation of the strength of efficacy expectations in relation to persistence and success in major choice.

Strategy use

Strategy use is a significant factor in determining the level of self-efficacy in individuals and vice versa. The use of strategy enables students to regulate their behavior and be in control of their learning environment, thus putting a significance on self-regulation in establishing a connection to successful uses of strategy with positive outcomes. Furthermore, the different strategies used by an individual is strongly dependent on their perception of academic efficacy as well as some factors of reciprocal feedback through teachers. According to Zimmerman, students use strategies to regulate three foundational aspects for learning: their personal functioning, academic behavioral performance, and their learning environments . Personal regulation are strategies such as organization, rehearsal, memorizing, goal setting and planning; strategies that are geared towards enhancing behavioral functioning are things such as self-evaluation and self-consequating; and finally, strategies that include students to seek information, keep records and seeking assistance can improve students’ immediate learning environment. For those students who are successful in self-regulation seem to have a general understanding of the environment on themselves and hold the ability to improve that environment through the use of strategy. To better understand students’ use of these self-regulated learning strategies and the factors that affect motivation for strategy use, we can take a look at Zimmerman and Martinez-Pons’ study conducted in 1986. This study was aimed at measuring students’ self-regulated learning strategies through the Self-Regulated Learning Interview Schedule (SLRIS). The results found that the measures of strategy use were highly correlated with students’ academic achievement ; additionally, perceptions of self-efficacy also acted as a determinant of strategy use.

Case Study: The SLRIS that Zimmerman and Martinez-Pon used in their study measured strategy use by asking students to report the methods they used in various learning contexts. Two multiple regression analyses were conducted in order to determine students’ perception of academic efficacy in relation to self-regulated learning strategies. These learning strategies were then used to predict both verbal and mathematical efficacy, where verbal self-efficacy was related to the individual’s use of strategies such as organization, reviewing notes and seeking peer assistance and mathematical self-efficacy had similar results, with the exception of seeking adult assistance which was negatively correlated. Final results on the strategy use of students indicate that “the achievement of these students in school indicates that a triadic model of self-regulation may have merit for training students to become more effective learners” .

In providing individuals with the necessary tools for efficient strategy use, Zimmerman proposes an academic self-regulation model called the SRL model. The theory behind this model outlines how teachers can aid students in becoming more engaged in their learning and lead to improvement in academic performance. The SRL model makes use of an feedback cycle consisting of three phases: planning, practice, and evaluation. In the planning phase, students will have a chance to carefully assess their academic environment and pick a strategy that can most efficiently address their learning goals. During the practice phase, students can implement their chosen strategy and make ongoing adjustments to the plan as they go, also giving them the opportunity to self-monitor their progress. Finally, in the evaluation phase, students can evaluate the effective of each strategy that was used to help obtain their learning goals. This model can be useful in to providing individuals with the necessary techniques to regulate their academic behaviors and control their learning environment.

Effort

Self-regulation strategies alongside self-efficacy successively help maintain the level of effort put forth by an individual. Volition is represented in effort regulation which describes one’s willingness towards a given task. Zimmerman and Martinez-Pons (1990) reported that individuals who demonstrate the successful use of self-regulation strategies and hold a high degree of self-efficacy were likely to succeed academically; this demonstrates that self-efficacy helps maintain volition and those who are successful in doing so consequently appear to promote the use of self-regulation strategies . Zimmerman’s Model of Self-Regulatory Process explains that learners regulate and maintain their concentration, attention and motivation so that they can learn efficiently and achieve their determined goal . Based on this, there exists a three stage model of self-regulation that includes three cyclical phases involved in the self-relation process: a forethought phase, a volitional or performance control phase, and a self-reflection phase. When a student is engaged in a task, their learning behavior is supported by volitional/performance control. They then regulate themselves by strategies such as maintaining concentration, attention and motivation. The last stage to this model is the reflection on learning outcomes. This reflection helps individuals in maintaining the motivation needed to maintain and improve on their performance for future academic success.

Throughout the three stages mentioned, the phase of volition and performance control is a significant factor in looking at effort. When individuals set an initial learning goal in the stage of forethought they are then needed to regulate themselves and use strategies that can allow them to reach their goal. One of the learning strategies used includes effort regulation which is then represented through volition. Furthermore, as motivation is associated with effort and volition, it can then be seen as an essential construct of self-efficacy which ultimately fosters effort regulation. Zimmerman suggests that it is crucial for educators to understand the importance of learners developing self-efficacy because this can positively affect effort regulation strategy use; in order to promote self-efficacy teachers can help learners experience personal mastery experiences such as observing peers, repeated successful experiences and positive feedback that will allow them to improve their effort regulation strategies as manifested by volition . In addition to these ideas, Onoda’s results of examining the relationship between self-efficacy and effort regulation strategy use determined that self-efficacy indeed significantly influenced effort regulation strategy use . Through a series of questions based on the Motivated Strategies for Learning Questionnaire created by Pintrich, Smith, Garcia, & McKeachie (1993), it was discovered that self-efficacy developed through previous learning experiences was a determining factor in employing effort regulation as well as their ability to control their learning behavior for successful learning.

Enactive and Vicarious learning

Enactive and vicarious learning represent two different ways of acquiring knowledge . Enactive learning occurs when one learn something by doing it; and vicarious learning refers to the learning that occurs when one observes others perform a task. Enactive learning, because it involves active engagement on a task, may appear to be most important because students can learn the steps to perform a task successfully; however it can also lead to a trial and error cycle if the student do not possess the knowledge required to perform the task. On the other hand, vicarious learning might seem more time effective because one does not actively perform the task and therefore there is no risk for errors, but at the same time it requires students to use more cognitive abilities such as focusing attention on the model that is being observed, and retaining the information intended to be learned. In spite of these differences, much of the learning happens enactively and vicariously; in mathematics for example, students first need to learn the theoretical knowledge of how to solve a problem before they attend to do it. In fact when both types of knowledge are used, the chances for errors is significantly reduced.

When discussing vicarious learning it is important to distinguish between learning and performance. Although learning might occur by observing a model, performance on a task might depend in several other factors such as motivation, interest, confidence, and several other factors. Self-efficacy might also play an important role in performance of a task that was previously learned by observation. As previously mentioned, self–efficacy is a judgement of one’s ability to perform a task in a specific domain. A student who has high levels of self-efficacy, is more likely to perform a task that was learned vicariously. One important question to ask is whether observational learning can improve the self-efficacy of students. Braaksma. M. H and his colleagues claim that indeed the relation between observational learning and self-efficacy can be influenced by the perceive similarities between a student and the model; this means that students who can identify with a model are more likely to learn from observation and increase their self-efficacy.

Because self-efficacy is domain-specific, Braaksma. M. H and his colleagues (2002) examined whether if students could learn more efficiently when observing a model that has more share similarities to them compared to models that are more different. The study involved a written task where participants observed peer models write argumentative texts. The authors separated the participants into three conditions: participants who observed a competent model, those who observed a non-competitive model, and a control group where participants just did the written task without observing any model. Results from this study show that students who were weak at writing benefit more from observing the writing of non-competent models, and strong students benefit more from observing competent models. The results from this study show that perceived model identification is important. The author offer several reasons for this results, perhaps the results can be explained better by individual's need for social comparison and identification. It might be the case that participants who were stronger writers identify more with competent writers since both have more things in common, such as writing style and error recognition.

Another interesting finding from this study is that participants who were considered strong writers benefit from both observation and performance of the written task. According to the authors, strong writers possessed previous information about writing and are probably able to divide their attention between learning and performing. In contrast weak writers, since they might not possess enough information about the task, were unable to do this. Hoover, J. D., Giambatista, R. C., & Belkin, L. Y. (2012) offer some further support for this finding. In their study participants were divided into two conditions: observation-performance, and performance only. The task in this study was a more complicated one compared to the study previously described; it involved negotiation between a buyer and a seller. Participants in the observation-performance condition were able to solve the negotiation problem more effectively than the performance alone condition. Together these findings point out that Vicarious or observation learning can increase performance and consequently raise the self-efficacy of students.

The results from both of these studies described above may have important implications for learning. On the one hand, Braaksma, M. H., et al study (2002) . show the importance of share similarities between models and students. In classrooms, teachers might enhance the learning of their students by asking a student to perform a task infront of his other peers. In math learning for example, a teacher may ask someone who seem to understand the procedures of solving a specific problem to come to the blackboard and solve the problem so everyone could see. By observing peers solving a math problem, students might feel more identified with the model since both share similar characteristics such as level of intelligence, student roles, and even physical characteristics. On the other hand, Hoover, J. D., et al (2012) study show that learning can be enhanced when observation and performance are combined. in classrooms, teachers might ask volunteers to try to solve a similar problem after observing the performance of other students. Observation, can also be important in the classroom because students might also get motivated to try to solve a task after observing one of their peers performance.

Modelling

The results from the studies described above suggest that modeling plays an essential role in learning; in a classroom for instance, students can learn from the performance of teachers and peers on a math problems; However not all models are the same; In Braaksma. M. .H; et al (2002) study, Strong writers benefit more from observing competent models and weaker writer from observing non-competent models. These results suggest that observational learning might depend somehow on specific characteristics of the model. These results also suggest that similarities between learners and models can be essential for learning. For instance in schools, students might learn more effectively from the performance of peers on a math problem. As it was mentioned in the previous section,there are several explanations for the fact that students are more likely to learn from other students compared to less similar models such as teachers or older peers; one reason is identification; students recognize and identify with the characteristics they share with a peer model. Another reason is social comparison where students compare themselves to peer models; and a final reason might be related toSelf-evaluation, that is when students use others as a standard to evaluate themselves. Similarly modelling also serve different functions; according to Bandura (as cited by Schunk, H, D; 2012) there are three main functions of modeling: to facilitate responses, disinhibit student's responses, and provide observational learning. In a classroom students might feel more motivated to participate in a discussion when they see other peers doing the same, and might feel more confident to do so.

Another function of modelling is that it provides the necessary strategies that enhance learning such as active engagement and participation . Improving Classroom Learning by simultaneously Observing Human Tutoring Videos while Problem Solving might be more effective than either watching a video or solving a problem alone ; furthermore it is important to encourage students to ask questions, discuss, and use examples to self-explain the material, in oth words it is important o actively involve students in their learning. Craig et al (2009) emphasize the importance of active observation in learning. Active observation refers to observing that facilitates engagement with the material so as to facilitate deeper processing (Chi et al; 2008 as cited in Craig et al; 2009) . In a study intended to explore the impact of collaboration on learning, Participants were divided into two conditions; the collaborative observing tutoring where students watch a video of a tutor teaching a student how to solve a problem, and a worked example where students watch a video of a tutor giving and performing the instructions of how to solve a problem. Participants were given a physics problem to solve right after they watch the videos and again 26 days after they watch the video. The results show no difference in score in the immediate post-test, but students in the collaborative observing tutoring score higher when the task was applied 26 days later. These results suggest that modelling provides essential strategies for effective learning such scaffolding and explanations in order to promote long-term retention of knowledge.

Results of this studies can be easily applied to classrooms. As mentioned in the previous section, teachers can not only enhanced immediate learning by assigning a student to demonstrate how a problem is solved in front of the classroom, but also encourage retention of knowledge. Given that perceived similarities depend on specific characteristics of a model, students might be more complying to look at other students as an extension of their own capabilities. When a student is more skillful at solving a particular problem than another, perceived similarities may play an important role since, a less skill individual might feel more motivated to perform at the same level as the highly-skilled peer. In contrast when a model is perceived to be less similar, such as teaches or older peers, the student's motivation to achieve at the same level might suffer Braaksma, M. H., Rijlaarsdam, G., & van den Bergh, H. (2002).

Teacher efficacy

In classrooms, teachers and students are equally affected by beliefs about their own abilities to perform a task. In the case of teachers, the beliefs are about their own capability to teach Teacher efficacy can be influenced by several factors such as classroom experiences, relation with colleagues, and even school settings. Knoblauch, D., & Chase, M. A. (2015) show that teachers have lower sense of efficacy in urban areas, this was perhaps because of the challenges that urban teaching represent. Teacher efficacy has a great impact on student’s learning. Teacher efficacy is associated with effective classroom management, efficient teaching methods, and greater student’s achievements. Teachers with high self-efficacy can influence student’s performance in several ways; they can encourage mastery experiences, provide verbal persuasion, and give informational feedback (Holzberger, D., et al 2015) In summary, at schools, teachers with high self-efficacy can be fabulous models for students since they can not only raise their academic success but also enhance their learning by providing effective instructions.

In one longitudinal study conducted by Holzberger, D., et al (2015) intended to explore the relation between teacher efficacy and the quality of instructions, students and teachers complete some test intended to measure teacher efficacy (social interaction with kids, and coping with job stress) and quality of instructions (cognitive activation, and mastery experiences). The tests were applied at the end of grade 9 and then again at the end of grade ten in order to measure changes in teacher efficacy or quality of instructions. Results show that scores in teacher efficacy measures change over the course of a year, it either improve or decrease depending on external variables such as student’s achievement and curriculum changes. Regarding quality of instructions, scores did not change between time 1 and time 2 suggesting that teacher efficacy and instructional quality are independent of each other and might be explained by other variables such as motivation to keep their jobs. It is important to notice that these results do not imply that teacher efficacy is irrelevant to learning. Even though this study might not show a relation between teacher efficacy and instructional quality, teacher efficacy is associated with other strategies that can enhance learning such as verbal persuasion and provision of feedback Schunk, H, D; 2012) .

Another interesting feature that characterized teachers with high levels of self efficacy is related to agency. As previously mentioned, agency is the willingness of a person to act in any given environment. Because at schools, often, there are situations that teachers can control such as classroom management, and situations that teachers cannot control like curriculum demands, teacher efficacy involve the ability to act on those features that can be control. At the beginning of this section, it was established that teacher efficacy is related to effective classroom management, and efficient study methods, these are features that are under the control of teachers. Teachers with high levels of efficacy focus on the things they can control while being aware of the situations that are out of their control (the figure shown below state some other situations that teachers can an cannot control).

According to Bandura (as cited in Woolfolk, A. E., & Hoy, W. K. 1990) the motivation of teachers to manage the classroom and use efficient teaching strategies depend on two factors: outcome expectation and efficacy factors. Efficacy factors refer to individual beliefs that one is capable to perform effectively on a task; in contrast, Outcome expectation refers to individual's judgement about the likelihood that a positive or negative outcome might happen. Teacher efficacy is a combination of these two factors, for instance a teacher who believe that she can greatly impact the learning of her students (personal efficacy), is more likely to believe that her effort s will result in a positive outcome (outcome expectation).

In a study, Woolfolk, A. E., & Hoy, W. K. (1990) intended to explore the relation between personal efficacy, outcome expectation, and classroom management. Participants in the study did a bunch of questionnaires intended to measure personal efficacy, teachers' outcome expectation, and strategies for classroom management. The results show a complex relation between these variables; overall, the results show that teachers who have higher personal efficacy tend to have positive views about outcomes and therefore use more humanistic strategies such as cooperative interactions and direct experiences. In contrast, teachers with a lower sense of personal efficacy tend to hold negative predictions about outcomes, and use more rigid and highly control environments in order to manage the classroom. Similarly, teachers with high personal efficacy have more positive views about teaching than teachers with lower efficacy, and therefore spend more effort to encourage intrinsic motivation on their students whereas teachers with lower efficacy tend to use rigid control strategies to elicit specific behaviors on their students The results from this study clearly show that teacher efficacy is a complex construct that involve a combination of personal efficacy and the general beliefs about teaching. these results can serve to explain the findings from Holzberger, D., et al (2015) study. The fact instructional quality can remains the same overtime regardless of teachers' level of efficacy can result from a change in individuals beliefs about teaching but not in the beliefs about personal efficacy. teachers may still belief that they are capable of teaching because of the extrinsic rewards and therefore adopt more controlling strategies; but on the other hand, their intrinsic motivation to teach might be affected.

Collective Efficacy

So far we have discussed self-efficacy, enactive and vicarious learning, teacher-efficacy and how they are related to the reciprocal determinism. This part of the chapter is going to explore the concept of group efficacy. First there is a distinction that needs to be made between collective efficacy and group efficacy. Collective efficacy is each individual group member's perception of how well the group will do on the task. Thus each group member could have a different collective efficacy based on their perception of the groups ability. Whereas group efficacy is the whole group's perception of how well the group will do on the task. This would include each group member holding the same efficacy This difference is small but is important when interpreting data results. The following discussion will look at collective efficacy, performance goals, group performance, group cohesion, social lofting and school efficacy.

Bandura argued that collective efficacy is related to self-efficacy. He suggested that the four factors that influence self-efficacy also influence collective efficacy. These factors are enactive mastery, vicarious experience, verbal persuasion, and physiological and effective state. He also emphasized social comparison, social influences, mix of knowledge, and past group performance which influence more specifically collective efficacy. Making references to reciprocal determinism these factors each fall under either personal, behavioural or environmental . Enactive mastery and mix of knowledge are personal factors. They are both related to knowledge that the individual already has which contributes to their feeling of being competent to complete a group task. Vicarious and social comparison are related to modelling which was discussed earlier. These behavioural factors influence collective efficacy. Verbal persuasion, physiological and affect states, and social influences are all related to environmental factors. Socially, these affect how the individual perceives his/her capability to complete a group task. Each of these factors contribute to collective efficacy. Each of these factors interact with one another and together affect collective and group efficacy.

Group Performance/ Performance Goals

Collective efficacy, group performance and performance goals are important aspects to examine. Collectively, research has shown that collective efficacy is related to group performance A higher sense of collective efficacy produces better performance on the task. Those students who perform well on group tasks often have higher collective efficacy than those who do not . For example, if a group is given the task to create a board game, and they have a high collective efficacy they are more likely to perform well. If the performance was done well it would be reflected in the grade or assessment that took place after the project. A way to improve collective efficacy and performance is through setting goals. In addition to group performance, the goals that a group sets are important, too. Research shows that when there are specific goals; overall performance and efficacy is higher than when there are no goals or they are non specific . For example, a teacher might divide the students up into teams and get them to build the highest tower. Here the teacher has set a specific goal which is to build the highest tower. Since students are given a specific goal they should perform well overall than if they were given the instruction to “do your best” when building a tower. As well as making the goals specific it is also important to make them challenging. However, making them too difficult and too easy was negatively correlated with group performance Thus, teachers need to take into consideration of the level of the students and their capabilities when setting group goals. For example, giving kindergarteners the task of designing a science experiment is too difficult for them, but giving the same task to fourth graders would be more appropriate. Once group goals are set, the group needs to make a commitment to these goals. Research shows that if a group has high efficacy they are more likely to commit to their goals . This makes sense considering that if the students perceive that the task needs to be attended to, has specific goals, and feels that they are capable of completing the project, they are more likely to be committed to the project. Higher commitment is also shown to correlate with persisting when difficulties arise in the project. Further discussion on persistence was discussed earlier in the chapter. Students need specific, challenging goals, and to make a commitment to these goals in order to achieve high collective efficacy and high group performance.

Another aspect is whether a task requires high interdependence or low interdependence. If a task has high interdependence, the group members are more likely to rely on one another and develop a higher group efficacy An example of this would be a group project that consists of performing a skit. The members have to rely one on another to perform the skit and all members have to be present when performing the skit. Whereas, a group that has low interdependence are more likely to not rely on group members and will have a lower group efficacy. An example of a group project would be the creation of this Wiki book. Although each of us are in a group and each group is creating a chapter we must likely divide the chapters up. This allows for each member to do their own part and not have to rely on other group members. In addition, at the end of the project we are getting marked individually. This project overall promote lower group efficacy.

Group Cohesion

Another way to increase collective efficacy is making sure the group has cohesion.Group’s cohesion,is defined as an attraction to group members and each group member wants to work with the others. It can also be defined as group members who are interested in the same subject or have a collective mind. Higher group cohesiveness is an important predictor of group performance . Thus the more cohesive the group, the better they will perform, and the higher the collective efficacy they will have. In order to achieve group cohesion a teacher should allow students to pick their groups. This would address the aspect of each group member wanting to work together. However, it should be emphasized the group’s goals and the expectation of the group this will promote commitment and collective efficacy. In addition, one of the downfalls of group work is that the students get off task. A study that observed high school adolescents found that they were able to complete group work while staying on task, whereas elementary school children were more likely to become off task. This could be due to the seating arrangements. In elementary school they are more likely to sit in groups, and have a lot of opportunity to interact with each other in informal situations thus making it easier for them to go off topic. Whereas high school students are more likely to sit in rows or individually so when they were put into groups they were only in groups to complete a task. This association with being in a group and completing a task makes it more likely they will stay on task. Further, research has shown that it takes up to seven weeks to fully develop group cohesiveness. These seven weeks allow the group time to work together, and develop their collective efficacy . If the group sees that they are able to perform well on previous tasks, this will increase their collective efficacy. Thus it is important for teachers to let the child work with the same group for longer periods of time. However, there is research that contradicts this assumption. Research conducted by Goncalo, Polman, and Maslach shows that having a high sense of collective efficacy right at the beginning of a project can be detrimental to the group’s overall performance. Having a high sense of efficacy can reduce the beneficial forms of conflict that is essential to group work. Even though previous research has suggested that it takes seven weeks for a group to develop collective efficacy some groups may develop it early. In addition, one group may develop high group efficacy from working with each other previously. It is uncertain if a group who has worked together previously and has a high group efficacy, will miss out on the beneficial forms of conflict. Beneficial forms of conflict include disagreeing on how to carry out the project, and reconstructing the information through discussion, evaluation, and consensus. For example, take this Wikibook project, if I had worked with my group members previously and we received a favourable performance outcome and had developed a high collective efficacy we might have gone about the project in a different way. At the beginning of the project we might not have changed our outline because in the past we had done well. As well, when we were in the final stages of editing we might not have put in as much time and effort because in the previous task we had done well. Our group discussed ways to improve our project, which included using more examples, adding pictures, and how to make the project flow better. Once again we might not have talked about it at such length if we had already established high collective efficacy. In conclusion, it may not be as beneficial as once thought for students to work together on multiple projects; there needs to be more research to further support this assumption. Another important note to be made is that self-efficacy is normally discussed as being domain specific, as was mentioned earlier in this chapter. This can also be used in relation to collective efficacy. Children should be placed in different groups for different subjects. To further illustrate this point, a baseball team might have high collective efficacy while playing baseball but they may not have a high sense of collective efficacy in completing a science experiment. Thus, they should be put in another group when performing different tasks of task to allow each member the opportunity to achieve collective efficacy. Some groups can be picked based on who the children want to be with and other groups could be picked based on interest. A group’s cohesion, is related to the environmental aspect of the reciprocal determinism. The other group members are the environmental aspect that influences group cohesion and collective efficacy. Another aspect of the environmental reciprocal determinism, is the size of the group.

The size of groups affects group performance and group efficacy. A research study showed that groups of three had higher group efficacy than those in groups of seven . In addition, group members in smaller groups are more likely to stay on topic and complete the task.The article suggested that the lack of group efficacy was due to the difficulty communicating within larger groups and multiple personal interest took over group goals. However, it was mentioned that the key to group size depends on the type of task at hand. Even though the article suggested that groups of three are good for multi motive tasks other tasks might produce higher group efficacy in larger groups. This would explain why sports teams work together well even though they consist of a larger group of people. Thus, if you are getting students in the class to build a tower this is best done in small groups to reduce the lack of communication. However, activities such as trivia would be better suited to larger groups. This is because each child in a group will have different knowledge which will lead to better performance and higher group efficacy. Cohesion and group size are important aspects of a groups performance outcomes and efficacy.

Social Loafing

Social Loafing

The discussion so far has been about how to improve collective efficacy through specific and difficult goals, making groups interdependent, group cohesion, and adequate number of group members. One pitfall of group work is 'social loafing 'which is an environmental factor. This occurs when a group member or members do not pull their weight in a group project . Ideally teachers would like to think that when they put students in groups that each one will contribute equally to the project. However, this is not the case as many students have experienced. There is always the one person in the group who never pulls their weight which has negative consequences for the other group members. Research has shown that when there is a group member that is not pulling their weight other group members put less effort into the project. This leads to a lower group performance and collective efficacy . A way teachers can avoid this is to make specific and challenging goals, promote each group’s interdependence, group cohesion and use adequate number of students in each group. In addition to making sure that there is an evaluation at the end of the project that includes what contribution each person made to the project. This evaluation is best done with the other group members not present in order to make each member feel more comfortable about saying what each member truly contributed to the project. This type of evaluation will lead to the social loafer getting the grade he or she should receive for their contribution. This should also help with the other members still putting in adequate effort despite having a member who is a social loafer.

School efficacy

We have addressed three different efficacy’s in this chapter. Although they each have their own defining characteristics they are also similar. School efficacy is the belief of the school that the students can perform well, and this includes the students and the teachers. Research has found that if a school collectively feels incapable of improving the learning of the students both the students and the teachers efficacy decreased. In context, students who have high self-efficacy because they have done academically well before is related positively to school efficacy. Some factors that contributed specifically to school efficacy are the SES status of the students and the stability of the students. Students who come from low SES status and do not show up to class often affects the school efficacy negatively . In order to promote a higher school efficacy both the students and the teachers efficacy need to improve. There are suggestions as to how to improve efficacy in previous sections.

Collective efficacy stems from self-efficacy and has similar factors that affect it. Those factors include enactive mastery, vicarious experience, verbal persuasion, physiological and effective state, performance goals and persistence. However, collective efficacy is associated with being in a group and thus has some different factors that affect an individuals collective efficacy. These include group cohesion, interdependence of the group task, group size and the phenomena of social lofting.

Implications for Instruction

Self-Efficacy - Teaching Presence

Self-efficacy stands as a significant factor in fostering self-regulation in students and have proven to enhance the quality of their learning. This leads to its implications within a classroom that demands the consideration of other factors, such as teachers. One of the most significant drivers of a learning environment are the teachers themselves. It has been shown that an individual’s own perception of self-efficacy was the final determinant of their success and in addition to having successfully acquired the motivation and effort to use self-regulated learning strategies, a teacher may incorporate constructivist learning environments to encourage or enhance these behaviors. As shown in the venn diagram below, personal factors, academic behavioral performance and learning environments interrelate with one another, showing how one factor affects another. Adopting a student-centred approach to learning and teaching can lead to an increase in student involvement; exerting a positive influence on students’ affective and cognitive domains, as well as their perception of the learning environment

Implications for teaching from the above discussed theories of especially task engagement and goal orientation suggest that team‐based learning is very successful when students take ownership of a complex problem, and engage the problem in a collaborative and systematic manner. Team‐based learning environments provide students with opportunities to solve complex problems resulting in their developing greater self‐confidence in their abilities. Understanding the relationship of goal‐setting in the learning process can facilitate a positive team effort experience for students through a learning and iterative process. Students, who successfully learned through collaboration, might be intrinsically motivated and self‐efficacious when placed in other team‐based learning settings. However, students who are inexperienced in this environment or who do not have sufficient knowledge of the subject might require additional guidance in order to have a satisfying experience. If this guidance is not provided, the experience could be not very satisfying, and thus have a negative effect on intrinsic motivation, self‐efficacy beliefs and team‐based learning in general. So, it might be more effective to expose upper class undergraduate students to collaborative learning projects, where it is assumed that they possess the minimum required subject knowledge so that they can successfully apply what they know to the experience: participate in collaborative activities involving critical thinking, and formulate creative and innovative solutions by setting goals.

Teacher efficacy can offer learning strategies that could be beneficial for students; Even though Craig’s et al (2009) study found no relation between teacher efficacy and instructional quality, Teachers with high sense of efficacy can contribute to learning by providing other means to enhance learning such as providing constructive feedback. Teachers can be an important model for students, especially when they incorporate the individual needs of students. Teachers can encourage students to use both enactive and vicarious learning in order to enhance the learning process. Apparently, the most effective way of learning involves learning occurs when students can observe teachers performance, and have some opportunity to apply the learned skills on a similar task. For example, in a math problem, students might benefit from observing a teacher or peer solve a problem, as well as by solving the problem themselves; this allows students to apply the knowledge they learn by vicariously.

In order to promote collective efficacy in group settings teachers should make sure their performance goals are clear, specific and challenging. Making sure the students know exactly what is expected of them for specific tasks allows the students to develop collective efficacy.

Allowing for group cohesion with the right number of members in the group allows for better performance and overall higher collective efficacy. Group cohesion can be achieved by allowing students to pick their groups and let them work in their groups throughout the school year. In addition, making sure the groups are appropriate for the task at hand is essential. Smaller groups should be used for more intimate projects, larger groups should be used when vast knowledge is needed to complete the task, or in group sports the necessary number of players needed in order to play the sport.

Conclusion

Social cognitive theory provides a framework for the constant changing of human behavior. In order to be able to understand and predict such behaviors, it is important to consider the variables that interact amongst each other and how those interacting factors are determined. The essence of social cognitive theory based on the theory that learning is the product of observation. It also considers these foundational interacting variables that come together to explain Bandura’s concept of reciprocal determinism as the basics for the theory of social cognition. Our chapter outlines three different elements that contribute to the social cognitive theory as well as cognition and instruction. Within these elements include self-efficacy, enactive and vicarious learning, and collective efficacy. Self efficacy determines how an individual perceives their own abilities and the level of confidence they have for achieving their goals and well as their abilities. Drawing from self-efficacy, we move on to enactive and vicarious learning that represents the ways we acquire knowledge. Enactive learning refers to the way an individual learns something by doing it, and vicarious learning occurs through observation of others performing the given task. Both learning styles are used in different cases, but the use of both are proven to be the most successful. In relation to self-efficacy, learning through observation - vicarious learning - can improve self-efficacy as it gives individuals a chance to identify with a model and lead to self-regulation. Furthermore, collective efficacy explains the individual perception of success of the group. Bandura argues that collective efficacy greatly relates to self-efficacy as there are factors that influence both efficacies. These factors come back down to the influence of personal, behavioral and environmental components of the reciprocal determinism model.

It is said that environments and social systems are greater influences of human behavior; thus, the social cognitive theory justifies that different factors do not affect individual behavior in a direct manner, but instead affect them to a degree that influence other factors such as one’s aspirations, self-efficacy beliefs, personal standards, emotional states, and other self-regulatory influences (Pajares, 2002). Our chapter determines how these different influences and factors co-exist and affect the basic components of Bandura’s reciprocal determinism theory.

Suggested Readings

Burney, V. H. (2008). Applications of social cognitive theory to gifted education. Roeper Review, 30(2), 130-139. Effect of self- and group efficacy on group performance in a mixed-motive situation. Human Performance, 13(3), 279-298. doi:10.1207/S15327043HUP1303_3

Phan, H. P., & Ngu, B. (2014). Factorial equivalence of social cognitive theory: Educational levels × time differences. Educational Psychology, 34(6), 697-729. doi:10.1080/01443410.2013.814190

Schunk, D. H. (2012). Social cognitive theory. In K. R. Harris, S. Graham, T. Urdan, C. B. McCormick, G. M. Sinatra, J. Sweller, J. Sweller (Eds.) , APA educational psychology handbook, Vol 1: Theories, constructs, and critical issues (pp. 101-123). Washington, DC, US: American Psychological Association doi:10.1037/13273-005

Glossary

`Active observation: Observation that facilitates engagement with the material

Agency: capacity of a person to act in any given environment

Collaborative observing tutoring: Observation of the teaching interaction between a teacher and a student

Collective efficacy: This type of efficacy refers to the individual’s perspective of how well the group can accomplish their task.

Enactive learning: Learning by doing performing a task

Group Cohesion: Is an attraction to group members as well as group members who are interested in the same subject or have a collective mind.

Group efficacy: This type of efficacy refers to the group’s perspective as a whole in how well the group can accomplish their task

Goal Orientation: refers to the mental framework that influences how people approach situations of achievement in terms of interpreting the situation and motivation to achieve

Identification: Feeling close to a person that has similar characteristics as yours

Informational feedback: Feedback that helps improve performance

Learning: Act of acquiring new knowledge

Learning Orientation: aim of completing a task is to gain knowledge

Mastery experience: performance that leads to learning

Performance: Process of completing an action

Performance Orientation: aim of completing a task is to gain favorable judgments of one’s performance

Persistence Continuing in a course of action despite difficulties

Reciprocal determinism: term coined by Bandura to describe the foundation of his theory of social cognition— psychological functioning involves a continuous reciprocal interaction among behavioral, cognitive, and environmental influences

School efficacy: This type of efficacy refers to the school as a whole in relation to how they can effectively promote learning in their school. It is closely related to student and teacher efficacy.

Self-efficacy: how the individual perceives ones own abilities and the level of confidence for achieving goals from the perceived abilities

Self evaluation: Evaluating one self according to a standard

Self-regulated Learning Strategies Uses of students' strategies that regulate individual behaviour

Social comparison: Determine self worth by comparing ourselves to others

Social Lofting: This happens when one person in the group does less work than the other members in the group

Subjective operative capability: the concept that efficacy beliefs form the foundation of human agency as people need to believe that they can produce results by their actions in order or else the incentive or the reinforcement to act is very little

Teacher efficacy : teacher's own belief about their teaching skills

Verbal persuasion: convince someone to do a task by using verbal communication skills

Vicarious learning: Learning by observing others

Worked examples: Explanation of how to solve a problem

Volition Cognitive process that allows one to decide on committing to a course of action.

Reference

Woolfolk, A. E., & Hoy, W. K. (1990). Prospective teachers' sense of efficacy and beliefs about control. Journal Of Educational Psychology, 82(1), 81-91. doi:10.1037/0022-0663.82.1.81

Social Contexts of Learning

This chapter discusses beliefs about the social contexts of cognition, and how social and cultural factors can influence a child's development of mind (thoughts). In the subsequent sections of this chapter, we will discuss social cognition, situated cognition, Bronfenbenner's ecological model, the child in culture, social interaction/cognitive tools, socio-cultural contexts of learning, implications for instruction, and individual contextual differences. Situated cognition theory identifies features of the environment relevant to immediate conversational contexts, interpersonal relationships, and social group memberships. It also increases our understanding about how these features shape thoughts and actions. We also look into Bronfenbenner's ecological model and it's influence on a child's learning environment. In the socio-cultural context, Vygotsky theorized that human development was inseparable from cultural and social development and that these social interactions help children to develop cognitive tools. These cognitive tools develop skills specifically tied to an individual's personal culture and social experiences and include language/speech and cultural production. As time progresses, these skills become internalized in the zone of proximal development. In relation to instructional implications, placed based, culturally based, and cooperative learning techniques are discussed. It will help future educators use this theory and research effectively, and apply it to a practical classroom setting. Individual Contextual Differences have various influences on our cognitive development. It encompasses both Bronfenbrenner's theory about the influence of the microsystem and macrosystem in regards to child development and Vygotsky's theory on social and cultural factors being essential to cognitive development. Therefore, we look at how differences in societal, classroom and institutional settings have an effect on a child's cognitive development. The social context in which cognitive processes take place are highly influential in the development of mind.

Social Cognition

Social cognition focuses on the theory of mind. Theory of mind is a broad concept, encompassing and understanding the full range of mental states, as well as the antecedents and consequences of such understanding.The social context is made up not only of our relationships with specific others but also the groups we identify. As we continue to develop and associate with certain social groups, this becomes a part of our “social identity"[1]. These social groups establish norms, or standards for correct and appropriate beliefs, opinions, and behaviors. For example, it may be the "norm" to use inappropriate language with your friends, but not with your parents or family members . Such norms influence our behavior all the time, whether other members of the social groups are physically present or not. This social identity is activated by situational reminders of our social group membership or by our own intentional thought. Once this identity is activated, we tend to conform to that group’s norms. [1].

Social identities serve as behavioral guides for appropriate behavior. This can have some negative effects. If define social identity by our social group membership that we share with some people but not others, it divides the world into ‘us’ and ‘them.’ Shaping how we think about and behave toward other people. People on the ‘us’ side of the line, are considered group members and are therefore better liked.[2] In a school context, children can often become victims of bullying if they do not identify with a popular social group, and adopt a social identity that suits their peers "cultural norms".

In order to understand the development of social cognition and social identity, we must examine situated cognition. Cognition almost invariably occurs in the context of other people. It refers to the web of face-to-face encounters, personal relationships, and social group memberships that make us who we are. Not only do these social entities very frequently constitute the content of our thoughts and feelings, but they fundamentally shape the processes underlying our thinking and behavior as well. To detail some of the evidence for this broad claim, this chapter describes the interface of situated cognition, the ecological model of development, and the child in culture. The social context of cognitive development has to do with our thoughts and beliefs about the social world. It also refers to our beliefs about others, the self, people in general, specific aspects of people (e.g., thoughts, desires, emotions), social groups and social institutions[3]. Situated Cognition

Situated cognition is centered on the idea that knowing is “inseparable” from actually doing and highlights the importance of learning within context[4]. The Situated Cognition Theory is based upon principles related to the fields of anthropology, sociology, and cognitive sciences. Its main argument is that all knowledge that a learner acquires is somehow situated within activities that are social, physically or culturally-based. The Situated Cognition Theory mainly supports, that the acquisition of knowledge cannot be separated from the context in which this knowledge is collected. Therefore, a learner must grasp the concepts and skills that are being taught in the context in which they will eventually be utilized. As a result, instructors who are trying to apply this theory in their classes are encouraged to create an environment of full immersion, wherein students must be able to learn skills, as well as new ideas and behaviors that are taught in the context in which they will be used at a later time. Collins, Brown, & Duguid are creators of the situated cognition model, and believed that learning culture played a major role in education, and that learning by doing was an often overlooked approach[4].

Situated cognitive learning emphasizes that learning in the real world is not like studying in school. It is often describe as acculturation or adapting the norm, behavior, skills, belief, language, and attitudes of a particular community. This community might be mathematicians, gang member, writers, and students of any group that has particular ways of thinking and doing. Knowledge is seen not as individual cognitive structures but as a creation of the community over time. The practices of the community, the way of interacting and getting things done, as well as the tools the community has created constitute the knowledge of that community. Thus learning means becoming more able to participate in those practices and using the tools. Situated cognitive learning emphasizes the idea that much of what is learned is specific to the situation in which it is learned[3]. However, situated cognitive learning says that knowledge and skills can be applied across contexts, even if the context is different from the initial learning situation. For example, when you use your ability to read and calculate (which you learned in school), to complete your income taxes, even though learning how to do your taxes was not part of your original high school curriculum[5]. In this situation, the student would be applying their mathematics and reading skills which they learned in the classroom, to the real world. Demonstrating how situational learning can be applied across different contexts.

Situated cognition offers the key insight that cognition is for adaptive action, our minds evolved under the demands of survival rather than for detached puzzle-solving or abstract cognition. This principle implies the existence of close connections between cognition, motivation, and action. Cognition is generally not neutral and detached, but is biased by the individual’s motives and goals, with motives shaping our thoughts. Consider a person’s understanding of the meaning of traits (such as reliable, honest, or intelligent), which are basic components of our impressions of other people as well as ourselves[2]. Research shows that our definitions of such traits are not objective and invariant, but are shaped in self-serving ways by our own perceived understandings of those traits. Also the fundamental human need to belong shapes our social cognition. People experiencing a heightened need to belong, after a social rejection; tune their attention and cognition to process social information in the environment more carefully and thoroughly. This example of biases in cognition caused by the perceives motivational concerns effectively illustrate how social cognition serves the needs of adaptive action important in determining the course of cognition [6]. There is evidence that social-cognitive development and learning recognizes that individuals develop through reciprocal interactions, in which people contribute to an individuals development. These social interactions, are rooted in the situation in which it occurs. Research on reciprocal transactions between organisms and the environment is a basic feature of Brenfenbenner's ecological theory.[7] Social-cognitive learning states that a child's personality functioning differs among individuals. Personality is understood by reference to basic cognitive and effective structures and processes. These personality variables develop through experiences with one’s sociocultural environment. Social-cognitive development differentiates among a number of distinct cognitive capacities contributing to personality functioning. These include cognitive mechanisms that underlie skills and social competencies, knowledge structures through which people interpret or “encode” situations, self-reflective processes through which people develop beliefs about themselves and their relation to the social environment, and self-regulatory processes through which people establish personal goals and standards for performance and motivate themselves to reach desired ends[8]. In the next section, Bronfenbrenner's theory divides the community in which a child grows up into four systems. The community in which a child develops, will ultimately effect the situation in which the child learns, a child's interpersonal relationships and who they associate with. As previously mentioned, social cognition and situational cognition explain the development of a child's mind, but both can be largely influenced by a child's environmental context. Bronfenbrenner outlines some of these social contextual influences in his ecological model.

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Ecological Model

Bronfenbenner's Ecological Model

Ecological Model.gif

Bronfenbrenner provides an ecological model for understanding human development. He explains that children’s development within the socio-cultural context of the family, community, broader society and the educational setting. All have an impact on the developing child, because all the various contexts are interrelated. For example, even a child in a supportive, loving family within a healthy, strong community is affected by the biases of the larger society, such as racism, sexism or violence, and may show the effects of negative discrimination and stereotyping. Bronfenbrenner’s ecological context of child development and learning can be depicted as a series of concentric rings as with each system influencing and being influenced by the others[7] for example:

Microsystem

Bronfenbenner's theory: The microsystem is the system closest to the person and the one in which they have direct contact. Some examples would be home, school, daycare, or work. A microsystem typically includes family, peers, or caregivers. Relationships in a microsystem are bi-directional. In other words, your reactions to the people in your microsystem will affect how they treat you in return. This is the most influential level of the ecological systems theory.

Let's look at the microsystem in Marian lives. The first part of his microsystem is her home environment. This includes his interactions with her parents and little sister. Marian’s school is also part of her microsystem. Her regular school interactions are with her kindergarten teacher and the other children in his class[9].

Mesosystem

The next level of ecological systems theory is the mesosystem. The mesosystem consists of the interactions between the different parts of a person's microsystem. The mesosystem is where a person's individual microsystems do not function independently, but are interconnected and assert influence upon one another. These interactions have an indirect impact on the individual.

One aspect of Marian’s mesosystem would be the relationship between her parents and her teacher. Her parents take an active role in her school, such as attending parent/teacher conferences and volunteering in her classroom. This has a positive impact on her development because the different elements of her microsystem are working together. Marian’s development could be affected in a negative way if the different elements of her microsystem were working against one another[9].

Exosystem

The exosystem is the next level we will examine. The exosystem refers to a setting that does not involve the person as an active participant, but still affects them. This includes decisions that have bearing on the person, but in which they have no participation in the decision-making process. An example would be a child being affected by a parent receiving a promotion at work or losing their job.

One part of Marian’s exosystem would be his father's workplace. Marian’s father is in the Navy. This often takes her away from the family, and she sometimes does not see her father for months at a time. This situation impacts Marian, and she becomes anxious when her father leaves. Marian’s anxiety has an effect on his development in other areas such as school, even though she has no interaction with her father's work or say in the decision making process, but this may have impact her learning environment[9].

Macrosystem

The fourth level of ecological systems theory is the macrosystem. The macrosystem encompasses the cultural environment in which the person lives and all other systems that affect them. Examples could include the economy, cultural values, and political system. The macrosystem can have either a positive or a negative effect on a person's development. For an example, consider the different effects on the development of a child growing up in a third-world economy versus that of the United States.

Ecological theorists such as Bronfenbrenner[7] point to the importance of the settings and circumstances in which students live for understanding children’s behavior and establishing productive programs and policies to promote the development of children and youth. Teachers make many decisions that can be informed by an understanding of the context in which children live. These decisions include curricular and instructional decisions about materials and methods used in the classroom. Teachers’ guidance of children’s classroom learning can be fostered by understanding how the knowledge, practices, and language socialization patterns within children’s families and communities contribute to children’s ability to function in the classroom how to communicate and work with children’s families,[7] as well as how to promote children’s participation and positive social relations in the classroom influence by cultural and social context. The Child-in-Culture

The child in culture, it is important for teachers to learn about the culture of the majority of the children they serve if that culture differs from their own. Recognizing that learning and development are influenced by cultural and social context, it would be an impossible task to expect teachers/caregivers to understand all the nuances of every cultural group they may encounter in their practice. It is more important for teachers/caregivers to become sensitive to the knowledge of how their own cultural experience shapes their perspective and to realize that multiple perspectives must be considered in decisions about children’s learning and development, in addition to their own. Children have the learning ability and capability to function simultaneously in more than one cultural context. However, if teachers/caregivers set too low or too high expectations for children based on their home language and culture, children cannot learn and develop optimally and reach their full potential. The ideal would be for example, that children whose primary language is not English should be able to learn English without forcing them to give up their home language and to get a teacher/caregiver to translate or teach in both languages. Likewise, children who speak only English benefit from learning another language. The goal is that all children learn to function well in the society or even community as a whole and move comfortably among groups of people who come from both the same and different backgrounds[10]

In understanding the mind of the child (learner), teachers must also understand that each student is an individual who is developing a sense of self and relationships in a variety of contexts, notably the family, school, and community.[9] Hence, teachers considered themselves least knowledgeable about issues concerning diversity and schooling effects on students. This perception exists despite major efforts made at the national level to provide guidelines for preparing teachers to teach culturally diverse students.[11] Research suggests that there is both cause for concern and hope for improvement. For example, Hollingsworth,[12] indicate that novice teachers’ views of children are often inaccurate because they assume that their students possess learning styles, aptitudes, interests, and problems that are similar to their own.[12] Furthermore, recent research suggests that prospective teachers hold simplistic views of student differences have little knowledge about different cultural groups, may have negative attitudes toward those groups, they Teachers) may view diverse backgrounds of students as a problem, and have lower expectations for the learning of ethnic minority students.[12]

For some children, these points of difference may not have much effect. But for others, the mismatch between parental or community expectations and the expectations of the formal learning environment may leave the child feeling as if he or she is straddling two distinct worlds. How we think about child in culture can help us move toward greater sensitivity or, alternatively, can create additional roadblocks to our ability to engage and work with families. Early calls for cultural competency sometimes put forward a list of observed parenting traits of minority cultures with little explanation of how these aspects of culture may be part of a whole and with little understanding of the cultural participants’ intention behind these actions. This type of thinking, though well-meaning, can solidify stereotypes instead of helping us penetrate them. Educators, open to embracing the diverse cultures represented in their classrooms, had little guidance in how to achieve this sensitivity in more than just a superficial way. One observation notes that ironically, teachers may conscientiously try to create culturally sensitive environments for their students (e.g., through multicultural displays and activities) while simultaneously structuring classroom interaction patterns that violate invisible cultural norms of various non-dominant groups. Teachers may also inadvertently criticize parents for adhering to a different set of ideals about children, families and parenting[13].

Research have shown that in many content domains when children are asked to learn or solve problems based upon materials with which they are familiar, or in ways that make “human sense” they learn more rapidly. These relations between culture and learning do not fade away, but become even more pronounced as children move from early into middle childhood and adolescence. Consequently, those concerned with leveraging the power of culture to promote learning should take care to pay as much attention to the cultural enrichment of children as to their health and physical well-being, all of which play an especially important role during this period of extraordinarily rapid developmental change[13]. Cognitive Tools and Social Interaction

The previous sections have mentioned how a community influences cognition by determining the context in which a child learns about the social and cultural rules around them[5]. This community also determines the situation in which learning and cognitive development takes place. For example, a child who grows up in a rural town in Saskatchewan is going to have grown up in a very different community, when compared to a child who grew up in New York City. Their learning will have taken place in a classroom with different socio-cultural "norms". Although these skills can be transferred across situations, each child is going to develop a different set of cognitive tools that reflects the cultural and social environment they grew up in. Cognitive tools are specialized, and designed to guide a learner in following the "norm" behaviors dictated by a particular community[5].

In a community, there are many social interactions and processes. As time goes by, these social interactions define our patterns of thought and cognition. This social cognition refers to the information processing of social situations. Once this information is encoded, it is used in all other social interactions and applied to people. Due to this fact, early interactions will shape and serve as a template for future pro-social behaviors. These early interactions also influence our ways of thinking, and shape how we view the world. This type of situated cognition, refers to knowledge that is learned and developed through authentic activity [4]. Social interaction can serve as an important conceptual tool. They reflect the collective knowledge and wisdom of the culture in which they are used, and connect the insights and experiences of individuals[4]. These tools are understood through repeated use, and by interacting with others. Over time, these tools become implicit knowledge and shape your view of the world. Allowing you to adopt the belief system of the culture they are learned in. For example, Vygotsky states language is a cognitive tool produced through social interaction[14]. Language is tied to culture, and different languages have different semantic meanings, leading to differences in speech and cognition. These differences in socio-cultural acquisition influences an individuals thought patterns and beliefs[14]. In this way, social interaction creates cognitive skills, specifically tied to an individuals personal cultural and social experiences. In the following sections, we define Vygotsky's socio-cultural contexts, and explain how these contexts produce cognitive tools such as language, speech, and cultural production, and how these tools are learned through more knowledgeable others in the ZPD. Socio-Cultural Contexts of Learning

In the 1930’s, psychologist Lev Vygotsky developed a new socio-cultural theory of learning and development. His theory was conceived decades before Bronfenbenner's ecological model, although both psychologists emphasized the social and cultural context. At the time, Vygotsky's theory contrasted that of the popular child development theorist, Jean Piaget[14]. For his era, Vygotsky's theory of development was revolutionary. He stated that human development was inseparable from cultural and social development[14]. These social and cultural interactions lead to the development of higher cognitive processes such as language, and attention[14]. Vygotsky developed four basic principles of learning and knowledge. These are: knowledge is constructed, development cannot be separate from the social/cultural context, language plays a central role in mental development, and learning is facilitated through collaboration by working with "more knowledgeable others" [14].

The learning of these socio-cultural processes occurs through the cultural inventions of a society. Thus, development of conscious cognition is the result of social and cultural influences[14]. Vygotsky defined specific aspects of these social interactions as specialized cognitive tools. These tools become internalized as a learner progresses through the ZPD, and shape our thought patterns. Specifically, Vygotsky emphasized language, speech, and cultural production as highly influential cognitive tools produced through socio-cultural interaction. Vygotsky also stated, that these cognitive tools are learned and enforced through more knowledgeable others in the ZPD[14]. These concepts will be broken down, and explained in detail in the subsequent sections. Language and Speech

The development of cognitive processes, are shaped through communicative interactions in specific social situations of development[15]. Vygotsky, emphasized that speaking and thinking are unified, with two basic functions: revealing reality, and communicating meaning in social interactions. Through language, an individual’s cultural identity is formed, because children acquire knowledge in a specific cultural setting through familial and institutional influences[16]. As Bronfenbenner suggested, the ecological community in which learning takes place, influences developmental processes like language and speech[7]. Language initially serves as means of communication between the child, and people in the immediate environment[16]. However, upon conversion to internal speech, it affects how a child organizes his/her thoughts. It becomes an internal mental function[16]. For example, a child that grows up in an English western family, has a different dialect and system of values and beliefs compared to a child that grows up in rural India[15]. These differences can manifest in differing writing styles. This is because, each child has their own set of deliberate semantics, and words can have different meanings[15]. This is also known as, dialectic contradictions, which are historically accumulated structural tensions in a language[15]. These differences in the cultural context of language acquisition, manifest themselves in differing thought processes resulting in different cognitive and communicative interactions. This process of language/speech acquisition, can also be referred to as acculturation[4]. In this way, language is a cognitive tool as it has the ability to influence our patterns of thought.

Cultural Production

In previous sections, culture was defined as acculturation[5], or the process where a child learns and adopts the "norm" beliefs and values of a community. Each child learns these norms in different situational contexts and interactions. After repeated use, these norms become a part of a child's social identity, and determines the character of a child and future patterns of behavior and thought[5]. Culture can be produced through language and speech, the learning of cultural norms from elders of a group with mastery social skills (ZPD), and by a community[4].

Culture plays a dominant role in shaping social interactions, and the development of cognitive processes. It is a tool that is changeable, and created during a child’s early social lives[14]. Cultural production can occur at two levels: institutional (macrosystem), and intrapersonal (microsystem). In an institutional setting, this refers to the larger social context such as school settings, political context etc. An interpersonal setting would refer to interactions between each other , such as peer to peer interactions[14]. An individuals overall cultural history, is responsible for producing useful cognitive tools that are accumulated over time [14]. Eventually, this leads to the internalization of culturally valued skills and behaviors, making these cognitive processes automatic[14]. A culture creates special forms of behaviors, which are specific to a specific cultural history[15]. These structures affect problem solving capacities, and patterns of social interactions. To examine these differences, psychologists can conduct cross-cultural studies. An example of a cross-cultural study, could include investigating how some cultures don’t believe in displaying knowledge overtly, compared to cultures where that is considered a good thing. Vygotsky states that culture is developed and produced through processes of social interactions, and through active agents in the immediate development context.

Zone of Proximal Development (ZPD)

Vygotsky theorized that learning largely occurs in a child’s ZPD. It mostly takes place in Bronfenbrenner's microsystem level of the ecological model. He defined this as “the distance between the actual developmental level, as determined by actual problem solving, and the level of potential development under adult guidance or in collaboration with a more capable peer[17] .” This form of social interaction occurring between the student and “more knowledgeable others,” serves as a cognitive tool for developing higher learning processes[17]. In a classroom setting, a more knowledgeable other includes any active agents such as teachers, supervising adults, or peers[17] . There are three levels of a learners developmental progress in the ZPD over time (see figure 2 [17]). These three levels are the actual level, potential level and realized level[17]. The actual level refers to what a learner is able to accomplish without assistance. It refers to the actual base level of knowledge a student possesses on their own[17]. Whereas, the potential level is how well a learners performs with assistance by a more knowledgeable other[17]. A student has the capability to achieve this potential level of knowledge through collaboration. For example, a tutor is helping a grade two student learn grade three level mathematics. On their own, the student is able to readily solve grade two mathematics problems. Since this student possesses a strong actual level of mathematics, the student can be taught grade three level mathematics by collaborating with a more knowledgeable tutor. Eventually, through rehearsal and practise, the student is able to complete grade three mathematics problems on their own. This is referred to as their realized level of knowledge. Three Stages of ZPD Progression

Figure 2.[17] Adapted from “The Mediation of learning in the Zone of Proximal Development through a Co-Constructed Writing Activity,” by Thompson, 2013 Research In The Teaching Of English, 47(3), p.259

Essential to this theory, is that the level of knowledge being learned must be more advanced than what the student currently knows [17]. Teachers can also use scaffolding, which uses a student’s prior knowledge to help give students a base level of information They can use this to build more complex concepts[17]. Like in the example, the tutor built off the students prior knowledge of grade two mathematics, and made sure the material was more advanced than what the student currently knew. Before a student attempts to master a new skill, they can be given supplemental information to introduce them to the new material. This can include artifacts such as: books, videos, textbooks, and computer technology[17]. These artifacts act as priming agents for learners, and ease the learning transition to more complex concepts. By using the ZPD as a cognitive tool, instructor’s can approach mastery of more difficult skills through collaborative classroom strategies. See figure two for further explanation learning through the ZPD[17].

Learning in the ZPD.jpg

Figure 3[17]. Stages of Learning in ZPD. Adapted from “The Mediation of learning in the Zone of Proximal Development through a Co-Constructed Writing Activity,” by Thompson, 2013 Research In The Teaching Of English, 47(3), p.257 Implications for Instruction

The social lives of school children, can have many instructional effects. As previously mentioned, the situation in which information is learned, level of difficulty, collaboration with more knowledgeable others, level of social cognition/competency, and cultural production, all have differing instructional effects in the classroom. Each student has a different cultural history, that influences their patterns of thinking, and how they approach solving problems in the classroom. Teaching should incorporate the situation and use conceptual tools[4]. Learning should involve, the activity, concept, and culture. For example, teaching children the definition of words. It is simply not enough to have them write out definitions from the dictionary, in an abstract way[15]. Learning words, must take place in an authentic way, and relate to the cultural situation in which the word is defined and used in speech[4]. The next section will discuss how some of the previous social and cultural factors can be mediated through instructional methods. Some useful pedagogies for instructors that will be discussed are place based and cooperative learning strategies.

Place-based Instruction

One way of taking otherwise abstract concepts and rooting them in culturally meaningful pedagogy, is a method known as place based instruction. It uses both ideas about situated cognition and Vygotsky’s socio-cultural theory. The environment in which we learn and situation in which the learning takes place, is responsible for co-creating our knowledge. A place based learning approach is suited for the multi-cultural classroom. If focuses on transforming the traditional classroom environment, into a place that engaging for all types of learners[18]. At its core, it links place to cultural struggles, and aims to empower diverse learners through the integration of local cultural knowledge[18].

Main Focuses of Place Based Pedagogy[18]:

1. Support thinking about a system using the “bigger picture”

2. Connect students to lived experiences- creating meaning through place based instruction

3. Foster Reflexive Inquiry

4. Regulate and Control How Ethnically Diverse Learners Organize their Identity

One way this pedagogy can be implemented in the classroom is by creating a community garden. It is a creative way of incorporating language, culture, and environment by increasing feelings of interconnectedness[18]. A community garden is open to all, and provides a green space for children in urban areas. It creates a setting for social interactions to take place through the cooperative planning, designing, and execution of a garden and all its elements[18]. The garden is a great way of creating conversation between students about local and self-cultural identity[18]. Students can research herbs related to their cultural background, and report to the class the various cultural ways in which the herb is used culturally like in, cuisine, medicine, or religion[18]. They can then plant these herbs in the garden, tying place with the construction of their knowledge. This also allows for peers to create conversations about cultural differences, fostering reflexive inquiry [18]. The place based framework, examines how a culture and local environment makes up the community and culture of the school. This method also allows ethnically diverse learners to, self-identify their cultural values, and decide what they want to share. This control and the self-regulation of their own identity, can help grow self-regulated learning as well[18].

Culture-Based Education and Its Relationship to Student Outcomes

Adapted from: Kana‘iaupuni, S., Ledward, B., & Jensen, U. (2010). Culture-based education and its relationship to student outcomes. EDUCATION.

Figure 4. "Hawaiian Cultural Influences in Education Study Model[19]"

In a study by Kana‘iaupuni[19], they explored the kinds of teaching strategies being used in Hawaiian classrooms and investigated the impact of teachers’ use of culturally based education strategies (CBE), on student socio-emotional development and educational outcomes. Cultural relevance in education was shown to have direct effects on student socio emotional factors such as self-worth, cultural identity, and community/family relationships. It was also shown to have direct and indirect effects on educational outcomes, such as student engagement, achievement, and behaviour[19] (Kana‘iaupuni, 2010). In Figure 1, it shows the reciprocal interrelating relationship between CBE, educational outcomes, and socio-emotional development. Adapted from: Kana‘iaupuni, S., Ledward, B., & Jensen, U. (2010). Culture-based education and its relationship to student outcomes. EDUCATION.

Figure 5: "School Engagement Among Hawaiian Students By Teacher CBE Use[19]"

The results of the of the study show (see figure 5[19]) that teachers who use CBE methods in the classroom have higher levels of student self-efficacy and trust, than students with Low CBE Teachers. Students exposed to high levels of CBE by their teachers are also more likely to be engaged in schooling than others, by putting cultural skills to use in their communities and forming trusting relationships with teachers and staff[19]. In the study, they used methodology involved in place based pedagogy[18]. They took into account the local environment and interwove it into the curriculum. Students took part in classes teaching Hawaiian culture, and and/environmental stewardship[18]. The study illustrates how place based pedagogy can significantly improve students rates of self-efficacy and trust in the classroom when teachers use a high amount of CBE/place based curriculum[19]. Cooperative Learning

In Vygotsky’s zone of proximal development, he emphasized the importance and role of peer collaboration and learning. Cooperative based learning refers to intentional learning activities, where group members work towards a shared learning goal[20]. It is different from classroom “group work,” as group work does not always guarantee actual learning will take place . The goal of cooperative based learning is to understand that each learner brings their own particular set of skills to the table[20]. If differs from collaborative learning, because students are not trying to improve a weak skill, but rather identify the skills they excel in and use them to help the group.For example, Amy may struggle with abstract concepts like mathematics, but has a great imagination (Also, see figure 3[20]). She is grouped together with students who excel with abstract concepts, but struggle thinking imaginatively. This way, students are able to share their skills, and teach each other. This is known as reciprocal teaching, where learners are able to teach other members of their group[20]. By working towards achieving their common learning goal, students must combine their different skill sets to solve the problem. It can help students see different perspectives on how to approach problem solving activities[20].

The Five Steps to Achieve Cooperative Learning in the Classroom[20]

1. Give Specific Learning Objectives

2. Plan out learning strategies, and composition of groups

3. Explain the learning objective

4. “monitor-observe” the students

5. Assess the achievement and cooperation of students

Some examples of cooperative learning strategies for the classroom are the jigsaw method and group investigation method[20]. In the jigsaw method, students are divided into groups. Then, one member from each group is sent to a special group to learn about a specific course topic. Once students individually read the material, they discuss and reflect upon the material as a group, making note of its key points[20]. Lastly, each student returns to their original groups. After their peers read the material, the student sent to the special group leads their group discussion, reflecting on the topics key points. The premise of this strategy is to have the students in each group teach each other, and become better self-regulatory learners[20]. In the group investigation method, students are first divided into groups. They are then given information about a specific course topic, and read through the material individually, and are asked to make note of its key points[20]. After this, the group discusses the material collectively, reflecting on its key points, and could be asked to prepare a presentation for the class.This strategy promotes group dialogue and aims at cultivating social interaction skills. Cooperative learning, is a strategy that instructors can use in the classroom to promote social cognitive growth, and increase student's performance[20]. In the next section, we discuss how social cognitive processes are affected by macrosystem influences, such as individual contextual differences in societal, classroom, and institutional settings. Individual Contextual Differences

The cognitive development process can be differed individually. Lots of aspects of social context can have varies of influences on our cognitive development, Such as: intelligence, environment factors, learning opportunities, economics status, family and society. As previously mentioned, the social and cultural context in which learning takes place, greatly affects our cognitive growth. Theories like situated cognition, Bronfenbenner's ecological model, and Vygotsky' socio-cultural theory, discuss how macrosystem influences such as the cultural environment, make up our implicit views on the world. In this section, we will look into how different classrooms, different institution and society can affect our cognition and how do we do to improve this development.

The problem of boys having lower graduation rates, greater worries about intimacy and relationships are touched upon to suggest some reasons behind it. Using the internet and accessing pornography are acting as arousal addictions that have negative effect on social life of boys. Lots of documents shows the problems of women getting misrepresented, objectified and sexuality are evident in our societies’ status quo. The society and media is often portraying women as object for sex and beauty, demising women’s actual capabilities. We should advocate the need to value women’s capabilities and encourage them to discover their true power.. Simply put, media is any device or system that we humans use to accomplish our goals. The wheel, an oar, an abacus, a hammer, a toothpick, and a TV set are various examples[21].

These influences heavily affect development of the authentic self for both males and females negatively. Being authentic self is being who you really are, knowing your personal why, discovering your capabilities and expressing your inner self to others. These are real, genuine and authentic which comes from your heart. The problem with the media is that they are portraying cognition of what it means to be ideal women or men that are accepted by the society. Often, these perfect images of beauty, success and satisfaction are falsely created by media often to get more people’s attention and money. Thus, people start to take in what the media tells them to be rather than finding their own true beauty, capabilities, and values that are truly meaningful for themselves. For that reason, the media exposure simply makes us to seek what is ideal in our society instead of genuine values that are found within self-discovery so lots of people are developing a wrong cognition because of that. In order to sustain the authentic life, we need to have a clear sense of values and define our view of life that comes from inner self. Our own clear vision, belief, goal, and mind act as a firm pillar that support from being impressionable person who easily get swayed by society and media influence. Therefore, we can prevent ourselves from following other people’s values.When movies and television first appeared predictions were made that they would replace most, if not all, classroom instruction[21]

The notion that these media companies are “giving us what the public want” is concerning because they’re actually just giving us what the media companies and advertisers want, and manipulating viewers in believing that it is our fault for the brainless content that’s being produced. It’s also a problem that men make up the majority of the board of these reputable media companies because the way women are represented is inaccurate and are often times exploited through the views of white, capitalist male elites who take no interest in genuine women empowerment. On the other hand, although men aren’t as demonized via media as women are, they still do struggle with radical stereotypes, biases, and discrimination. In Demise of Guys, Atherton mentions that men are constantly exposed to explicit content such as pornography, creating an “arousal addiction[22].” Men are also constantly shown “ideal” images of masculinity where there is a lack of emotional representation and here, problems in intimacy and relationships will start to manifest.

These media influences affect the development of the authentic self for both females and males in a sense that when they are exposed to inaccurate representations without knowledge on the corporate strategy behind it, they will be easily manipulated into believing that who they are and how they look isn’t good enough. Especially for girls and boys who are exposed to explicit and exploitative content at a young age, they will start to believe that what they see on media is their reality. When in reality, everyone is different – we come in all shapes, sizes, and color – and it’s important to base your beauty from within rather than from the physical.Educators increasingly are aware of media’s potential for changing how learning and teaching take place. Even though education continues to lag behind other segments of society in using media[21]. Media likes to hyper-sexualize women and pit them against each other while romanticizing the male character for their strength and independence. Although some women and men might prefer to play that role in reality, we would possibly live in a different society if we focused on issues such as gender equality, health and fitness, and educated viewers on the reality of the world instead of the dream. Classroom

We should value children’s competencies in learning, focusing on self-directed learning approach.We should value children’s competencies in learning, focusing on self-directed learning approach. Rather than simply throwing information and knowledge at children, it is important to acknowledge that they are capable, competent learners who are not helpless. Children are competent enough to be innovated by learning, creating changes and solving problems. We should also emphasize design thinking approach where children are engaged in real life context to solve problems and create solutions. Thus, the opportunities actively engage children to be part of a community member. They can highly relate their learning in their real life that matters and is meaningful. We should be providing real tools and materials to build real things where children have an access to these materials for their creative ideas of invention. The social contexts of cognition and learning have obvious applications to the classroom. As any teacher knows, the classroom is above all a social environment and teaching is a form of social interaction that affects group collaboration, motivation, learning and even use of technology[23].

One of the strength of these kind of learning approaches is that these encourage children to form great cognitions and fulfill their potentials. By recognizing children’s capacities to think, learn, and change will help them to see their learning abilities. Also, these approaches of learning are very good for children to enjoy and have some fun. Because it requires children to come up with their own creative ideas and solutions, they can have more interest in what they do and learn throughout the process. The weakness in these approaches is the possible financial problem. Many resources and materials are probably needed for children to access that could cost quite of bit of money. If these approaches of learning are incorporated in other regular classes, funding will be needed and not all schools can afford it as they wish.

The self-directed learning approach can help students to be engaged in what they learn and do with genuine interests[24]. Also, being in the field rather than simply staying in the classroom can motivate them better. Thus, the learning can be made more effectively. For instance, whenever students go to a field trip to learn about certain thing with their own eyes, it got me more interested and motivated. Do you still vividly remember when you went to Science World, different kinds of museums, and Camps where you got to participate in activities that engaged you actively? The answer will be yes. Institutional

The whole education system is seems like "Building a house", and the base or the foundation construction is the most important part for a building, just like the meaning of the elementary education for the whole education system[24]. Lots of schools are ranked according to standardized testing, but the author didn't told us is this kind of practice is right or wrong, good or bad. However, school ranking in some way is good, they may help schools to improve themselves by comparative. But with my personal experience, the ranking by testing for student is not good and really make me stressful in my whole middle and high school. In China, school ranking and ranking students in all schools is very universal, they divide student into two classes, good and bad. Then, the parents who wants their child get in the good school or class, they will pay a lot money and time for them. This classification is serious influence and hurt students' self-esteem and enthusiasm for learning and study. In conclusion, in view of all its defects and the harmful effects of university and schools, why would anyone pay attention to the school ranking?

"when the teaching begins, educators must ask, who are the students, what are their particular needs, and what do they bring to the classroom?" points out the importance of student in teaching and curriculum design as well as the whole education process. When a school designs their education methods, they should consider the students themselves. What is their goal of learning? How will students' own value, culture and experience influence their learning? And what can teachers learn from the students? If remembering the questions when designing and implementing curriculum, I think the curriculum can better cope with students' needs.

We do have pressure on curriculum, which includes technology, culture, economy and environment, etc. When designing and implementing curriculum, it is also very important to consider these factors that will influence students' learning goals, needs, etc. For example, a curriculum for in-class course may greatly differ from a distance course.

The problem of boys having lower graduation rates, greater worries about intimacy and relationships are touched upon to suggest some reasons behind it. Using the Internet and accessing pornography are acting as arousal addictions that have negative effect on social life of boys. Lots of documents shows the problems of women getting misrepresented, objectified and sexuality are evident in our societies’ status quo. The society and media is often portraying women as object for sex and beauty, demising women’s actual capabilities. We should advocate the need to value women’s capabilities and encourage them to discover their true power.

These influences heavily affect development of the authentic self for both males and females negatively. Being authentic self is being who you really are, knowing your personal why, discovering your capabilities and expressing your inner self to others. These are real, genuine and authentic which comes from your heart. The problem with the media is that they are portraying cognition of what it means to be ideal women or men that are accepted by the society. Often, these perfect images of beauty, success and satisfaction are falsely created by media often to get more people’s attention and money. Thus, people start to take in what the media tells them to be rather than finding their own true beauty, capabilities, and values that are truly meaningful for themselves. For that reason, the media exposure simply makes us to seek what is ideal in our society instead of genuine values that are found within self-discovery so lots of people are developing a wrong cognition because of that. In order to sustain the authentic life, we need to have a clear sense of values and define our view of life that comes from inner self. Our own clear vision, belief, goal, and mind act as a firm pillar that support from being impressionable person who easily get swayed by society and media influence. Therefore, we can prevent ourselves from following other people’s values.

The notion that these media companies are “giving us what the public want” is concerning because they’re actually just giving us what the media companies and advertisers want, and manipulating viewers in believing that it is our fault for the brainless content that’s being produced. It’s also a problem that men make up the majority of the board of these reputable media companies because the way women are represented is inaccurate and are often times exploited through the views of white, capitalist male elites who take no interest in genuine women empowerment. On the other hand, although men aren’t as demonized via media as women are, they still do struggle with radical stereotypes, biases, and discrimination. In Demise of Guys, Atherton[22] mentions that men are constantly exposed to explicit content such as pornography, creating an “arousal addiction.” Men are also constantly shown “ideal” images of masculinity where there is a lack of emotional representation and here, problems in intimacy and relationships will start to manifest.

These media influences affect the development of the authentic self for both females and males in a sense that when they are exposed to inaccurate representations without knowledge on the corporate strategy behind it, they will be easily manipulated into believing that who they are and how they look isn’t good enough. Especially for girls and boys who are exposed to explicit and exploitative content at a young age, they will start to believe that what they see on media is their reality. When in reality, everyone is different – we come in all shapes, sizes, and color – and it’s important to base your beauty from within rather than from the physical.

Media likes to hyper-sexualize women and pit them against each other while romanticizing the male character for their strength and independence. Although some women and men might prefer to play that role in reality, we would possibly live in a different society if we focused on issues such as gender equality, health and fitness, and educated viewers on the reality of the world instead of the dream. Conclusion

In conclusion, from a socio-cultural perspective there are many social influences on cognitive development. As previously stated, the socio cultural context of cognition is explained through social and situated cognition, cultural production, social interaction and cognitive tools, socio-cultural theory, and individual contextual differences.Through social interaction students learn social cognition and develop cognitive tools. Individual differences in socio-cultural contexts are influenced by those closest to you. Over time these differences are internalized, and affect your cognition, thought patterns, and views about the world. As learners, we are influenced by macrosystem factors outside our control. This includes societal, individual, classroom, and institutional differences in contexts and situations of learning. This can have many instructional implications, and calls for more place based and cooperative classroom pedagogies, Research has stated that situated learning has an increasing influence on education. The ecological model also states that in order to understand human development, one must consider the entire ecological system in which growth occurs. As discussed, recent research suggests that prospective teachers hold simplistic views of student differences. They have little knowledge about different cultural groups. In fact, they may have negative attitudes toward those groups, and view the diverse backgrounds of students as a problem, and have lower expectations for the learning of ethnic minority students. In the development of children, there are many social processes of interaction. These early interactions will shape and serve as a template, for future pro social behaviours. The social context can have various of influences on our cognitive development. Such as : intelligence, environment factors, learning opportunities, economics status, family and society. In order to be effective instructors, one must take into account the social-cultural perspective, and account for the social influences on cognitive development. Glossary

Acculturation: adapting the norm, behavior, skills, belief, language, and attitudes of a particular community[4].

Cognitive development: Cognitive development is a field of study in neuroscience and psychology focusing on a child's development in terms of information processing, conceptual resources, perceptual skill, language learning, and other aspects of brain development and cognitive psychology compared to an adult's point of view[4].

Dialectic contradictions: Historically accumulated structural tensions in a language. . Each child, has their own set of deliberate semantics. Therefore, words can have different meanings according to each child[15].

Ecological model: An ecosystem model is an abstract, usually mathematical, representation of an ecological system (ranging in scale from an individual population, to an ecological community, or even an entire biome), which is studied to gain understanding of the real system[7].

Exosystem: The exosystem refers to a setting that does not involve the person as an active participant, but still affects them. This includes decisions that have bearing on the person, but in which they have no participation in the decision-making process. An example would be a child being affected by a parent receiving a promotion at work or losing their job[9].

Macrosystem: The macro-system encompasses the cultural environment in which the person lives, in the larger sociological context. This level of the ecological model often influences students without them even knowing it, leading to implicit beliefs or beliefs shared by a culture. Examples could include the economy, cultural values, and political system[9].

Mesosystem. The mesosystem consists of the interactions between the different parts of a person's microsystem. The mesosystem is where a person's individual microsystems do not function independently, but are interconnected and assert influence upon one another. These interactions have an indirect impact on the individual. For example, the relationship between parent and teacher, can have an indirect impact on a students learning[9].

Microsystem: The system closest to the person and the one in students have have direct contact. Some examples would be home, school, daycare, or work. A microsystem typically includes family, peers, or caregivers[9].

Place based instruction: The environment in which we learn and situation in which the learning takes place, is responsible for co-creating our knowledge. A place based learning approach is suited for the multi-cultural classroom. It focuses on transforming the traditional classroom environment, into a place that is engaging for all types of learners[18].

Scaffolding: building of a students prior knowledge to learn new or difficult concepts[17].

Situated Cognition: A theory based upon principles related to the fields of anthropology, sociology and cognitive sciences. Its main argument is that all knowledge a learner acquires is somehow situated within activities that are socially, physically or culturally-based[4].

Social cognition: A subtopic of social psychology that focuses on how people process social information (especially its encoding, storage, and retrieval) and how this information is applied to social situations, other people, and social interactions[4].

Social Context: refers to the immediate physical and social setting in which people live or in which something happens or develops. It includes the culture that the individual was educated or lives in, and the people and institutions with whom they interact[4].

Zone of proximal development: The zone of proximal development, often abbreviated as ZPD, is the difference between what a learner can do without help and what he or she can do with help[17]. Suggested Readings

Bronfenbrenner, U. (1999). Environments in developmental perspective: Theoretical and operational models. In Measuring environment across the life span : emerging methods and concepts(1st ed., pp. 3-28). Washington DC: American Psychological Association.

Brown et al., (1989). Situated cognition and the culture of learning. Educational Researcher, 32- 42

Campbell, F. A., Pungello, E. P., & Miller-Johnson, S. (2002). The development of perceived scholastic competence and global self-worth in African American adolescents from low income families: The roles of family factors, early educational intervention, and academic experience. Journal of Adolescent Research, 17, 277-302.

Poch, S. (2005). Higher education in a box. International Journal of Educational Management 19(3), 246-258. doi:10.1108/09513540510591020

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes Cambridge, Mass.: Harvard University Press. References

Miller, S. A. (2010). Social-cognitive development in early childhood.interactions, 20, 21. Turner, J. C., Hogg, M. A., Oakes, P. J., Reicher, S. D., & Wetherell, M. S. (1987). Rediscovering the social group: A self-categorization theory. Cambridge, MA, US: Basil Blackwell, Inc. Smith, E. R., & Conrey, F. R. (2009). The social context of cognition.Cambridge handbook of situated cognition, 454-466. Brown, J. S., Collins, A., & Duguid, P. (1989). Situated cognition and the culture of learning. Educational researcher, 18(1), 32-42. Anderson, J. R., Reder, L. M., & Simon, H. A. (1996). Situated learning and education. Educational researcher, 25(4), 5-11. Smith, E. R., & Conrey, F. R. (2009). The social context of cognition.Cambridge handbook of situated cognition, 454-466. Bronfenbrenner, U., & Morris, P. A. (1998). The ecology of developmental processes. Cervone, D., Shadel, W. G., & Jencius, S. (2001). Social-cognitive theory of personality assessment. Personality and Social Psychology Review, 5(1), 33-51. Bronfenbrenner’s, U. (2011). YOUTH, Science TEACHING AND Learning. Böhmer, W. (2009). An investigation into the inclusion of child development in early childhood programs (Doctoral dissertation). Gay, G. (2000). Culturally responsive teaching: Theory, research, and practice. New York, NY:Teachers College Press Hollingsworth, S. (1989). Prior beliefs and cognitive change in learning to teach. American educational research journal, 26(2), 160-189. Maschinot B. (2000). The Changing Face of the United States The Influence of Culture on Early Child Development: 2000 M St., NW, Suite 200, Washington, DC 20036-3307 (202) 638-1144 Rogoff, B., & Morelli, G. (1989). Perspectives on children's development from cultural psychology. American Psychologist, 44343-348. doi:10.1037/0003-066X.44.2.343 Mahn, H. h. (2012). Vygotsky's Analysis of Children's Meaning Making Processes. International Journal Of Educational Psychology,1(2), 100-126. doi:10.4471/ijep.2012.07 Reunamo, J. J., & Nurmilaakso, M. (2007). Vygotsky and agency in language development. European Early Childhood Education Research Journal, 15(3), 313-327. doi:10.1080/13502930701679320 Thompson, I. (2013). The Mediation of learning in the Zone of Proximal Development through a Co-Constructed Writing Activity. Research In The Teaching Of English, 47(3), 247-276. Sloan, C. (2013). Transforming Multicultural Classrooms through Creative Place-Based Learning. Multicultural Education, 21(1), 26-32 Kana‘iaupuni, S., Ledward, B., & Jensen, U. (2010). Culture-based education and its relationship to student outcomes. EDUCATION. Clapper, T. t. (2015). Cooperative-Based Learning and the Zone of Proximal Development. Simulation & Gaming, 46(2), 148-158. doi:10.1177/1046878115569044 Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971 Atherton J S (2013) Doceo; Assignment Presentation Guidelines [On-line: UK] retrieved 2 March 2016 from http://www.doceo.co.uk/academic/assignment_presentation.htm#Referencing Lajoie, K& Azevedo, J (1992). Laughter and stress Humor, 5, 43-355. Dobbin, F. 2004. The New Economic Sociology: A Reader. Princeton, NJ: Princeton University Press. Social emotional learning (SEL) is the development of knowledge, skills and attitudes to effectively manage and understand emotions in social settings. SEL programs teach children to establish positive relationships while making responsible decisions in the school setting. SEL is intended to provide a foundation for socialization and achievement in school and later life. There are five competencies identified within SEL: self-awareness, self-management, social awareness, relationship skills and responsible decision making. These competencies enhance students' understanding of themselves and others around them. This chapter examines the theory, research and application of the five SEL competencies.

Self-Management

Self-management is the management of one’s emotions, behaviours, and thoughts in a variety of situations. There are three approaches to social emotional learning: positive youth development (PYD), critical youth empowerment (CYE), and sociopolitical development (SPD). The approach that relates to self-management the most is PYD. PYD uses a variety of activities and experiences to assist young people in building their social and emotional competence in the society. These activities and experiences allow young people to build an attitude towards their capability at different stages of their life. It is important to develop a positive attitude because attitude is the way of thinking or feeling that is reflected in one's behaviour. In order to maintain a positive attitude, one need to learn their capability on managing stress, motivating oneself, controlling impulses, and setting toward achieving personal and academic goals. In an educational setting, self-management is an essential component for young people to grasp. Stress is often the feeling that occurs to young people the most often in school. Self-management will benefit young people by preventing a mental breakdown and have methods of calming oneself. So through the PYD activities and experiences, young people can learn how to self-manage their social and emotional competence.

Managing Emotions

Emotions are an instinctive or intuitive feeling derived from reasoning or knowledge. Being able to manage emotions is important because it can either affect an individual’s behaviour in a positive or negative way. Every individual has different methods of coping with emotions; it just comes down to the individual's self-management skills. An individual first self-manages through learning how to manage stress, motivate oneself, control impulses, and set toward achieving personal and academic goals. Research has shown that stress is one of the factors affect a student’s level of functioning. Academic stress is when a student feels they lack the skills, emotions, and time to effectively perform a given task. Under stressful conditions, it is difficult for students to manage their emotions because majority of the time, they feel helpless about a task. Motivation can be one of the best methods to manage emotions. Motivation gives an individual the drive to set towards achieving their personal and academic goals. Throughout that process, an individual can maintain positive when they think about what their accomplished goal or the reward (if any) at the end of the goal. In a school setting, student motivation is called autonomous motivation. Autonomous motivation is undertaking an activity because of its meaningfulness and relevance. Students are more motivated to pursue activities that made more meaningful to them by their educators. It is called autonomous motivation because the educators will mold a classroom environment that allows students to make choices of their own in classroom interactions. According to a research done on social emotional learning skills such as motivation and managing stress, these skills are good indicators of future academic outcomes. The research was conducted towards high school students. The results showed that students who had lower social emotional learning skills academically scored in the bottom 25%, and students with high social emotional learning skills academically scored in the top 25%. Students who saw college as an important journey or goal in life was reflected in their grade point average (GPA) after their first year of high school. If there is a steady or significant increase in a student’s GPA, this means they had the motivation to work towards getting admitted into college. By improving students’ social emotional learning skills, students will become more self-regulated and engaged learners. Becoming self-regulated means to become autonomous by controlling their own emotions and behaviour. Self-regulated students will be able to effectively seek motivational goals to pursue. They will also be able to seek methods that can sufficiently cope with their stress.

Classroom Management

In an educational setting, classroom management is one of the contributing factors to students' self-management. Classroom management is the teacher's knowledge about student's behaviour and development, as well as developing strategies and practices that would assist students. With this knowledge, teachers can pass down the tools necessary for students to successfully manage their own behaviour. For students to gain the capability of managing their behaviours in a classroom, they must first learn how to regulate their own emotions. For example, if a student knows how to calm their own emotions and be patient, chances are they will be less disruptive in class. However, students are not the only ones who must learn how to regulate their own emotions. As an educator of the students, they must learn how to regulate their emotions before becoming a role model for the students. As a role model, the teacher demonstrates proper solutions on handling situations, as well as creating positive relationships with every student in the class. Creating positive relationships with the students will allow the teacher to understand them better. This way, teachers can develop better strategies and practices tailored to each student’s needs.

There are four principles of effective classroom management :

Four Principles of Effective Classroom Management	Details
1. Planning and Preparation	Teachers have a clear lesson plan for the day so transitions between activities will be smooth.
2. Extension of the Quality of Relationships in the Classroom	Creating positive relationships with students will decrease the possibilities of classroom disruptions.
3. Management is Embedded in the Environment	Teachers use materials to support their teaching routines (eg: using charts or pictures)
4. Ongoing Processes of Observation and Documentation	Teachers need to consistently reflect upon their management skills to see if it is working effectively.

The main purpose of these principles is to allow educators to gain the skills to prevent the worst case scenarios. This means being planning ahead of time so educators will not panic and handle the situations ahead of them. These principles are not to prepare educators on how to react, but how to prevent and build skills. Reaction is how the educator manages and expresses their emotions during a situation, whereas prevention will allow the educators think ahead of time and prepare for the worst. In doing so, this promotes organization and educators will have control over the classroom. A technique that could be used to gain control over the classroom is enforcing a daily routine. This routine could be used when transitioning between activities or to get the class to quiet down. For example, if an educator wants to get the students' attention, they could clap their hands in a rhythm and have the students follow. By doing so, this enforces positive behaviour from the students and students will manage themselves by reinforcing expectations. The clapping creates a positive behaviour and will be emitted by students in applicable situations. Also, having a particular transition between activities can create positive behaviour because it will make the classroom more predictable. For example, educators can use a particular song to end an activity to start the next. Students will get into the habit of this routine and manage themselves through reinforcing the positive behaviour.

Using these principles, students can gain autonomy through managing their own behaviour. These principles not only allow educators to gain control over their classroom, but students will have the opportunity to self-manage. To create a positive relationship with the students, educators will need to create boundaries and balance between warmth and discipline. Educators need to understand the degree of their discipline because going by the rules for everything will stray the students away from the educators. Discipline that are over controlling can cause educators to be inflexible and unresponsive to student needs. There should not be a determined discipline because every year, there will be new students in every classroom. The discipline should be modified based on the needs of the students so there will be opportunities for students to learn the skills to self-manage.

Self-Awareness

Self-awareness is assessing one’s emotions and thoughts and its impact on behaviour. One of the three approaches to social emotional learning, sociopolitical development (SPD), connects to self-awareness. SPD is the critical reflection of young people that help them see and understand structures, social values and practices that they may be struggling with. Critical thinking will assist young people on realizing what their weaknesses are. Self-awareness allows the young people to determine their strengths and weaknesses, as well as maintaining a positive attitude and confidence. This is especially important in an educational setting because young people need to understand their capabilities to set goals for themselves that are not out of their limits. Figuring out what one’s strengths and weaknesses are can influence emotions and thoughts either a positive or negative way. If one is struggling with their weaknesses, this could result with frustration, anger, or any negative emotions or thoughts. This will also lead up to negative behaviour. In an educational setting, educators need to understand students’ weaknesses so they can scaffold alongside to turn them into strengths. This will be beneficial with students’ self-awareness.

Morals and Values

On one hand, morals are a person’s standards of behaviour involving their definitive belief about what is acceptable and what is not acceptable for them to do. It is crucial for people to develop morals because they establish a set of rules for themselves based on their belief between right and wrong. Having morals will provide a person with directions, guiding them towards more positive decisions and preventing themselves from negative choices. This works in with SPD through the critical reflection that one must go through. SPD seeks out social values, structures, practices, and processes that need to be altered. A person with morals can easily seek out those social factors that do not fit in with their beliefs. Setting a set of ground rules allows an individual to determine whether their emotions and thoughts are generating a positive or negative behaviour.

On the other hand, values are what are important to an individual. Values and morals work to build on each other. Morals determine what is acceptable and not acceptable in an individual’s perspective, and values determine what is important. Values will trigger the emotions in an individual because a value sets an importance on an object, a person, a place, etc. in the individual’s life. Values can give an individual confidence and optimism in life because these values act as a motivation for the individual. Motivation is a factor that will benefit young people in schools. Motivation gives people a reason to do things because it interests them. Usually, an individual will develop motivation for a task because they can get something out of it (eg: a reward). The reward they get out of a task could be of some value of theirs. Thus, having values can also be used as a motivator for people.

SECURe

SECURe (PreK) Strategies and Routines

Researchers have come up with a school intervention called SECURe. SECURe stands for Social, Emotional, and Cognitive Understanding and Regulation in education. This intervention is used in primary education to assist with three skills: cognitive regulation/executive function, emotion processes and interpersonal skills. SECURe uses games and songs to teach these skills, such as using a storybook to identify the emotions of the characters. The educator would then teach a method called, "I Message" . This method teaches students to express their emotions to their classmates. For example, if a student is upset with their classmate because they were calling them names, then the student would speak up to their classmate and say, "I am upset because I do not like being called names". I Message is beneficial in assisting students to become self-aware because this method allows students to regulate their emotions to discover how they were feeling and why they felt that way. By becoming self-aware, students can regulate their emotions and communicate in a calm manner to their peers about how they feel. This reduces the chance of students acting in an irrational behaviour that could lead to negative consequences.

Another component of SECURe is creating daily structures and routines because this provides opportunities for students to practice skills in recurring interactions and relationship-building activities. This is mainly for students in prekindergarten and/or kindergarten. These students have just started interacting with other students their age so creating a routine is very beneficial. Creating a structure or routine will give them the basic understanding of which behaviour to use in certain situations. Grasping this component of SECURe will enable them to move further as they progress and eventually self-manage.

Social Awareness

Social Awareness Refers to
Being aware of others
Understanding that others have feelings
Knowing that YOUR actions affect others

Social awareness is the student's ability to express and control their thoughts and emotions in different situations. Developing the student's ability to self regulate their thoughts, emotions, attention and reactivity is a key goal of SEL. Through learning social awareness strategies, students can identify which emotions are appropriate to display in different social events. For example, students know how to regulate their behaviors inside a classroom compared to a formal event such as a wedding ceremony or funeral. As students continue to develop frameworks on how to behave in a formal setting compared to a casual setting, students demonstrate more behaviors aligned with the social norm.

Through becoming socially aware of one's surroundings, students also learn techniques in how to remain motivated and focused on a given task within the classroom. For example throughout the school day, students can learn how to improve their level of motivation and focus as teachers encourage them to practice mindfulness techniques which refers to being consciously aware of how one is feeling physically and emotionally at that present moment and accepting those emotions. Research has shown students who are mindful of their emotions are more socially aware of how to regulate those positive and negative emotions For example, when students are feeling stressed and angry, being mindful of their current emotional state allows students to reflect on how they are feeling and encourages regulation of their emotions through talking about their feelings, or accepting their emotional state and relaxing. Social awareness also refers to the student’s ability to see situations in different perspectives. This teaches students how to be respectful, and open minded when being introduced to new situations with different challenges such as transitioning into a new school, classroom or having to work with new people. These new situations allow students to become more aware of one's surroundings as it also encourages students to be accepting of diverse point of views. If these skills are not practiced within the classroom, these transitional situations would lead to chaos as individuals will not understand the importance of compromising and integrating ideas from both the sides of the relationship. For example, teachers can demonstrate social awareness within the classroom by incorporating the student's ideas when creating classroom rules and boundaries. This demonstrates social awareness as the students are encouraged to speak up and share their perspectives on situations in which the teacher will take into consideration. This demonstrates social awareness as there is a level of compromise and integration of ideas when creating classroom standards and rules. These types of relationships leads students to build a trusted relationship with their teacher which allows the student to be less at risk of developing social and emotional regulation problems as the students learn new strategies in how to be open minded to different ideas . Through being open minded, students learn compromise helps to resolve social, emotional and physical problems. For example, if there is a conflict between two friends, if both individuals demonstrate social awareness by listening to the perspective of the other individual, it is more likely that the conflict will be resolved sooner as both sides of the relationship shares their ideas while listening to the other.

Mindfulness brings many advantages to students Physically, Emotionally and Mentally

Physically	Emotionally	Mentally
Students report feeling less fatigue	better emotional regulation	Better attention span
Improved sleep cycle	teaches students to "think before acting"	better memory capacity
Lowers blood pressure	feeling less stressed	higher academic performance
Helps relieve physical tension	teaches relaxation techniques	less substance use and depression

Gestures

Gestures are the ways in which children learn to express how they are feeling through physical hand motions and body movements. These methods of learning can be integrated into the classroom setting by teaching students ways in which they can express their emotions through words and showing their emotions through their their hand gestures. For example, when teachers ask students how everyone is feeling today from 1-5 (1 being bad), students should learn to express their emotions through hand gestures not simply holding their emotions into themselves. When students do not practice skills in expressing their emotions through gestures, they are more likely to develop temperament as these students may internalize all their emotional expressions . Gestures help students to develop more efficient ways in communicating their thoughts and feelings which may be unclear for teachers and peers.Gestures can also be used to teach students new information. For example, when learning their colors, body parts and letters, students can learn these information through songs, videos, and hand gestures such as Head, Shoulders, Knees and Toes or ABC. Through learning these songs, hand gestures and body movement, students can retain the information in a fun and interactive way allowing the students to be more motivated and engaged to learn new information. Gestures also teaches students strategies in reading and understanding symbols in different situations. For example when seeing a "quiet" sign in the library, students will know they need to remain silent inside the library, taking into consideration the other people who maybe studying and trying to focus. However, some gestures or symbols have more than one meaning. For example raising our hands in class demonstrates the student has something they want to share. On the other hand, raising our hands while crossing the street shows a different meaning as it represents manners to the driver. Students can learn which gestures are appropriate for certain situations when the teacher demonstrates the meanings behind these gestures through "role playing" in which students and teachers act out situations helping to demonstrate which gestures are appropriate for certain situations.

Relationship Skills

Relationship skills are the strategies students use to build and maintain positive relationships among peers and surroundings. When building positive relationships, researchers often wonder why individuals chose to create friendships with certain people but not others. Researchers wonder whether creating relationships has to do with personality traits, physical abilities, socioeconomic systems, intelligence, or other features. Overall, students build positive relationships as they learn to communicate their thoughts and feelings in a positive and healthy way through using emotional regulation. Learning these techniques allows students to become more open minded to diversity within the classroom as they learn to interact with all peers regardless of their age, gender, size or ethnicity. When these skills are developed at a young age, students built upon these frameworks on how to build and maintain relationships in the future with their co-workers, family members and their partners, as students are able to identify which relationship strategy worked and didn't work while they were in school. Overall, students who show better acceptance by their peers often demonstrated more admirable qualities within them such as being friendly, intelligent, attractive and athletic. To add on, these students were shown to be more successful in the future facing less emotional problems such as depression and social anxiety disorder .

Bullying

Bullying is one of the most common issue within all school environments but can be difficult to identify due to the several different methods of bullying that takes place. Bullying can be done directly (hitting, pushing punching), or indirectly (verbally abusing someone through name calling, isolating). Two main reasons for bullying others include alleviating boredom/creating excitement and to split up friendship and group processes. Bullying is common within the classroom as students choose to reject and/or "pick on" students who look more vulnerable and seem to be easier targets. In general, researchers show females to be more verbally victimized whereas males report being bullied more physically These situations affect children emotionally as they feel alone, misunderstood and are scared to speak up and seek help from an adult due to the believed consequences behind their actions. However, not speaking to a trusted friend or adult only makes the situation worse as bullying is often a destructive process as the bully continues to become stronger within the relationship while the victim becomes weaker During these situations, teachers need to step in and teach students the effects of bullying how it can lead to depression, isolation and withdrawal within the victim . The teacher can bring more awareness of the effects of bullying by incorporating role plays of different bullying situations, or having professionals come into the classroom and speak about the consequences behind bullying and the importance of speaking up when one is involved in a bullying relationship. Through these involvements, the bullies are more likely to see the situations in the perspective of the victim, as they learn ways in how to create and maintain an equal respectful relationship with their peers.

Building Relationships

Building relationships centers around student’s ability to learn how to create and maintain positive relationships inside and outside the classroom. It is evident that certain students have better relationship building skills compared to others, however, the true and main reason behind their advanced skills is still in research. It can have something to do with the student's cultural family background; the student's peer groups; personality characteristics and much more. Nevertheless, learning these skills at a young age teaches students appropriate strategies to use when building relationships with future peers, partners and co-workers. Students learn that the way they talk with their surrounds should be altered when interacting with people who are older than them such as teachers and parents. For example students should should show respect to older people by constantly being open minded towards receiving positive and negative feedback. If students chose to talk with adults like how they interact with their peers such as saying "what's up" or "how's it going"? teachers and adults can be lead into the perspective that this student is extremely rude and should be better educated. In order for these situations to be avoided, research has shown that students learn better when they are shown ways in how to build positive relationships. Therefore teachers should step into the classroom modelling positive relationship building techniques such as demonstrating how to share, be respectful, and be welcoming for diversity. For example, when providing students with snacks, teachers can demonstrate how giving all students 1 piece is the fair thing to do as everyone gets the same amount. To add on, teachers should model the different levels of acceptable interaction between one's peers compared to adults they know. Through modelling these behaviours, students learn to modify their behaviour and create positive and long lasting relationships with their peers and surroundings .

Responsible Decision Making

Responsible decision making is the student's ability to construct responsible choices about their personal behaviours and social interactions. For students to develop these skills they need to consider various questions such as, how would this decision benefit me? what would be the consequences behind this decision? who will it impact? These question, choices and behaviour are often guided by the individual's pre-constructed ethical standards, such as safety concerns, social norms and the evaluation of consequences behind performing these actions. These responsible decision making techniques are often guided by cultural and religious beliefs.

There are two main different cultural point of views known as:

Collectivist	Individualistic
We Oriented	Me Oriented
Blending in	Standing out
Belonging	Standing out
Group Goals	Individual Goals
Cooperation	Competition
Group Support	Self Reliant

For example, cultures that emphasize individualism (US, Canada, Australia), chose to make decisions based on what they believe would benefit themselves the most, whereas collectivist communities (Asia, Latin America) emphasizes the importance of group harmony instead of individual decisions . These cultural differences affect the student's level of moral decision making even at the young age of 4. In the CBC video babies born to be good?, researchers conducted an experiment where researchers recruited students under the age of 5 to test their level of moral reasoning. All students showed diversity in age (4-5), ethnicity and gender. In each of the situations, the experimenter left one student in a room (1 on 1) full of mess. When the experimenter left the room to grab a clipboard, all the children chose to clean up the mess to help the researcher. Before conducting the experiment, researched believed students coming from collectivist communities (Asia, Latin America) will lie in order to not receive credit for helping the researcher whereas individualistic communities would be honest and take the credit for the job being done.The results confirmed the hypothesis as researchers found students from individualistic communities didn't mind “standing out” and receiving acknowledgement” whereas collectivist students preferred “blending in”. These cultural differences can be present upon individuals as they grow older due to their different moral and ethical values. For instance, when a student receives a job offer, individualistic communities would encourage the students to make the decision based on how the situation would benefit the individual whereas collectivist communities emphasize putting their group unity before their individual choices. Despite the different cultural perspectives, in order for responsible decision making to take place, individuals need to keep in mind how this decision would affect themselves as well as their surroundings. Through using responsible decision making, students learn to become critical thinkers as it introduces them to the importance of "thinking before acting". Teachers can integrate these aspects of learning through reading short scenarios of a story and by asking students questions on what would be the responsible thing to do next? or What action can lead to a big consequence? These scenario acting techniques help students learn strategies in regulating their actions to fit their ethical beliefs.

Social Behaviour

Social behaviour looks at how individuals interact physically, emotionally and socially in different situations. This includes looking at how individuals interact through verbal face to face conversations or talking through a phone or electronic device. Social behavior also refers to physical interactions through holding hands, linking arms, hugging, etc. An individual's level of social behaviour is highly correlated with their past experiences. For example, when a child is highly neglected by their parents, they are more likely to display aggression inside the classroom due to the fact they were mostly rejected by their primary caregiver. . These negative interactions guided students to believe this world is an unsafe place therefore, they become highly defensive in new situations as they choose to reject new peers and teachers. During these situations, teachers need to step in and make the child feel safe and comfortable within the classroom, by integrating positive reinforcements such as complementing the student on their work, or providing feedback on how the student can improve their learning and understanding. Through these levels of interactions, the student often becomes less aggressive in different situations as the teacher helped to restructure the students understanding of the world to be a "safe environment" altering one's morals and shaping their interactions with their peers and parents.

Teamwork

Teamwork is build within the classroom when students acknowledge the importance of collaboration of different works and ideas to make it better. Through using teamwork strategies, students learn ways in feeding off of each other through learning ways in getting their points and ideas across while listening to the opinions and feedbacks of other individuals. For example, when students work on group assignments, they often split up the work evenly and collaborate their ideas in the end. This allows the work to become more developed as it integrates different perspectives of the situation into one assignment. When there is a disagreement within the group, students learn more teamwork strategies by compromising and being respectful towards the ideas of their peers. However, if these skills are not practiced, teamwork situations would become chaotic for learners as well as teachers and students will struggle to regulate their ideas, emotions and relationships. Nevertheless, when teamwork is practiced inside the classroom through doing group projects, or playing team sports, children learn ways in building neutral and respectful relationships in the future. For example through playing a team sport, students better understand that in order to run a company, there needs to be different individuals having different roles to run the company however, group collaboration is important for the company to be successful.

Conflict

Individuals often face social and emotional conflicts inside and outside the classroom setting. The individual's ability to deal with these conflicts are highly dependent on their previous experiences resolving conflicts and is often shaped by their beliefs of social norms and ethical beliefs. For instance, at a young age, students are often unclear on social norms on how to resolve a conflict as they may have not been exposed to these situations. These students may lack morals therefore, may believe the best way to resolve a conflict is fighting back and becoming defensive. During these times, teachers can show students the consequences behind fighting back as it only makes the conflict worse. Teachers can then teach students ways to resolve the conflict through talking about the problem as students may have had a miscommunication. Many individuals also face emotional conflict such as feeling lonely, rejected, mad, and sad. During these situations, teachers should guide students in having a conversation about what they are feeling and why they are feeling this way. Research has shown students who resolve emotional conflicts through talking about it, lead the student to become more emotionally and socially stable in the future

Glossary

Academic Stress: An academic task is perceived as stressful by people who do not feel as if they have the skills, or emotional or time resources, needed to effectively manage a given activity.

Aggression: The practice of making assaults or attacks; boldly assertive.

Alleviating Boredom/Creating Excitement: Picking on an individual to make their lives more fun, eventful and interesting.

Autonomous Motivation: Engaging in an activity because of its perceived meaningfulness and relevance.

Collaboration: to work, one with another; cooperate, integrate ideas

Collectivist Perspective: view point of Asian, Latin American communities emphasizing importance of group collaboration, group unity, and group belonging.

Critical Youth Empowerment (CYE): Focuses on collaboration and connection through various models of youth empowerment.

Destructive process: as the bullying continues, the power imbalance becomes greater because the bully continues to grow stronger as they figure out more vulnerable aspects of the victim making the victim weaker and an easier target.

Diversity: The inclusion of individuals representing more than one national origin, color, religion, sexual orientation, etc.

Emotional Regulation: The child's ability to monitor their behaviour in different situations.

Ethical standards: Perception of what is morally right and wrong, and their reasoning behind beliefs.

Individualistic Perspective: view point of Canadians, Americans, Australian citizens emphasizing the importance of independence, having privacy, being self oriented, etc.

Motivation: The reason for acting or behaving in a particular way.

Neglected: Given little attention to, fail to show care.

Positive Youth Development (PYD): Using activities and experiences to assist young people in developing social, moral, emotional, physical, and cognitive competence in their community.

Reactivity: How the individual responds to the environment.

Scaffold: Process through which educators guide children along their emerging abilities.

SECURe: Social, Emotional, and Cognitive Understanding and Regulation in education.

Self Regulation: Child's ability to control their reactivity in different situations while controlling their emotion and attention.

Sociopolitical Development (SPD): Promotes an understanding of the cultural and political forces that shape one's societal status by emphasizing the acquisition of practical, analytical, and emotional faculties to act within political and social systems.

Split up friendship and group processes: Convincing others to not hangout with certain people due to having undesirable qualities.

Symbols: Something used for or regarded as representing something else; Can include words, images, phrases to represent another object.

Recommended Readings

Ann Sanson, S.A. (2004) Connections between Temperament and Social Development: A Review. 143-170.

Jackson, Cassandra McKay. (2014). A Critical Approach to Social Emotional Learning Instruction Through Community-Based Service Learning. 292-312.

Weinstein, C.S., Romano, M. (2015) Knowing Your Students and Their Special Needs. 110-145.

References

< ref name="&&&&">@@@@</ref > < ref name="&&&&" / > < ref>B. Paul, Kinematics and Dynamics of Planar Machinery, Prentice-Hall, NJ, 1979</ref >

Activity Theory

The Development of Activity Theory

Activity Theory (AT), often referred to as Cultural-Historical Activity Theory (CHAT), is a broad cognitive learning theory that emphasizes complex mediation of action and interactions between individuals (subjects), objects, contexts and communities. It is often considered as a conceptual lens through which to consider a human activity more in-depth, providing “the tools for revealing the social and material resources that are salient in activity.” Activity Theory was developed by revolutionary soviet psychologists Lev Vygotsky, Alexi Leont’ev and others during the 1920’s and 1930’s in response to earlier theories of learning including behaviorism, psychoanalysis and reflexology. Vygotsky’s focus was to draw on his earlier work in cultural-historical theory to determine how culture and language mediate between a stimulus and the response. AT also differs from Piaget’s theories, as it sees cognitive processes as influenced and mediated by external artifacts and influences, rather than considering them as separate. Basically, AT focuses on activities undertaken by subjects (human agents) who are motivated toward some objective (object); their activities are mediated by tools or mediating artifacts (which can include symbol systems and language). This can take the form of a person (subject) using a can-opener (tool) to open a can (object), or of a child (subject) using words (tool) to ask for a cookie (object). Tools / mediating artifacts can be more abstract, including mathematical algorithms or the knowledge of a process, and are considered separately from, but also mediating the subject and its motivations. This relationship has often been portrayed using a triangular diagram; pictured first is Vygotsky’s original theory (stimulus (S), Response (R) and “a complex, mediated act” (X)); the second image presents a more common version; subject, tool/mediating artifact, object. The following example demonstrates this and suggests Leont’ev’s further contributions to follow.

Vygotsky and Leont'ev's Early Activity Theory

Example: Leont'ev’s "primeval collective hunt"

"a beater, for example, taking part in a primeval collective hunt was stimulated by a need for food, or perhaps, a need for clothing, which the skin of the dead animal would meet for him. At what, however, was his activity directly aimed? It may have been directed, for example, at frightening a herd of animals and sending them towards other hunters, hiding in ambush. That, properly speaking, is what should be the result of the activity of this man. And the activity of this individual member of the hunt ends with that. The rest is completed by other members. This result, i.e. frightening of the game, etc., understandably does not in itself, and may not, lead to satisfaction of the beater's need for food, or the skin of the animal. What the processes of his activity were directed to did not, consequently, coincide with what stimulated them, i.e., did not coincide with the motive of his activity; the two were divided from one another in this instance. Processes, the object and motive of which do not coincide with one another, we shall call "actions". We can say, for example, that the beater's activity is the hunt, the frightening of the game the action." In this early example, the subject is the hungry hunter, the tool/mediating artifact is the hunter’s specific activity (frightening the animals) and the object is the food and clothing resulting from the successful hunt.

Over time, more elements were elaborated in this model which better represent the complexity of each of the three components above. Recently, Yrjö Engeström summarized this development of AT across three “generations” with Vygotsky and Leont’ev’s foundations above as the first and second. Both of these focused mainly on children and learning, and Vygotsky put much emphasis on language as the dominant mediating artifact. The third and current generation of AT began in the 1970’s, and focused on an increasingly complex range of relationships between the individual and his or her activities with various communities, including the workplace. New areas of focus such as cross-cultural interactions and networks of interacting Activity Systems are central to contemporary activity theory. Differences of tradition, language and cultural contexts add much complexity, and remains an area of active research. In the diagrams below, the activities are now constrained by the rules/conventions of the context; they exist within communities, and they must consider the division of labor (social strata). These elements are all interconnected, meaning that each one is influenced by each other. One exception is the outcome, which may occur separately from and differ from the object of the activity. The object is also considered to be a “moving target” because it is dependent on several variables. The second image includes object 3, which is an outcome that is only possible when two or more subjects perform some activity to that end, each with an Activity System in play. The inclusion of many communities and subjects creates a complex challenge for AT theorists.

An individual Activity System, and two interacting systems

Example: A Teacher’s Decision-Making

The following is a slightly shortened example that demonstrates the added components in the above Activity System diagrams: Roth and Lee provide a fictional example of Katherine, a fifth grade teacher in a rural district who has taught her group of students in previous years. She decides that a unit on electricity would be best taught through hands-on experiential activities, but feels pressure from her school board towards accountability. She instead decides to rely on a canned direct-teaching method in order to ensure economy of instructional time, under assurances of mastery learning and higher achievement scores. During the week, she sees excited faces slowly dim, though she finishes the unit in time. She is disappointed at the apparent disengagement, and consoles herself that they will be fine in the end, and that they will do some fun experiments another time.

The subjects interacting here are Katherine and each of her students. The desired outcome is to learn the prescribed content satisfactorily. Katherine’s activity included direct teaching, influenced by rules set by members of her educational community, and the object was to complete the unit of instruction in time. While she was successful, the outcome of good understanding and engagement was not achieved by most students, who have their own Activity System at play. Their Activity System includes rules within a classroom community and their position as students; their activity was limited to listening, taking notes, responding occasionally and their object was to successfully complete instruction led by their teacher. Their activities were directed also by the educational community that put pressure on their teacher.

The missed opportunity in this example is that Katherine did not have confidence enough in her theoretical defense of experiential learning; even though it would have provided for richer interactions in the activity of her students and ultimately led to a better learning outcome. Both groups did achieve their objects, but because the outcome is variable based on all subjects’ mediated activities, it was less than ideal. Following Activity Theory, if Katherine or another teacher were to lay out their desired outcomes, their community influences, rules, and divisions of labour, she would be able to point to features that show that her objective (completing a unit on time) will not lead to the desired outcome. She may need to leverage her professional and personal knowledge of her students as well as her school community in order to influence her administration to accommodate and validate her methods in way that satisfies their standards.

Engeström lays out five principles that characterize contemporary (third generation) AT. The first is that the unit of analysis is the Activity System described above, though it may change or adjust to groups activities. The second is that Activity Systems are multi-voiced, where multiple points of view, traditions and interests are presented according positioning within the division of labor, and following various historical layers/strands; all of which is multiplied in networks of Activity Systems. The third is historicity; Activity Systems must be understood within their own history, as the local history of an activity and its objects changes how they affect other components; to understand medicine in a given area, one must consider the history of local medical organizations and the global history of medical concepts, procedures, tools, etc. The fourth is the central role of contradictions as sources of change and development. This refers to accumulating tension (rather than conflict) that arises with the introduction of new elements and generates attempts at adaptation or change of a given Activity System. The fifth is the possibility for expansive transformations in Activity Systems. As contradictions lead to tension and change, some members may reach out to other Activity Systems or deviate from norms, resulting in a larger collaborative effort to reconceptualize and reform some larger societal activity.

Application: Activity Theory and Human-Computer Interaction

An example of contemporary application of AT is in the field of Human-Computer Interaction (HCI). Bonnie Nardi’s book Context and Consciousness highlights several examples of fruitful application of AT to HCI. Nardi begins with a proposition that rather than HCI, the term computer-mediated activity be used. The focus can then shift from the computer separated from the human by an interface, and instead dissolve this, incorporating contextual elements as well in mediating the activity. The focus on the Activity System is useful in designing electronic tools to suit a given activity because it provides a new perspective on the nature of the activities, objectives, and mediating artifacts involved. It also directs attention systematically to the other elements in the Activity System such as the division of labour, community and rules and conventions. Nardi discusses the implications for the design of educational technologies, which are similar to the design of any instructional tool. That is, in order to achieve an objective (learning x or y), an activity or tool must be developed while considering the whole situation as well as the end users and their motivations. The design is usually also iterative, using prototypes in the usage context, with students and teachers and curriculum. In this way, the external influences on a student must be well-understood in order for the student to internalize (learn) what is expected (the objective).

Distributed Cognition

Distributed Cognition (DCOG) is a theory that suggests that certain cognitive processes may be externalized into certain objects, processes, or other individuals in a group or team. This makes a distinction made between traditional cognition taking place in an individual’s head and Distributed cognition, which may take place partly or mostly outside of the individual. In the case of objects, processes or technologies, there is an additional distinction between cognizers, who initiate new cognitive activities (thinking, understanding, knowing) and those cognitive helpers who are tasked with some element of it. Additionally, some internal (e.g. mental representations) elements can be considered as examples of DCOG alongside external ones (e.g. a scientific poster or a calculator). We will now consider aspects of DCOG as they relate to specific groups, technologies and processes through a series of illustrative examples.

DCOG in a Notebook

Clark and Chalmers’ (1998) present a popular example of Otto’s Notebook. They compare two people who need to find their way across town to an art exhibition; one relies only on memory to reach her destination, and Otto, who suffers from Alzheimer’s disease and relies heavily on a notebook to remember the date, location and directions to the same exhibition. The example illustrates how we are quite used to relying on information stored in our environments to support our own memory systems. Many people with memory deficits carry a notebook in order to record and retrieve information in a way that others would do using their long-term memory. The authors present this story as part of an argument for an extended mind, with external artifacts considered as on-par with normal cognitive processes, rather than separate from them. This case presents the notebook as an extension of memory. It is an effective example because many people frequently use similar tools, and increasingly use digital tools for the same ends (camera-phone, voice recording, webmail etc.).

DCOG in a Sports Team

Williamson and Cox (2014) present an example of a sports team. They compare a team of individual experts to another, expert team. They refer to the first as an aggregate system, and the expert team as an emergent system. The performance of the second team may be greater than the sum of its parts, just as the first team may look good on paper but underperform. Shared knowledge and skill (embodied and declarative) is important to the success of the expert team, and is often based on a shared history. It is possible for teams to “gel” quickly if members are compatible. This approach to DCOG is also found in other collaborative domains including music, dance, surgery and work teams. Each case involves coordination of many individuals’ skills, intentions, patterns of action and cognition. Also important is a sharing of affect or mood, which can also lead to a shared drop in morale, which negatively affects performance. Cohesion in an expert team is maintained through verbal communication, body language and performance. Finally, members share their collective and individual knowledge of the game, their own skills (some intangible ones), others’ skills, and knowledge about what others know about them. This shared knowledge, supported by an affective community allows each player to rely on the whole team in order to attain their shared objectives. In this way, there is a network that can distribute cognitive processes and activity in a fluid and highly efficient manner.

DCOG in an Airline Cockpit

Hutchins and Klausen describe a commercial airline cockpit as an example of DCOG, in which interactions between internal (the pilot’s thoughts) and external knowledge representations (controls, instrumentation). Considering the movement of information through such a complex system, the authors determined that the expertise within the system resides also in the organization of tools and the work environment itself. The shared cognitive processing emerges when tools, information and human activity are efficiently combined to create a system that is not dependent on individual pilot skills. Success is dependent instead on socially distributed tasks with the complexity and support required changing based on need (communication with an air-traffic controller before landing, takeoff, maintenance checks, etc.). As in the Sports example, parallels to other work environments can be made; where the arrangement of tools, protocols and human activity are clearly defined (as in medicine or collaborative technical work), DCOG provides a useful lens though which to examine cognitive tasks and the roles of support systems in order to optimize efficiency, ensure appropriate redundancy, or other improvements to a given process.

DCOG in Scaffolding

Belland (2011) examines the concept of scaffolding through the lens of DCOG, with Problem-Based Learning using computer-based scaffolds as a focus. The support provided by a scaffold can rightly be called distributed cognition, but it differs in that scaffolding is meant to “fade” as responsibility is transferred to the learner. Additionally, scaffolding is often human-dependent, as dynamic responses to learner needs are difficult for computers to reliably accomplish. The effect of removing scaffolding too early may be hazardous, and allowing the tool to remain may prevent the formation of new schemas. Therefore, where DCOG is used to help design a scaffold, it is most useful to scaffold in a way that supports the learner in generating their own schema, accomplishing some task that the learner need not internalize (using a ruler and calculator to draw an enlargement grid for an art project). Further, if the scaffolding does use a tool as in DCOG, that tool should be easily available for use if needed to solve that problem in the future. In this way, certain scaffolds may remain in place in order to augment the learner in transfer contexts, or remain until the learner has internalized their functions.

DCOG and Computers - The Cognitive Commons

Many examples of machine-supported information retrieval and learning have been presented over time, but the personal computer and the Internet have provided many examples of distributed cognition in everyday contexts. Much like Otto’s notepad, many people regularly consult digital calendars, notes, emails online search engines for timely information for a wide variety of cognitive tasks. Search engines provide one of the best examples, with effective algorithms and enormous amounts of data combining to make a tool that is relied upon to assist in many tasks, from trip planning to social networking.

Clark and Chalmers present a popular example of a brain implant, in which a normally external technology is able to interpret and respond to stimuli inside of the "skin and skull". This is an example of DCOG, but also what the authors refer to as extended cognition, or active externalism because of the potential for a coupled system which may be considered a cognitive system in its own right. Personal electronics such as smartphones are a limited form of this kind of extended cognition (slower, external); the authors describe the element of portability being central to the popular conception of cognition, which is partly addressed by ubiquitous and portable technology.

Dror and Harnad (2008) describe the concept of a cognitive commons, in which the Internet is a persistent and dynamic aid to many cognitive processes, and a common space for people to share cognitive tasks. They draw attention to the term cognizing as the act of thinking, understanding and knowing things, as a mental state which is not present in technologies that may support cognition. However, when cognizers, with performance capacity extended through DCOG with various tools, apply language and networking on the Internet, a cognitive commons is possible, similar to Engeström’s expansive learning theory. The cognitive commons describes not just the place for thinkers (cognizers) to interact, but also how their interactions online, combined with online tools such as search engines, allow that community to accomplish greater cognitive goals, much like a larger, distributed expert sports team, performing better than the sum of its expertise.

Metacognition and Self-Regulated Learning

This chapter introduces the basic concepts of metacognition and self-regulated learning, explores how learners take an active role in their own learning through self-regulation. We examine the different models of self-regulated learning (SRL). We discuss the theory of metacognition and SRL and show how these fundamental cognitive processes drive learning in academic settings, as well as how to facilitate SRL in the classroom.

After reading this chapter, you will learn:

• The concept and major models of SRL.
• The concept of metacognition and its importance for students to reconstruct knowledge and manage their learning strategies.
• The major factors that affect SRL and metacognition.
• How learning analytics promote research in SRL.
• How technology can facilitate SRL.
• The four stages in the development of self-regulation, and the four types of SRL strategies.
• How to Facilitate and encourage SRL in the classroom.

Figure 1. Metacognition and Self-Regulated Learning

Defining the Concepts

Figure 2. Defining the Concepts

Definition of Self-Regulated Learning

Self-Regulated Learning (SRL)means that learners have ability to monitor and control their own learning processes ; it is concerned with the learners’ use of different cognitive and metacognitive strategies to control, monitor, and regulate their cognition, behaviour, and motivation in their learning. Learning in a self-regulated way, learners can set their own learning goals, control their learning processes, and motivate themselves when they participating, in order to achieve their goals . In a SRL environment, learners can be more active and efficient for their learning performance and behavior to improve their final learning outcomes. Self - regulated learners have abilities to change and develop their own learning strategies based on self-understanding and examine their learning through constructive activities, collaborative work, and free exploration. SRL is a cognitively and motivationally active approach to student-centred learning.

As “a behavioural expression of metacognitively guided motivation” (Winne & Baker,2013, p.3), the process of SRL assists learners in managing their thoughts, behaviors, and emotions in order to successfully navigate their learning experiences. This process requires learners to independently plan, monitor, and assess their learning.

According to Zimmerman (2002), SRL can be broken down into three phases during learners’ cognitive and behavioral activities: the forethought phase, the performance phase, and the self-reflection phase. The forethought phase (self-assessment, goal setting, and strategic planning) involves analyzing the learning task and setting specific goals toward completing that task. The performance phase (strategy implementation and strategy monitoring) takes place during learning, and self-reflection phase can be the evaluation of learning outcome.. By adopting this method, learners can be better understood through viewing specific strategies which they use to engage in their own learning. The large scale structure of self-regulated learning is as follows and the detailed explanation will be provided in later section of this chapter.

Definition of Metacognition

Metacognition is one of the key components in self-regulated learning, which involves cognitive thinking and regulation of thinking. Learners who have metacognitive ability, can be able to monitor, control, regulate their own learning. In this section, we will look at how the definition of metacognition has evolved.

In 1979, Flavell first introduced the concept of metacognition in his research. The concept of metacognition can be related to various aspects in learning process, which includes reading, writing, planning, and evaluation. Both monitoring and controlling of cognition are two basic functions served by metacognition. In 1980, Ann Brown provided a definition of metacognition, which not only majorly address on the relationship between knowledge and regulation of cognition, but it also the first time brings up the word “regulation”. Recently, the concept of metacognition has been mentioned in so many research and usually divided into three components:

Metacognitive knowledge also called metacognitive awareness. As cognitive processors, each individual learners should know about themselves, tasks, strategies, goals, and other relevant information. There are three different types of metacognitive awareness, i.e. declarative knowledge, procedural knowledge, and conditional knowledge.

Metacognitive experiences are “what the person is aware of and what she or he feels when coming across a task and processing information related to it”. It is very important in self-regulated learning because it allows learners to make attributions about their feelings and adjust their own goals.

Metacognitive skills/strategies are the “deliberate use of strategies (i.e. procedural knowledge) in order to control cognition, which include orientation strategies, planning strategies, strategies for regulation of cognitive processing, strategies for monitoring the execution of planned action, and strategies for the evaluation of the outcome of task processing”. Similar to metacognitive knowledge, metacognitive regulation or "regulation of cognition" contains three skills that are essential: planning, monitoring, and evaluating.

In these three components, metacognitive experiences and metacognitive knowledge are related to the monitoring of cognition, and metacognitive skills/strategies focused more on controlling of metacognition. The definitions of metacognition have conceptualized metacognition as “multifaceted”, “conscious process”, and “individual phenomenon”. In order to study metacognition in the self-regulation processes, we need to combine “different experimental methodologies that implicate the self (e.g., feedback, social comparison) along with measures of metacognitive experiences and affect”.

A number of interventions have been developed in education that involve three components of metacognition. For example, interventions provide metacognitive experiences to control learners’ cognitive learning. The interventions usually emphasize on the metacognitive knowledge of strategies and the procedures that involved in metacognitive experience over time. Specifically, metacognitive interventions can also assess self-regulated learning and identify reasons why metacognitive regulation is failing, “that is, if it is metacognitive knowledge, metacognitive skills or metacognitive”.

Other Related Concepts

Judgements of Learning

A topic related to metacognition is Judgements of learning. Judgments of learning (JOLs) are “assessments that learners make about how well they have learned particular information”. Nelson and Dunlosky (1991) define that judgements of learning “help to guide self-paced study during acquisition”. It’s more accurate when it’s happening shortly than immediately after study. This implies learners should evaluate their learning process after waiting for a short time. In addition, they call the way of learners self-evaluation “Delayed-JOL Effect” and they believe that judgements of learning can be self-monitoring during learning.

Feeling-of-knowing judgment refers to the “degree of accuracy for recognizing or knowing a task or answer and predicting one's knowledge”, which is similar to the concept of judgments of learning. Both “Feeling-of-knowing” and SRL concept are connected because of metacognitive accuracy. The concept of Metacognitive Accuracy will be discussed later in this chapter.

Self-Regulated Action

Self-regulated action shows the way of how regulation is conducted. Both object and action are the major components of Self-regulated action. To better explain this, the object is the learning goal that learners set up at early stage of their learning and the action is how the particular learning goal have achieved by learners. Actions can include changes in cognition, emotion, motivation, behaviour, personality attributes and physical environment. For instance, the action of motivation can be directly affected by how and when learners have the ability to complete their learning tasks. The action of behaviour from individual learner will also impact on each individual learning ability and goal achievement.

Self-Assessment

Self-assessment makes people reflect on their abilities and their strategies. It requires choosing techniques that are most appropriate for the information needed to learn. It occurs in the first stage of self-regulated learning. Making self-assessment requires the learners to be motivated, and have the will and effort to adopt new learning techniques. Self-assessment requires a positive attitude towards learning. A positive attitude and an open mind about learning techniques can enhance the process of self-assessment. Questions you can ask yourself may be: What are my skills? What are my Interests? Do I learn by watching videos or taking notes? Do I learn better by writing or typing out notes? Do I learn best by memorizing and explaining?

Figure 3. Self-Regulated Learning Process

Purpose of Engagement

Purpose of engagement is a combination of self-process, purpose, and possible actions that are relevant in a specific learning situation. Each individual learner has different reasons for engagement of their own learning. For example, some learners want to learn because they are interesting about particular knowledge, and some of them learn because of their workplace needs. In this way, they will have different motivating factors, which will lead their learning process. During learners’ self-regulated learning process, their engagement mainly display in their plan, monitor and evaluate their learning. A more detailed table of the self regulated process and how students regulate their personal functioning, academic performance and learning environments is as follows:

Self Explanation

Self-explanation is an effective learning strategy which is conducive to robust learning. Butcher states that the concept of self-explanation was initially examined and used by Chi and her colleagues back to 1997 and it refers to a meaningful verbal set of utterances through which the participants explain the content they are dealing with. Chi herself defines self-explanation as a cognitive activity through which one can comprehend new content or learn a new skill by explaining to oneself, usually in the context of learning either from a text or any other medium. Self-explanation is similar to elaboration, except that the goal is to comprehend what a learner is learning or reading, not simply to memorize the content. In this regard, self-explanation is a knowledge-constructing activity that is self-directed Chi. In the process of self-explanation, learners find the logical connections among causal concepts (Bisra,Liu, Salimi, Nesbit and Winne). According to Bisra et al., self-explanation is conducted toward self in order to make sense of new information. Because it is self-addressed, the self-explanation process can be done silently or, if stated loudly, it is comprehensible only to the learner. Wylie and Chi. describe self-explanation as a constructive and generative strategy which deepens learning and, similar to other cognitive skills, develops and improves across time. Generally, the term self-explaining (SE) refers to a set of utterances generated through explanation to oneself. In other words, it is any content-related articulation produced by the learner after reading a part of a text.

Self-explanation versus instructional explanation

Self-explanation is an effective learning strategy which leads to robust learning. Based on Bisra and her colleagues, self-explanation is an effective activity which is not used only by the individual who has produced it, it can also be used by others. In this case, learning happens through the product not the process of the self-explanation just like the instructor’s explanation who is not aware of the student’s prior knowledge. Hausman and VanLehn (as cited in Bisra et al.) call this product-based self-explanation the coverage hypothesis, describing that self-explanation works by generating “additional content that is not present in the instructional materials” (p. 303). More widely-accepted theories of self-explanation consider self-explanation product as a generative cognitive process while coverage hypothesis considers the cognitive product of self-explanation similar to the product of instructional explanation. An instructional explanation is preferred, when the learner fail to generate a proper self-explanation (Bisra el al.). VanLehn, Jones, and Chi suggest three possible reasons for why self-explaining is effective. First, self-explanation is a persuasive procedure. That is, it causes the learners to spot and fill the gaps in their knowledge repertoire. Second, self-explanation seems to help learners to think about the solutions and steps of the original context in which they were produced to a more general condition of the problem. Third, it enhances analogical ability in learners which eventually generates a stronger elaboration of the problem. Additionally, Wylie and Chi, propose that the process of self-explanation helps learners recognize the inconsistencies and make proper adaptations in their mental models. Self-explanation procedure aids learners to gain a better declarative knowledge of the domain and enhances their problem-solving skills.

The advantages of Self-Explanation over Instructional Explanation

Results of a meta-analysis study on self-explanation by Bisra and her colleagues are against the coverage hypothesis. This meta-analysis indicates that self-explanation is more effective than instructional explanation. In this study, Bisra and her colleagues show that self-explanation (g=.29) has a more detectable benefit over instructional explanation. The authors attribute this superiority of self-explanation to the cognitive process of matching prior knowledge to the new knowledge which the learner goes through during the process of self-explaining helps learners build a meaningful relationship. When a meaningful association is formed between prior knowledge and new information, the cognitive processes are activated, the newly-generated explanation is recalled later and is used as further reasoning (Bisra et al). Wylie and Chi state that self-explanation with prompts could be more effective than self-expalnation paired with instructional explanation because the former procedure activates students’ cognitive abilities even with receiving no training, no error correction or no explaining. Ionas, Cernusca and Collier. believe self-explanation is more effective than the explanation transmitted by teacher, books or other sources for three reasons: 1. it makes the learners to activate their prior knowledge, so self-explaining is a knowledge-constructing activity. 2. It addresses the learners’ specific problem and 3. Learners have access to this source whenever they need.

Multimedia leaning environment, Prior Knowledge, and Self-explanation

Multimedia learning environments facilitate learning through a combination of text, animation, illustration (such as figures, diagrams, and pictures), text, and narration and are usually computerized . Multimedia benefits learners because it provides them with various modes of presentation. For instance, diagrams help learners fully understand spatial information, and narration creates a dynamic environment so that learners acquire more from it than from text only. While learning from multimedia, learners have the opportunity to encode both verbal and nonverbal data and they should be able to combine the information presented from each source. But it is noteworthy that learning through multimodal presentation is very beneficial only if learners can involve themselves in cognitive process of integrating information across each source. Wylie and Chi hold that one way for involving cognitively in multimedia learning environment is self-explanation which is conducive to integration of information. Butcher in a study demonstrated that students who did self-explaining while dealing with a multimedia source learned much more than students who did self-explaining when studying a single medium resource (only text). A study by Ionas, Cernusca, and Collier showed that having prior knowledge enhances the effectiveness of self-explanation for chemistry problem-solving. In this study, learners showed they benefited from the interaction of prior knowledge and self-explanation procedure in two ways. Firstly, the more the students expressed their knowledge of chemistry to themselves, the more effective their self-explanation was. In other words, the employment of self-explaining seem to help learners integrate their prior knowledge with the activities on hand. Secondly, in order to make self-explanation-based strategies work, learners should obtain a particular level of prior knowledge, a “threshold”. It means using self-explanation is not helpful for students when they have little prior knowledge of the domain; in other words, not only does little prior knowledge help learners reap any knowledge but also it impedes successful performance . When learners have high perception of their knowledge about chemistry they tend to provide powerful self-explanation and when they have not reached the threshold, they might have an understanding of different chunks of a domain but they are not aware of the relation among the chunks and cannot link them. In fact, they understand the chunks and concepts separately but they cannot find the interaction among them . Thus, when students are self-explaining, they are trying to find the similar concepts, conditions or procedures in their prior knowledge repertoire so that they can build new knowledge and solve the given problem. The authors concluded that the whole procedure does not move smoothly when the learner does not possess a strong foundation of prior knowledge. In addition, Yeh, Chen, Hung, and Hwang assert that level of prior knowledge affects the way students self-explain. They did a research on 244 students with various levels of prior knowledge to interpret the students’ prompts impact while learning with dynamic multimedia content. They devised two kinds of self-explanation prompts and applied several indicators including learning result, cognitive load, learning time span, and learning efficiency. The reasoning-based prompt made students to reason the action of the animation and the predicting-based one required the learners to guess the forthcoming action of the animation and if their prediction was wrong, they had to reason. The results showed that learners who had lower prior knowledge reap most benefit from reasoning-based prompts while higher-knowledge students experienced most benefit from predicting-based prompts. To conclude, it could be argued that learners should reach a certain level of prior knowledge and make a decent background of the domain, so that they can comprehend the new information and self-explain better. Moreover, learners with diverse levels of prior knowledge perform differently. Thus, prior knowledge takes a vital role in assisting learners to self-explain. When dealing with multi-media environment, students with higher knowledge prefer to predict the next scene of the animation and those one with lower knowledge favor reasoning the action of the animation.

Implications for Instruction of Self-explanation

According to Ionas, Cernusca, and Collier, there are suggestions for the instructional design of curriculum for finding a threshold by which the application of self-explanation is productive. Although self-explanation is used to extend the advantage of tutoring and review sessions or short transfer problems via more intensive cognitive involvement of the learners during the different learning activities, teachers or instructors should not ask their students to use self-explanation too early in the learning cycle. Thus in the beginning stages of learning cycle, self-explanation is not recommended. Instead, it is highly advised to apply other cognitive methods and procedures. These methods help students in the initial stages of the learning cycle to reach a certain level of competence that would make the use of self-explanation beneficial. The authors argue that the advantage of self-explanation is that when learners learn how to self-explain, they attempt to apply it in other areas and problems because self-explanation is a domain-independent cognitive strategy. The disadvantage of self-explaining is that when learners’ general domain expands, their domain-specific knowledge still needs to expand so that self-explanation can work optimally, but if the learners are at the early stage of acquiring a particular domain, they might experience the ineffectiveness of self-explanation. Therefore, as Ionas, Cernusca and Collier argue, when designing an instructional design, educators should take into account such tendency and implement preventive measures that would prohibit learners from using self-explanation until they reach an appropriate level of knowledge. Overall, based on the finding of this study, before asking learners to self-explain, their prior knowledge should initially be assessed; then, based on this assessment the educators can plan how to put forward self-explanation. For instance, to asses the learners’ prior knowledge, teachers can give more specific guiding questions to students before asking them to solve a new problem. When learners comprehend the content, teachers can apply general prompts to elicit self-explanation. The instructors can also ask the learners to utilize these prompts by themselves while they are solving a problem. Indeed, the long-term effect of using self-explanation is for the learners which is gaining an ability to activate self-explanation strategies by themselves when trying to solve a problem. According to Ionas, Cernusca and Collier a further strategy is to make teachers help students explain to themselves through an argumentation structure. In this case, learners use pre-planned prompts which help them create an argument about the way they have already solved a problem. Hence, in this strategy the pre-planned argumentation prompts are those which elicit self-explanation. There is no universal prescription for designing self-explanation prompts because they are subject-dependent. The prompting questions vary from general to specific and it is the instructor’s responsibility to discern the best strategy to elicit the self-explanation behaviour. In order to help learners adopt a method to use in further problem solving, teachers are highly recommended to start with simple questions in case the topic is not familiar and then gradually move towards more specific questions.

Different kinds of self-explanation

If we put self-explanations on a continuum, at one extreme we have open-ended self-explanation prompts that persuade learners to link prior knowledge to the new information. In this form of self-explanation learners are free to describe their thoughts and are not influenced by pre-imposed ideas. Because learners’ thoughts are not influenced by other’s idea and are original so it is a very natural explanation. At the other extreme of the continuum are menu-based explanation prompts. In this type a list of explanations are provided to learners, then they are asked to choose from the list and are prompted to self-explain the reason of their choice. Findings of a study by Atkinson, Renkl, and Merrill demonstrated that students who were prompted to self-explain while selecting from a multiple-choice menu were more successful in both near and far transfer conditions than students who were not prompted to self-explain. This fact suggests that prompting students to explain via menus can be an effective educational strategy. While open-ended and menu-based approaches are placed on two ends of the continuum, focused, scaffolded, and resource-based prompts fall between the two ends. Focused prompts and open-ended prompts are similar in two ways; both are generative and do not influence learners’ ideas. But in focused approach the instruction about the required content of the self-explanation is more explicit than open-ended type. In open-ended self-explanation prompts students are simply asked to explain new information, but in focused self-explanation prompts students are directly asked to explain in a specific way . Self-explanation scaffolds are even more focused. Scaffolded or aided self-explanation prompts deal with a cloze or fill-in-the-gap method. In this approach learners are required to complete explanation by filling the blanks. Wylie and Chi, hold that this method might be advantageous for less experienced learners who do not have adequate prior knowledge to be able to engender open-ended self-explanation by themselves. Resource-based self-explanation is similar to menu-based approach. In this approach the learners are required to justify or explain the problem-solving steps by selecting from a given glossary. They can use this glossary as a reference to check the explanation and use the explanation of each step as a recognition of the problem instead of recalling it. Wylie and Chi distinguish the resource-based approach from menu-based method by the feature of large size of its glossary. Wylie & Chi believe that all different forms of self-explanation make learners to think profoundly and engage them cognitively in learning new information through making bridge to prior knowledge and modify their mental model. Based on research, among different types of self-explanation, open-ended self-explanation approach is less beneficial especially in multimedia learning environments than strategies which present more focused-based direction. The research also suggests that the self-explanation prompts including focused, scaffold and resource-based approaches that direct learners toward a specific explanation are conducive to deeper comprehension compared to open-ended explanations. Van der Meij and de Jong built two models of a simulation-based learning environment that include multi modal representations. In one model, students are asked to self-explain by answering a broad prompt requiring them to defend or justify their answer (open-ended self-explanation). In the other model, students were given more direct instructions and they were asked to clarify how the two given representations were connected (focused self-explanation). The study findings indicated improved performance in two models of simulations, but students obtained more learning profits in the focused self-explanation group. Therefore, the results prove the hypothesis for multimedia learning environments saying that a more focused self-explanation prompt is better than a broad free-form, open-ended prompt.

Models of Self–Regulated Learning

Figure 4. Models of Self-Regulated Learning

Zimmerman’s Cyclic SRL Model

Zimmerman’s Cyclic SRL Model divides self-regulated learning process into three distinguished phases: forethought phase, performance phase, and self-reflection phase. The forethought phase refers to processes and beliefs that occur before efforts to learn; the performance phase refers to processes that occur during behavioral implementation, and self-reflection refers to processes that occur after each learning effort.

Forethought Phase

There are two major classes of forethought phase processes: task analysis and self-motivation. Task analysis involves goal setting and strategic planning. Self-motivation stems from students’ beliefs about learning, such as self-efficacy beliefs about having the personal capability to learn and outcome expectations about personal consequences of learning.

Goal Setting is looking at what you need to achieve and how to get there in a specific time frame. Goal setting requires a basic understanding of the information need to be learned, because in order to set a goal learners must have some knowledge in what the outcome should look like. Goal setting is important because it helps create motivation and can motivate learners to accomplish a specific learning goal. It is essential to create attainable goals which you are capable of reaching. Therefore the goals set should neither be too high nor too low; it should be in your realm of attaining and succeeding. Attainable goals promote desire and motivation because they are more likely to be accomplished. There is considerable evidence of increased academic success by learners who set specific proximal goals for themselves, such as memorizing a word list for a spelling test, and by learners who plan to use spelling strategies, such as segmenting words into syllables. Some questions that one could ask themselves to goal setting are as follows: What do I want to achieve? What steps will take me to my goals?

Strategic Planning is similar to goal setting in that learners need to have a basic understanding of the learning content. After goal setting, learners should plan specific strategies to achieve those learning goals. Strategic Planning is a more detailed way to reach learning goals. A strategic plan consists of a number of small goals within a bigger goal. To make a good plan, learners need to understand the learning tasks, learning objectives, and the direction they want to pursue.

For example, if one had seven days to study for an exam covering fourteen chapters, he can separate the learning into studying two chapters per day. By strategically planning how much he need to study everyday, the final goal of learning fourteen chapters in seven days will be achieved. Strategic plans can also be used to reach athletic goals. E. g., in order to accomplish a marathon training in one month, one can create a timeline of how much he should improve each week, and how long he should run each day and each week, so he can add the workouts of each day and each week to reach the final goal.

In order to help developing strategic plans, learners could ask themselves some kinds of questions, such as: What is my purpose of the learning? How will I reach my learning goals? How can I implement my learning strategies to reach my goals? Do I have enough time to accomplish each goal? Are my goals realistic in this specific time frame? How should I study for this specific goal? How does my personality affect me reaching those goals? What might distract me when I am learning?

Self Motivation Beliefs include self-efficacy, outcome expectations, intrinsic interest, and learning goal orientation. Self-efficacy in this case is students’ belief about their ability to learn a task. For example, when a student is learning a difficult concept in the class, he may feel he is going to understand it easily or he might fear that he is going to get lost. "Self-efficacy is extremely important for self-regulated learning because it affects the extent to which learners engage and persist at challenging tasks. "Higher levels of self-efficacy are related positively to school achievement and self-esteem. Teachers can enhance self-efficacy by providing learning tasks with appropriate levels of difficulty and with an appropriate amount of scaffolding. Schraw, Crippen and Hartley suggest that there are two ways to enhance students' self-efficacy. "One is to use both expert (e.g., teacher) and non-expert (e.g., student peers) models", “The second is to provide as much informational feedback to students as possible”. Outcome expectations are personal expectations about the consequences of learning, such as students believe that they can learn a difficult concept in economics class and are going to use this knowledge in the future. Teachers can promote outcome expectation by reminding students that the information is going to be useful in the future. Intrinsic interest refers to the students’ valuing of the task skill for its own merits, and learning goal orientation refers to valuing the process of learning for its own merits. Students with high intrinsic interest are more motivated to learn in a self-regulated fashion because they want to acquire the task skills. A student who wants to become a teacher, for example, might study the educational knowledge really hard. Teachers can enhance the intrinsic interest by introducing the application of knowledge. Teachers can enhance learning goal orientation by making the class entertaining or intrigue students' attention using different modality (video clips, graphs).

Schraw et al elaborated the motivation component, in science self-regulated learning, as a composition of self-efficacy and epistemological beliefs. Epistemological beliefs are “those beliefs about the origin and nature of knowledge”. These beliefs affect problem solving and critical thinking, which are important component of self-regulated learning.

Performance Phase

Figure 5. Zimmerman’s Cyclic SRL Model

Performance phase processes fall into two major classes: self-control and self-observation. Self-control refers to the deployment of specific methods or strategies that were selected during the forethought phase. Self-observation refers to self-recording personal events or self-experimentation to find out the cause of these events. For example, students are often asked to self-record their time use to make them aware of how much time they spend on studying. Self-monitoring, a covert form of self-observation, refers to one’s cognitive tracking of personal functioning, such as the frequency of failing to capitalize words when writing an essay.

Self Control processes, such as self-instruction, imagery, attention focusing, and task strategies, help learners and performers to focus on the physical task and optimize their solution effort. For example, self-instruction involves overtly or covertly describing how to proceed as one executes a task, such as “thinking aloud” when solving a mathematics problem. Imagery, or the forming of vivid mental pictures, is another widely used self-control technique to assist encoding and performance. A third form of self-control, attention focusing, is designed to improve one’s concentration and screen out other covert processes or external events during problem solving. Volitional methods of control, such as ignoring distractions and avoiding ruminating about past mistakes, are effective in enhancing problem solving. Task strategies can assist problem solving by reducing a task to its essential parts and reorganizing them meaningfully.

The second major class of performance phase process is self-observation. This refers to a person’s tracking of specific aspects of his or her own performance, the conditions that surround it, and the effects that it produces. Learners who set hierarchical process goals during forethought can self-observe more effectively during performance, because these structurally limited goals provide greater focusing and reduce the amount of information that must be recalled. Regarding the accuracy of self-observations, individuals who fail to encode and recall their prior solution efforts can not adjust their strategies optimally.Self-recording can provide the learner with more accurate information regarding prior solution attempts, structure that information to be most meaningful, and give a longer database for discerning evidence of progress of problem solution efforts. Self-observation of one’s performance, especially in informal contexts, can lead to systematic self-discovery or self-experimentation.

Strategy implementation is the process of which learners deploy strategic learning plans and actually applying these plans into learning practice. Strategy implementation requires motivation and self-determination. Learners must have a solid strategic plan to prevent environmental distractions and understand what will motivate and demotivate the learning in achieving the goals. Strategy implementation is important in the success of learning experience, because it affects the efficiency and effectiveness of learning. It addresses how and where the learning will occur and is one of the most important factors for learners to reach their learning goals.

Strategy Monitoring is the process of monitoring how effective the strategic plans are for facilitating learning. By monitoring the implementation of learning strategies, the progress of learning tasks and how the environments affect the learning processes, learners can assess how effective their learning is, and adjust the strategies as needed so that the best learning experience could take place.

Self Reflection Phase

There are two major classes of self-reflection phase processes: self-judgment and self-reaction. One form of self-judgment, self-evaluation, refers to comparisons of self-observed performances against some standard, such as one’s prior performance, another person’s performance, or an absolute standard of performance. Another form of self-judgment involves causal attribution, which refers to beliefs about the cause of one’s errors or successes, such as a score on a mathematics test.

Self Judgement: There are four main types of criteria that people use to evaluate their problem solving: mastery, previous performance, normative, and collaborative. Mastery criteria are absolute indices of a solution, such as comparing a crossword puzzle solution with the author’s solution. When solving problems in unstructured informal contexts, learners must often rely on non-mastery standards, such as comparisons of their current performance with previous levels of performance. Self-comparisons involve within-subject changes in functioning, and as a result, they can highlight learning progress, which typically improves with repeated practice. Normative criteria for self-evaluating one’s learning involve social comparisons with the performance of others, such as classmates or during a national competition. A collaborative criterion is used primarily in team endeavors towards accomplishing learning tasks.

Self-evaluative judgments are linked to causal attributions about the learning outcomes, such as whether a failure is due to one’s limited ability or to insufficient effort. Attributing a poor score to limitations in fixed ability can be very damaging motivationally because it implies that efforts to improve on a future test will not be effective. In contrast, attributing a poor math score to controllable processes, such as the use of the wrong solution strategy, will sustain motivation because it implies that a different strategy may lead to success.

Self Reaction: One form of self-reaction involves feelings of self-satisfaction and positive affect regarding one’s performance. Increases in self-satisfaction enhance motivation, whereas decreases in self-satisfaction undermine further efforts to learn. When learners condition their self-satisfaction on reaching their problem-solving goals, they can direct their actions and persist in their efforts much better. Self-reactions also take the form of adaptive/defensive responses. Defensive reactions refer to efforts to protect one’s self-image by withdrawing or avoiding opportunities to learn and perform, such as dropping a course or being absent for a test. In contrast, adaptive reactions refer to adjustments designed to increase the effectiveness of one’s method of learning, such as discarding or modifying an ineffective learning strategy.

Outcome Evaluation : Outcome evaluation takes place after learning has occurred. It reviews the learning goals, the strategic plans, and evaluate how effective they were. Outcome evaluation is very important because it helps learners to improve the efficiency and effectiveness of their learning practices and create a better plan for the future learning processes. Questions that learners may ask themselves could be: How practical were my goals? Were they attainable? How accurate was my strategy plan? Should I have included any other strategies which I did not? What should I change about my learning in the future? Was my environment distracting?

Issues and Topics of Research

Figure 6. Issues and Topics of Research

Cultural Differences in Self – Regulated Learning

The concept of learning, and self-regulated learning in particular, relates to cultural differences. Most information on ‘self-regulation’ and the ‘concept of learning’ are Western views. This is a one-sided approach to understanding self-regulation. Being exposed to different cultures, people are also exposed to different ways of thinking.

When Japanese students studied in Australia, they learnt different learning strategies and found new ways to understand knowledge than what they were used to. This process may have been unconscious but because they were put into a new system with a different language and a different structure, they were forced to change some of their learning strategies. Viewing learning from different perspectives makes people realize that knowledge is not necessarily dualistic. This means that knowledge is not right and wrong, or good and bad. Knowledge is something flexible and dynamic and, therefore, it can be questioned. The stereotypical view of Asian culture on learning is that knowledge is something learnt by an authority figure who knows right and wrong and that it is something that need to be memorized. This results in the assumption that students from Asia are passive learners who are compliant, obedient, and absorb knowledge rather than understand it. The stereotypical view of Australian students is that they are more active learners, as they are characterized “by assertiveness, independence, self-confidence, acceptance of diversity, and a willingness to question and explore alternative ways of thinking and acting”.

Individual differences in metacognition

Figure 7. Different Mind

Another popular topic in the studies of metacognition is the issue of individual differences. Research of individual differences in metacognitive ability shows that this issue makes metacognition very difficult to measure. Winne (1996) proposed that there are five sources of individual differences affecting metacognitive monitoring and control in self-regulated learning. These are: “domain knowledge, knowledge of tactics and strategies, performance of tactics and strategies, regulation of tactics and strategies, and global dispositions”. (Winne 1996, p. 327) Global dispositions refer to dispositions about learning. Winne emphasized that his proposals are tentative and require further investigation. However, his research encouraged other researchers to dive into this topic.

A number of researchers suggest that individual differences in metacognitive accuracy reflect differences in metacognitive ability, however Kelemen, Frost, & Weaver (2000) suggested that this is not the case. Metacognitive accuracy refers to “the relationship between metacognition and future memory performance”(Kelemen et al., 2000, p. 92). The study measured four common metacognitive tasks: judgements on “ease of learning”, judgements on “feeling of knowing”, judgements of learning, and text comprehension monitoring. In the study, including pretest and posttest, memory and confidence levels were stable. However, individual differences in metacognitive accuracy were not stable. This suggests that metacognitive accuracy is not reliable when it comes to measuring individual differences in metacognitive ability. It should be noted that the validity of research is questionable, as a lot of researchers acknowledge the difficulty of measuring metacognition. Further research is required in the field.

The notion of individual differences in metacognitive ability also suggests that there is no one-size-fits-all solution for metacognitive instruction. Lin, Schwartz and Hatano (2005) suggest that application of metacognition need to be proceeded with careful attention to differences in individual learning and classroom environment. They also suggest teachers to use adaptive metacognition which involves "both the adaptation of oneself and one's environment in response to a wide range of classroom variability" (Lin et al., 2005, p. 245). Classroom variability includes social and instructional variability. In order to implement adaptive metacognition, Lin et al suggest an approach called Critical Event Instruction which "help teachers appreciate the need for metacognitive adaptation, particularly in situations that appear routine on the surface level" (Lin et al., 2005, p. 246). This approach helps prepare preservice teachers deal with commonly occurred problems in the classroom. It provides information on how to deal with different values, goals and experiences.

Learning analytics and SRL Research

Defining Learning Analytics

In fields ranging from business to epidemiology, propagation of computer use and the increase of computational power has created opportunities for extracting, analyzing and reporting useful information from large data-sets. In education, similar methods for dealing with ‘big data’ are referred to as learning analytics. Although often presented as a new discipline, learning analytics has been formed by ideas, principles and methodologies that have been around for some time. Its roots are multi-disciplinary, combining elements from artificial intelligence, statistical analysis, machine learning, business intelligence, human-computer interaction and education.

What is Learning Analytics?

The Society for Learning Analytics Research (SoLAR) provides the following definition for the field of Learning Analytics: “Learning Analytics is the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environment in which it occurs.” Synthesizing the different definitions suggested by various experts in the field, the following points can be inferred about the nature of learning analytics:

• The discipline involves techniques, methodologies, frameworks and tools that are implemented to deal with data.
• It focuses on data deriving from learner behavior and activity in various educational settings. Actually, as Siemens (2013) suggests, the origins of the data can be traced to various levels of education, from individual classrooms to international curricula.
• Its scope extends in every phase of data manipulation: data capture, with tools that are actually used to collect the necessary data, data analysis, with tools that aim at finding structures and patterns in the data, and data representation, with tools creating visualizations of data to be used further.
• It has a theoretical aspect, as the analysis of the educational data may lead us to a better understanding of the learning process, providing the necessary empirical evidence to support relevant theories.
• It has a practical aspect, as the results of these data analyses and interpretations may provide new ways to manipulate and thus optimize learning environments and the learning process in general.

Factors that facilitated the increased use of Learning Analytics Even though the narrative of learning analytics, in terms of its focus, is not new, there were certain developments and factors that reinvigorated the interest in the field, resulting in its establishment as a distinct discipline. The most prominent of these factors are the following:

• Quantity of data

The quantity of educational data available to be further analyzed has been greatly increased, especially after introducing digital devices in various learning contexts, like blended modes of instruction, learning management systems etc. When learners use digital media, they leave a “digital trace” of their interactions in the form of data that are easily captured and stored for further analysis. That kind of data may include logging times, posts, number of clicks, sections of the material visited by the student, components that have been used and for how long etc. Subsequent analysis of the data could lead to interesting insights on the learning activities and the deeper cognitive processes related to them.

• Increased processing / computation power and more efficient algorithms

Certain advances in computation facilitate the analysis of the large quantities of educational data available. Computational power has greatly increased, making possible data analysis in shorter periods of time, while new algorithms on machine learning and artificial intelligence allow the discovery of patterns and constructs in the data without immediate human supervision of the procedure.

• Data formats

Capturing the necessary data for analysis is not enough. The data have to be in a usable form, in order to be processed efficiently. That is the role of standardized formats for logging specific types of educational data. Having those formats beforehand saves us a great amount of time that was needed to prepare the data for analysis and interpretation.

Key methods and tools of Learning Analytics

Siemens (2013) distinguishes two major components of learning analytics, techniques and applications. Techniques include computational elements (algorithms and models) that are used for analyzing the educational data. Applications are the actual implementations of these techniques in educational settings, in order to achieve specific goals like adapting the learning environment to the user or creating learner profiles.

In this section, the major techniques and methodologies used in Learning Analytics are presented, along with some examples of possible applications, which outline the ways that these techniques can be applied to learning environments and other educational settings.

Prediction methods

A simplified description of the function of these methods is to identify the value of a specific variable (which is called the predicted variable) by analyzing a set of other aspects of data that relate to other variables (which are called predictor variables). For example, there are prediction methods that collect data from various activities of students in an online course (log in times, blog activity, performance in assessment tests – predictor variables) to determine the probability of failing the course (predicted variable). These prediction models can be used in two types of applications: to predict future events, like student dropout or student outcomes in courses . There are also cases of data that cannot be collected directly, as this will intervene with the students’ activity. In these cases, prediction models allow the researchers to infer the necessary data by measuring other sets of variables.

Structure discovery

This Learning Analytics technique appears quite different from the previous one, as it includes algorithms that have the goal to discover structures in educational data without previous hypotheses on what it is to be found. There are several methods to achieve this goal. In clustering, the objective is to organize data in groups, with the result of splitting the data set into a set of clusters. These clusters can be, for example, student groups, categorized on how they use exploratory learning environments. In social network analysis, patterns of relationships and / or interactions between learners are identified. This method have been used for many different studies, like determining how students’ behavior and status in a social network relate to their perception of being part of a community.

Relationship mining

This technique is used as a method to detect relationships between variables in the case of large data sets with a high number of different variables. The most usual goals of this method is to discover which variables are more strongly associated with a specific variable or to pinpoint the strongest relationships between variables. There are several applications for this Learning Analytics technique. Baker et al. (2009) managed to compute correlations between several features of Intelligent Tutoring Systems and the students' tendency to “game the system” (= intentionally misuse the system in order to proceed with the course without actually learning the material). In another study, Perera et al. (2009) used this method to analyze data, in order to determine what path of student collaboration leads to successful completion of group projects.

Distillation of data for human judgment

This technique involves several methods of refining and presenting educational data, using appropriate visualizations, in order to support basic research as well as the practitioners of education (teachers, school leaders, administrators etc.). For example, Bowers (2010) used visualizations of student trajectories spanning over several years to identify patterns that would predict which students are at risk. The rationale was that there are certain common patterns among successful or unsuccessful students that can be identified and which, when appearing, can be considered an indication for the student's success or failure.

Learning Analytics and research in SRL

Considering the previous section on Learning Analytics methods and applications, it is obvious that these tools provide the empirical evidence to form and support theories on learning. Research in the domain of self-regulated learning isn’t an exception. Several studies have been conducted using learning analytics methods and tools, in order to explore the field and test hypothesis on the nature of self-regulation and the conditions under which it appears.

Issues and challenges in Self regulation research

The continuously expanding use of computer-based learning environments brought a subsequent increase of the interest in research of self-regulation. The reason for this is that these new learning applications present important opportunities for learning, leading researchers to examine how successful students are in taking advantage of this potential and the conditions for this success. These learning environments provide a high degree of learner control and, hence, opportunities for self-regulated learning. Learners are able to approach the content on multiple ways, decide on multiple ways of representations, manipulate several parameters of the environment etc. However, this also means that learners lacking the necessary self-regulation skills may face the possibility of failing the learning objectives of these resources. Therefore, it is crucial to capture and assess self-regulated learning behaviors of students in these environments, in order to further understand the nature of these cognitive processes and to design interventions and scaffolds to support them.

Several issues and challenges in capturing and assessing self-regulated learning behavior have been identified by researchers and experts in the field, especially due to the internal nature of the processes involved. In examining or measuring self-regulation of learners in computer-based learning environments, it is very important for the researchers to adopt a specific theoretical model for SRL. Siadaty et al. (2016) emphasize the fact that, in order to have valid interpretation of the measurement of self-regulation, “the selection, development and deployment of a measurement method (or a combination of methods) should align with the underpinning SRL model or theory” (Siadaty et al., 2016 p. 190). However, there are cases of studies that do not acknowledge a specific theoretical model or framework, thus resulting in lack of clarity about terminology and definitions. Additionally, in certain research studies specific aspects of self-regulated learning models are addressed, like goal setting, self-monitoring or self-efficacy. These approaches, isolating and treating these aspects as individual elements, do not provide an accurate picture of the role the pieces play in the larger construct of SRL.

Another issue in self-regulated learning research lies in the method of data collection used in the several studies. The majority of relative studies use as the major source of data self-reports of the learners who use the learning resources. The accuracy and overall quality of the data are highly dependent on the students' learning awareness, as well as their skill to describe their actions and strategies when interacting with the learning environment. As Winters et al. (2008) point out, these student self-reports are not always as accurate as observational techniques. Other studies rely on think-aloud protocols as their primary source of data. These methods can capture self-regulated processes as they occur and in a more accurate way. However, the use of these protocols is focused on identifying strategies and processes used, ruling out the examination of their quality, i.e. how successful the students are in using and implementing these during their learning. As an example, summarization is a very effective learning strategy. However, the degree of effectiveness is not determined by merely implementing or not this strategy, but also by the quality and the conditions of summarization, in relation to the learning objectives (the time of summarization, how it is conducted, the choice of topic etc.).

Finally, an important issue to the researchers when deciding on the data collection and measurement tools is how intrusive they are in the learning procedure. The ideal capturing method is the one that functions in parallel with the learner's interaction with the system and collects data without interfering with the learning process in any way. This kind of “unobtrusive” behavior appears in learning analytics data collection tools. These tools are tracing the user's actions, as they interact with the system, log times, features of the environment that are used more frequently, performance in assessment activities etc. to discover patterns of actions that provide evidence of self – regulation. The use of learning analytics in investigating self-regulated learning will be further discussed in the next section.

Capturing Self Regulated Learning behaviors using Learning Analytics

Learning analytics techniques and applications provide accurate and non-intrusive data collection methods, in order to trace and further analyze empirical evidence of self-regulation processes, during the learners’ interactions with the learning environment. Additionally, recent developments in computer science provide highly sophisticated methods to collect trace data on these processes, enriching the variety of tools that are in the researchers’ disposal.

Figure 8. KWL

As we have already seen in the previous section, the majority of relative studies on self-regulation uses as the primary source of data students’ self-reports, with all the challenges that this choice entails. However, there have been studies that have used a blend of self-report surveys, online behavioral data and learning outcome measurements. Sha et al. (2012) attempted to explore patterns of self-regulated learning during the use of a mobile learning environment. That specific study involved primary school students (Grades 3 and 4) that used the affordances of a mobile learning platform to learn science. The platform was used in the context of the official curriculum in Singapore. The learning platform included several applications for a variety of purposes, like drawing animations, creating concept maps and creating KWL tables. The students’ actions and performance on the latter application (iKWL) was the data source that was used in the study. More specific, this application consists of three pre-designed questions that students answer before, during and at the end of each lesson (see also Figure 8): What do I know?, where the students bring their prior knowledge to the task, What I wonder?, which functions as a goal setting component and What did I learn?, that refers to the self-reflection phase of self-regulated learning. The researchers’ intention was to explore the characteristics of the learners’ engagement in answering the KWL questions. In order to measure this, two variables were implemented: one indicating whether or not a student completed the KWL table (0 if none of the fields were completed and 1 if at least one of the fields was completed) and another indicating the degree to which each student completed the table (rubric that measures number of items inserted in each category). This measurement is rather simple, so it can be performed automatically by the system, without examining anything about the quality of content for these insertions.

Figure 9. Posterlet

There are studies that focus on investigating specific aspects of self-regulation strategies implemented by learners in computer-based learning environments. Cutumisu et al. (2015) in their study investigated the effectiveness of the strategies “seeking negative feedback” and “revision” to the learning outcomes, for primary school students using a learning application named Posterlet. This learning environment enables students to design posters for a school’s Fun Fair. The learning objectives accommodated with this resource is for the students to learn principles and practices of effective poster design (optimal graphical and textual characteristics). The component for capturing that specific learning behavior is embedded as a feature to the learning environment. In particular, the learners design a poster using the several tools provided by the environment and then receive feedback on their product, in the form of positive (I like…) or negative (I don’t like…) comments by animal – agents (see also Figure 9). The system captures two learning choices made by the students, the number of times a student chose the negative feedback option and the number of times a student revised his / her product. The data collected were strictly numerical. No measurement of the quality of revisions (whether the students’ revisions were directed by the feedback they received by the system) had been made during the study.

Figure 10. MetaTutor

There are certain learning environments that have a dual role in terms of self-regulated learning: learning tools, which are designed to teach and support self-regulation behaviors, and research tools, used to collect data on students’ self-regulation behaviors. Such a case of a learning application is MetaTutor, used in the research studies by Azevedo et al. (2013). MetaTutor is a learning environment with biology science content, using multiple agents to guide and support students in using self-regulated learning strategies when interacting with the platform. Several of its features refer to specific self-regulation stages and processes (goal setting, planning, self-monitoring, self-reflecting) and they are seamlessly embedded in the system’s interface (see also Figure 10). Additionally, MetaTutor includes data collection mechanisms which are used to collect information on user interactions, in order to provide researchers with the necessary data to investigate self-regulation processes, but also to provide students with the necessary formative feedback, in order to support and further expand their self-regulation skills. The system uses a range of sophisticated learning analytics techniques, apart from the usual ones (self-report surveys, think aloud protocols), in order to capture and assess self-regulated learning. An eye-tracking component is used to infer valuable information about how learners navigate and explore the content, in which parts they focus, the order they access the information, the parts of the diagrams that they use etc. These data are very important, as they reveal information about processes that may not be mentioned in the students self-reports or think- aloud sessions. The system also traces data from various processes and interactions that relate to self-regulated learning strategies and which are being deployed by students to facilitate the learning procedure. Examples of these data traces include note-taking patterns or drawing behaviors, as well as event-based traces of the students’ interactions (key strokes, mouse clicks, accessed chapters or activities, performances in quizes etc.). The data are subsequently analyzed and patterns or sequences of actions are discovered, in relation to specific self-regulation processes and strategies. The synthesis of all these different types of data provides the researchers with an insight of the subordinate cognitive processes. For example, the longer time a student spends when reading a text indicates increased cognitive processing of textual content, or tracking the user's transitions from text to diagrams and graphs indicate an attempt to integrate multiple representations of informational sources. There is also an elaborate facial expression recognition component. The system collects video data of students' facial expressions, which are subsequently analyzed by specialized software (Noldus FaceReader 3.0) and the students' emotional states are determined. The drawback is that the system recognizes a limited number of basic, universal emotions, that don't represent the whole range of emotions that students experience when interacting with the learning environment.

Figure 11. Bretty's Brain

Finally, there are studies that use specific components of learning applications that are related to certain self-regulation stages, implementing the data collected by these components to discover structures in the data (see also clustering in section 2). Segedy et al. (2015) incorporate a similar data collection method in an approach to self-regulation learning research which they call coherence analysis. In their study, they are using a learning application called Betty’s Brain. In this learning environment, students attempt to teach a virtual agent, Betty, about a science phenomenon, by constructing a causal map. This map (see also Figure 11) consists of entities, which represent key concepts of the phenomenon, connected by directed links, which represent causal relationships between concepts. Betty uses this causal map to reason using chains of links and to provide answers to various quiz questions.

The correctness of the causal map will determine the ability of the agent to answer correctly these questions. The students infer these causal links by acquiring the necessary information from specific texts they are provided, test their causal maps against certain quiz and, depending on the feedback, revise them to achieve higher accuracy. Analyses of the data collected during the students' interactions with the program determined 5 different groups of students, depending on their behavioral patterns. The first group, frequent researchers and careful editors, spent large amounts of time viewing sources of information and not so much on editing their causal maps. Group 2, strategic experimenters, spent enough time viewing information, without actually taking advantage of that. Their edits of the causal map, though, are more frequent than group 1. Group 3 can be characterized as confused guessers and they edit their causal maps frequently but without support from the science resources. Group 4 involves students disengaged from the task. These students have a high proportion of unsupported edits and they spent more than 30% of their time in the system in disengaged mode. Group 5, engaged and efficient, have a high edit frequency on their causal maps and most of these were supported. These students had also high viewing time and potential generation time. That behavior is actually the one that makes students succeed in Betty's Brain.

From Theory to Practice

Figure 12. From Theory to Practice

Applied theories of Metacognition

Metacognition in Reading

Recent research on metacognition and its effect on reading comprehension includes studies and individuals with language disorders and adolescents. These studies show relationship of metacognition with reading and writing, as well as the applicability of metacognitive interventions. Furnes and Norman (2015) compared three forms of metacognition (that is metacognitive knowledge, metacognitive skills, and metacognitive experiences) in normally developing readers and readers with dyslexia. Participants read two factual texts, and their learning outcomes were measured by a memory task. Metacognitive knowledge and skills were assessed by self-report and metacognitive experiences were measured by predictions of performance and judgements of learning. The results showed that reading and spelling problems of individuals with dyslexia are not generally associated with lower levels of metacognitive knowledge, metacognitive strategies or sensitivity to metacognitive experiences in reading situations. A longitudinal study on normally developing children indicated that girls have better metacognitive knowledge between age 10 -14. The study also revealed that text comprehension is positively correlated with individual differences in metacognitive knowledge of strategy use. These two studies suggest that text comprehension in dyslexia is not related to the students’ metacognitive skills, metacognitive knowledge or metacognitive experiences. However, for normally developing children, their text comprehension is related to their level of metacognition.

Question generation often helps students understand the texts better. “An ideal learner – self-regulated to active – is a person who asks deep questions and searches for answers to thought -provoking questions” (Garcia et al. 2014, p. 385). A number of research has been done to determine the effect of question generation to reading. García et al. (2014) examined 72 ninth-grade students in science class. The results indicated that “question-generation training influenced how students learned and studied, specifically their metacognition” (Garcia et al. 2014, p. 385). Participants in group 1, who received question-training by providing prompts had the highest score on metacognitive knowledge and self-regulation. This suggests that effectiveness of question generation depends on the person's metacognitive knowledge. It is important for teachers to recognize students' metacognitive skills before letting students generate questions.

Metacognition in Writing

Metacognitive abilities are essential in writing, especially in university level courses. Although instructors often urge students to reflect on their writing and revise it several times, it is rare for students to actually evaluate and re-work their writing in a detailed fashion. Parrott and Cherry (2015) brought up this concern and suggested a new teaching tool to make students think about their writing more actively. The strategy is called process memos.

Process memos are guided reflections submitted from students and teachers. Students submit process memos after writing the first drafts and the final versions of their papers. For the first draft, students are asked to reflect on their paper, the helpfulness of the rubrics, questions regarding the assignment, the strengths and weaknesses of their paper, and what they think they need to improve in the final version. After this, teachers mark the paper and provide feedback. In the second process memo, students are asked to reflect on the feedback they received from the teacher. Questions include “which comments were most helpful, and why?” (Parrott et al, 2015, p. 147). Parrot et al. started testing out process memos in 2005 and fully implemented it in a study in 2015. The study included 242 university students in various sociology courses, including introductory courses and more advanced courses. The results suggested that process memos help both students and teachers to actively engage in the process of writing. Teachers get feedback on their instructional qualities so that they can improve their teaching in the future and make sure the rubrics are clear. Although some students did not take process memos seriously and provided insufficient comments, most students found this method useful in improving their writing skills. Most students were honest about their comments. Process memos also promoted communication between students and teachers, as they allowed teachers to directly respond to students' reflections. Another advantage of using process memos, according to Parrot and Cherry is that they engage every student in the class, so students who feel too shy to raise their hands and ask questions in class can benefit. It is an efficient way to enhance students' metacognitive awareness, and guide students' writing step by step.

Metacognition in Science Education

As mentioned before, metacognition is important in the field of science education because higher levels of science require students to reconstruct perceptual knowledge and procedural strategies on their own. It is also important for students and teachers to be aware of their beliefs about science, as they affect their learning and teaching respectively. However, a number of teachers take these beliefs for granted. A study (Abd-El-Khalick et al., 1998) where researchers interviewed pre-service teachers and students revealed that not many teachers teach beliefs about science or the nature of science. Some teachers in this study believe that teaching the nature of science is not as important as teaching other concepts in science.

This becomes a problem when students proceed to university and learn higher levels of science. It also affects students' motivation to study science because it hinders their understanding of science. Schraw, Crippen & Hartley (2006) agrees to this and state that “effective instruction should help students and teachers aware of the beliefs they hold about science” (Schraw, Crippen & Hartley 2006, p.117). Then, how do we promote metacognition in science learning? Schraw et al suggest that “authentic inquiry promotes metacognition and self-regulated learning because students are better able to monitor their learning and evaluate errors in their thinking or gaps in their conceptual understanding”(Schraw et al, 2006, p.119). This is part of the inquiry based learning that many researchers believe it is effective for science teaching. In inquiry based learning, students pose questions and construct solutions. Another way to enhance metacognition in classroom, as suggested by Schraw, Crippen and Hartley, is by collaboration among students and teachers. This will promote feedback, modeling and social interaction, which will benefit in students' motivation and epistemological beliefs. Similarly, metacognition and self-regulated learning is highly discussed in math learning and instruction research. Please refer to the Learning Mathematics chapter for more information.

Metacognition through a developmental lens

Research shows that metacognitive abilities are related to factors such as age and biology (citation 4). It is therefore important to understand the developmental progression in order to apply the theory.

Maturation Bases

Age as a factor

• Young children
  • Theory of Mind
• Adolescents
• Adults

Biological Bases

Deficits in learning

SRL Strategies

Self-regulated learning is a vastly growing topic of interest, especially within the field of educational psychology (Rosman et al., 2015). The goal lies in seeking to integrate theories into a cohesive framework that can be used to guide educators and learners. In a review of the literature regarding self-regulated learning, Paris & Paris (2001) summarize several principles as being practical applications of SRL in the classroom environment. They categorized them within the confines of four ideas that integrate the research in this field. Firstly, students are capable of better understanding what learning entails when they can make self-appraisals. This means that by analyzing their ways of learning and comparing it to others, evaluating what they have and don’t have knowledge about, and assessing their efforts students can enhance their awareness of the process of learning. Secondly, self-management of thought and affect allows for greater flexibility in the ability to problem solve adaptively. By setting realistic goals that focus on improving their competence, effectively managing their time through continual monitoring, and reviewing/revising learning strategies students can commit to higher performance standards for themselves. Thirdly, with respect to instruction self-regulated learning can be taught in a variety of ways that allows for accommodation. SRL may be taught to students explicitly (directed reflection, discussions around metacognition, practice with experts); it can be taught indirectly (modeling, and reflective practices); and it can be prompted with individualized mapping of growth. Lastly, it is believed that self-regulation is intertwined with the narrative experiences related to identity for each student. The way in which students choose to assess and monitor their behavior is consistent with the identity they desire and by being a part of a reflective community of learners/instructors, one can enhance the level of depth by which they look at their self-regulated learning.

While there may be variation in the ways in which students self-regulate, the importance lies in understanding how children come to self-regulate in the first place. According to Paris & Paris (2001), SRL can be enhanced in three ways: (1) Indirectly through experience: repeated exposure to experiences in school can elicit learning of what is expected by the teacher and what is most beneficial to the student. An example of this is the learning that double-checking work, although initially time-consuming, can be beneficial in the long-run and will therefore be advantageous to do the next time around also. (2) SRL can be taught directly: students can learn from the explicit instruction of educators who highlight effective strategy use, and increase awareness of the importance of goal-setting. As an example, an instructor may emphasize the strategic steps of how to analyze a word problem from start to finish. (3) Self-regulation can be elicited when integrated with active practices that embody SRL within them. An effective practice that encompasses SRL into it is collaborative learning projects where each student takes on responsibility for a portion of an overall project. Self-regulated learning appears throughout such projects as students are bound to learn from the feedback of others, and from analysis of what they have done to contribute to the whole. These three outlined ways of enhancing SRL are often found in combination as students get exposed to experiences with their peers and instructors in their educational environment.

Throughout education, students are taught various learning strategies to incorporate into their studies; yet as research shows, it is not always enough to know such learning strategies but to be able to regulate the use of the strategy effectively (Leutner et al., 2007). In a computer-based training experiment by Leutner, Leopold, and Elzen-Rump (2007), the researchers were able to show the benefit of not only teaching students a useful cognitive learning strategy (highlighting) but of additionally providing training on how to monitor and regulate the use of this tool with metacognitive learning strategies. The study involved 45 college students randomly assigned to either a treatment group that received no training at all, one in which they were trained only in the cognitive strategy of highlighting, and the other in which training on highlighting was combined with training on self-regulation in learning about new-born babies. The combined self-regulation training group had a version of the computer-program that included steps on how to obtain metacognitive control with time to practice the control strategy and apply it in the next section of their text learning. The results of the study indicate that students trained in both strategy-use and metacognitive control of this strategy use were more successful in applying their learning in a goal-oriented way when tested after the training. The cognitive-strategy use only group performed better than the control group, which received no training at all; however the combined training group outperformed both, indicating that, while strategy use can improve outcome performance, learning can be enhanced even further when students are taught to regulate such strategies.

Incorporating Technology

The Link Between Technology & SRL

The undeniable growth in technological use, Prensky (2001) in his article, suggests that teachers must find ways to use technology to enhance students’ learning experience. Also, teachers must know the “needs” of students and take advantage of the available information, combined with computing power, to deliver content to digital natives in a convenient and comfortable manner. Today, technology interventions can consist primarily of learning tools for the digital natives’ self-regulatory learning process and goal achievements . Students are comfortable trying different kinds of new technologies to plan their own learning activities, monitor themselves, and self-evaluate their own learning outcomes. In regards to students, their previous knowledge, interests, and motivation can directly influence their individual learning experiences, performances, and outcomes in technology enhanced SRL environments.

For example, Ma et al. (2015) provide the example of Intelligent Tutoring Systems (ITSs) being implemented in learning environments to investigate the possibilities and approaches of using technologies to support students’ learning outcomes. ITSs as computer systems, bring intelligence to computer-based instruction by engaging students in learning activities and interaction according to their behavior. ITSs provide knowledge of the subject domain and “can perform task selection by characterizing each task as a set of production rules required to complete it and each student as a set of production rules that most need to be practiced, and then finding the best match” (Ma et al., 2015, p.4). ITSs provide an opportunity for each individual learner to choose and monitor their own tasks, which can be more effective and useful for students who have different knowledge levels and learning abilities. The individualized learner-control options provided by ITSs can encourage students to assume control over their learning, which will promote their self-motivation and foster their self-regulated learning

Kauffman, Zhao, and Yang (2011) have come to similar conclusions as Ma et al.’s regarding the use of technologies to facilitate and support self-regulation and metacognition among learners. More specifically, Kauffman et al. (2011) find that the use of technologies in educational settings can help people to teach and learn through multimedia and in organizing course content. For instructional designers and instructors, they can create and deliver the course content through both web-based pedagogical and multimedia tools to their students. Various media formats can help educators to maintain the attention of learners, increase their learning interests, and better integrate them in the self-regulated learning process (Kauffman et al., 2011). On the other hand, learning through multimedia can help learners obtain relevant information to complete tasks and “provide them multiple options to view the course content in various media formats” (Kauffman et al., 2011, p.43) that will increase their learning interests and help them engage in self-regulated learning. In addition, the content creation tools will employ powerful learning strategies, enabling learners to demonstrate their understanding of course content through media formats to monitor and evaluate their own learning process (Kauffman et al., 2011).

Issues that learning technologies have brought to SRL context

The increased rate in which students have been using digital technologies has introduced many challenges to SRL.One of the biggest challenges is that technologies cannot fully monitor learners’ understanding and are controlled by learner themselves, which can be less effective in developing the students’ cognition skills during SRL. In this way, learners lose their freedom to learn in SRL process and they have to receive verbal feedback and explanation from educators during their learning process to better understand the flow of information.For instance, Learning Management Systems (LMS) distribute learning content, organize the learning processes, and build connections between learners and teachers through the interface. However, students do not really get any freedom in their own learning process on the LMS. Instead, teachers monitor their understanding the whole time when they participate in LMS courses. In contrast, Personal Learning Environments (PLE) give each student opportunities to select and control the services they want to use instead of control over content and learning strategies. Lack of guidance in course content and methodologies in PLE makes learning less efficient in the students’ self-regulated learning process ; in addition to, limiting their effectiveness of SRL.

Opportunities that learning technologies have brought to SRL context

Although there are many concerns regarding technology use in SRL, we cannot deny that the role of technologies have great potential important in helping students with the transmission and retention of the knowledgeduring SRL process. By accessing different sources of information, Simao et al. (2008) find out that technology involves new ways of planning and accomplishing learning tasks, which can result in the development of specific skills. Learners have to be capable of self-regulating their learning process in order to achieve the goals they established or that were established for them. On the other hand, teachers should encourage social and intellectual environments which promote self-regulated learning.

Many academic articles and reports seem to hold the same view. It has been shown that learning technologies can serve as an important determinant in fostering self-regulation.In fact, the last part of this paper will provide several technology examples on recent student experiences with learning technologies in SRL. The review is intended to demonstrate the effectiveness of learning technologies tailored engage students’ self-regulation in the context of self-regulated learning. Specially, when learning technologies are deliberately used to support self-regulation, motivation, and engagement in online learning contexts, students’ academic performance will significantly improve towards learning.

In addition, the incorporation of learning technology to support self-regulated learning had been addressed by some researchers, teachers, colleges, and universities. They wish to discern the role that learning technologies play in self-regulated learning environment. Do learning technologies fit into the education landscape as an alternative mode of teaching and learning or a substantial supplement? Can learning technologies bring opportunities for increased interaction between teachers/students and students/students? How can learning technology develop students’ metacognition, motivation, and behaviour to achieve their learning goals in SRL. Additionally, the last part will reveal the role technologies play in self-regulated learning and why incorporating technology is essential for self - regulated learning. Several technologies have been developed to engage students in self-regulation, such as Betty’s Brain, MetaTutor, and nStudy. Technologies play a critical role in students’ SRL activities, which will allow them to select searching strategies, monitor strategy impact, and critically evaluate accessed information, all to promote metacognitive reflection. This part will describe three specific existing technologies and illustrates their implications on supporting and promoting students SRL.

Figure 13. Betty’s Brain primary interface

Figure 14. MetaTutor Interface

• Betty’s Brain

Betty's Brain is a teachable agent system created at Vanderbilt University to support students’ self - regulated learning and strategy use In Betty's Brain, students first “learn by reading about scientific phenomena” (Roscoe et al., 2013, p.287). Based on the knowledge they gain, they will construct a simplified visual representation of concept maps to represent their understanding and to teach the computer agent character Betty via the concept maps they created. Roscoe et al. (2013) in their article explain how constructing these concept maps can help students to integrate and organize both new and prior knowledge while assisting them in understanding “how individual concepts cohere within deeper principles” (p.287). In order to teach someone else, students have to learn and solve the learning problem first. When learning by teaching, students receive feedback from the Betty program and are motivated to transfer knowledge from one context to another, which results in greater metacognition and self-regulating practices. In this way, they will be able to monitor themselves and teach their agent to perform better. In the end, Roscoe et al. (2013) summarize that students can finally “apply metacognitive processes to detect and repair map errors to improve accuracy and completeness” (p.289) by using Betty’s Brain.

• Azevedo's MetaTutor

According to Khosravifar et al. (2013), MetaTutor is a research-based learning tool for improving students’ academic performance. By applying different interactive and strategic intellectual techniques, students will better self-regulate their cognitive, affective, metacognition, and motivation in learning processes . MetaTutor is designed to train and foster high school and college students’ learning about complex and challenging science topics through hypermedia MetaTutor detects, models, traces, and fosters students’ self- regulated learning about human bodily systems , which is mainly based on cognitive models of self-regulated learning. All the users required by MetaTutor to complete the training session on SRL processes before they begin to explore and access the content on the hypermedia learning environment. There are four pedagogical agents in the hypermedia learning environment, which not only provide feedbacks to scaffold participants SRL skills and content understanding, but also help participants to navigate the system, guide them setting appropriate goals, monitor their progress toward their learning goals, and deploy SRL cognitive strategies such as summarizing and note-taking.

By using MetaTutor, students can interact with different agents and enact specific SRL learning processes by their personal preference. MetaTutor can track all participant interactions and record user behaviours in a log file. When the data show that a student is using ineffective strategies, the agent might provide feedback by alerting the student to use a better learning strategy. The students could use the feedback from MetaTutor to improve their own learning choices and outcomes in the learning environment . At the same time, teachers can collect data from MetaTutor to gain a greater understanding of how students interact with MetaTutor and their learning experiences in self-regulatory processes. Although pedagogical agents in MetaTutor cannot control students overall learning progress in the learning environment, they still provide useful learning strategies to help students and teachers in planning and monitoring.

Figure 15. nStudy browser, table of quotes, and linking tools

• nStudy

Professor Winne and his research team have designed nStudy, a web-based learning tool, for learners to search, monitor, assemble, rehearse, translate during their self-regulated learning process. The design of nStudy allows both learners and researchers to be active in their learning and researching through a web-based learning environment. In nStudy, they can organize their learning objects by creating, manipulating, and linking them as needed, to help themselves achieve their learning goals. As with Betty’s Brain, they can also build learning concept maps and then link, group and spatially arrange them. Linking allows learners to create their personal learning network of data and structure the information in their own way, which can be optimal for them to improve their skills in interacting, elaborating, and managing information.

nStudy provides both individual and group learners a workspace for them to collaborate, exchange information, and discuss content online, which can create opportunities for them to contact each other to support their collaborative learning. In addition, the ability to exchange information across workspace can be “structured by roles and prompts create opportunities for students to self-regulate, to co-regulate each other’s work, and to share regulation” (Winne & Hadwin, 2013, p.302). As learners and researchers use nStudy’s tools to study or research, the system collects trace data can reflective of particular cognitive and metacognitive events during their self-regulated learning

Facilitating and Encouraging SRL

Self-regulated learning (SRL) is a process that assists students in managing their thoughts, behaviors, and emotions in order to successfully navigate their learning experiences. This process requires students to independently plan, monitor, and assess their learning. SLR is an important predictor of student academic motivation and achievement. The construct of self-regulation refers to the degree to which students can regulate aspects of their thinking, motivation and behaviour during learning. In practice, self-regulation is manifested in the active monitoring and regulation of different learning processes.

Self-regulated learning is not asocial in nature and origin. Self-regulatory processes often develop gradually within an environment that balances structure with opportunity for autonomy. Research shows that self-regulatory processes are teachable and can increase students’ motivation and achievement. Each self-regulatory process can be learned from instruction and modeling by parents, teachers, coaches, and peers. In addition, numerous studies reveal that Interventions and trainings on self-regulated learning can enhance students’ academic performance In a study of high school students, Labuhn et al. (2010) found that learners who were taught SRL skills through monitoring and imitation were more likely to elicit higher levels of academic self-efficacy (i.e., confidence) and perform higher on measures of academic achievement compared to students who did not receive SRL instruction. Accordingly, students should practise self-regulated learning throughout their whole school career, and teachers need to cope with the task to foster their students’ self-regulated learning behaviour.

By teaching students to be more self- regulative, teachers may experience greater success in promoting academic achievement, motivation, and life-long learning. Teachers can help students become self-regulated learners who can use effective strategies to help them to make plans and set goals for a learning task, monitor the learning process, and evaluate learning performance with a view to improving it next time. Teachers can promote self-regulated learning in classrooms either directly by teaching learning strategies or indirectly by arranging a learning environment that enables students to practice self-regulation.

Developing Self-Regulated Learning

According to Zimmerman (2002) , self-regulated learning process can be divided into three distinct phases:

Forethought and Planning Phase involves analyzing the learning task and setting specific goals toward completing that task. In this phase, teachers instruct students on effective approaches, provide structured and explicit instruction, model and explain the strategies, and help students to generalize the strategy to other similar learning tasks.

Performance Monitoring Phase includes employing strategies to make progress on the learning task, monitoring the effectiveness of the strategies, and monitoring motivation for completing the learning task. Teachers can organize activities, provide close monitoring and specific feedback to help students learn to use new strategies. As students learn how to execute the strategies independently, teachers gradually fade instruction and transition into the role of guide.

Reflection on Performance Phase focus on evaluating performance on the learning task, and managing emotional responses related to the outcomes of the learning experience. Teachers can provide support by encouraging peer evaluation and reflection, facilitating assessment, and continually relating findings back to the learning goals. Teachers should also prompt students to share what worked well during the learning process, contribute to student self-efficacy and motivation, and provide praise focused on their efforts and use of effective strategies.

Figure 16. The Cycle of SRL

Self-regulatory skills are not automatically acquired. The developmental stages of self-regulatory skills consist of four levels: observation, emulation, self-control, and self-regulation. Observation level skills are acquired through modeling which provides learners with an image of successful performance. This helps students establishing general performance standards and conveys a strategy to control motivation during the process of acquiring a skill. On the emulation level, students perform a skill using a general strategy learned through modeling, while teachers’ feedback and guidance are critical to improve accuracy of performance. In addition, social reinforcement, such as praise or encouragement, also increases students’ motivation. Self-control level involves structured practice and self-observation. Students practice a skill in structured settings on their own. Students may refer to and internalize a model’s performance, and should focus on process rather than outcomes. Self-regulated level skills are perform in unstructured settings. Student should focus on effectiveness or quality of performance rather than mere execution of a learned skill, and adjust their performance according to personal and environmental conditions. They can perform skills independently, but still need social support occasionally. Figure 16. shows the cycle of SRL.

Self-Regulated Learning Strategies for Students

Types of Self-Regulated Learning Strategies

There are four types of SRL Strategies that can facilitate learning: Cognitive strategies include rehearsal, imagery, elaboration and transformation or organization of materials. Elaboration helps students to connect new material to the prior knowledge; imagery refers to mental pictures that students form to enhance their memory; rehearsal helps students sustain information in their working memory; transforming and organizing strategies include summarizing, outlining, note taking or rearranging materials to make learning easier.

Metacognitive strategies include planning, self awareness and monitoring, and self-evaluation. The most important planning strategies are task analysis and goal setting. Commonly used monitoring strategies are self-recording and self-experimenting. Self-testing is a strategy associated with self-monitoring and self-evaluation. Self-instruction and attention focusing are strategies to monitor or control attention. Self-instruction helps students to focus on a task and enhance their encoding and retention of materials. Attention focusing is used to eliminate distraction in order to concentrate on a task.

Management strategies are used to create the optimal learning conditions, which include control of learning environment, time management, and help seeking. Self-recording is generally used to improve time management skills. Encouraging students to ask questions increases students’ help seeking behavior. The structure of the classroom, including feedback and interaction, also affects students’ help seeking.

Motivational strategies help students enhance and sustain their motivation to engage in academic tasks. Examples are the formulation of a learning objective, which enhances the goal orientation; the development of a positive style of attribution, which enhances the student’s self-efficacy; interest enhancement which manipulate materials to make them more interesting or challenging; and self-talk which refers to verbal self-encouragement.

Table1. Types of Strategies:

Strategy Type	Description	Examples
Cognitive strategies	This type includes strategies to interact with the content.	Rehearsal, imagery, and organization of materials
Metacognitive strategies	This type includes strategies to organize, monitor and assess learning.	Task analysis, self-recording and self-experimenting
Management strategies	This type includes strategies used to create optimal learning conditions.	Time management, and help seeking
Motivational strategies	This type includes strategies to enhance and sustain student’s motivation.	Formulation of a learning objective and development of a positive style of attribution

Teach Student SRL Strategies – Develop Self-Regulated Learners

Teachers play a principal role in developing students' capacity for self-regulation. To promote SRL in classrooms, teachers must teach students the self-regulated strategies that facilitate learning. The most common and effective SRL strategies include: goal setting, planning, self-motivation, attention control, flexible use of learning strategies, self-monitoring, appropriate help-seeking , and self-evaluation.

Goal Setting: Establishing personal goals helps students focus on practical and specific actions that they can undertake to improve their learning. Short-term attainable goals are often used to reach long-term aspirations. Setting proximal goals can enhance self-efficacy and skill development . Teachers should encourage students to set short-term goals to help them tracking their progress, thinking about what they expect to learn and to be able to do.

Planning: Planning can help learners establish well thought goals and strategies to be successful. Teaching students to approach academic tasks with a plan is a viable method for promoting SRL. Teachers can explore with students their plans for reaching the goals they set. Students can then use the plan to remind themselves of the steps and procedures to accomplish the goals and to make any needed adjustments.

Self-Motivation: Students’ behaviors regarding choice of tasks, as well as their effort and persistence in academic tasks, are directly related to their intrinsic motivation. Students with high intrinsic motivation are more likely to use metacognitive strategies. Intrinsic motivation may be enhanced by increasing perceived autonomy, perceived competence, and task mastery goal orientation. Stressing the importance of the learning process, providing choice and allowing opportunities for self-direction can enhance intrinsic motivation by increasing the feeling of autonomy.

Attention Control: Self-regulated learners must be able to control their attention. Teachers can help students control their attention by removing stimuli that may cause distractions, and providing students with frequent breaks to help them build up their attention spans.

Flexible Use of Strategies: Successful learners are able to implement multiple learning strategies across tasks and adjust them as needed to facilitate their progress. By modeling how to use new strategies, organizing the classroom to support the related activities, and providing appropriate scaffolding as students practice, teachers can help learners become independent strategy users.

Self-Monitoring: Strategic learners assume the ownership for their learning and achievement outcomes. Teachers can encourage self-monitoring by having students keep a record of the number of times they worked on learning tasks, the strategies they used, and the amount of time they spent on working. This practice allows students to visualize their progress and make changes as needed.

Help-Seeking: Self-regulated learners rather frequently seek help from others when necessary. Classrooms with mastery goal orientation encourage students to ask for help without feeling embarrassed. Teachers can promote positive help seeking behaviors by providing students with on-going progress feedback and allowing students opportunities to re-submit assignments after making appropriate changes.

Self-Evaluation: Teachers can promote self-evaluation by helping students to monitor their learning goals and strategy use, and make changes to those goals and strategies based on learning outcomes. Self-evaluation activities can include using checklists, summarizing learning content, developing and responding to self-questions, and seeking feedback from peers. Figure . shows the basic concepts and corresponding actions regarding SRL.

Figure 17. Facets of SRL – concepts and corresponding actions.

Promote Self-Regulated Learning in Classroom

Instructional Strategies for Encouraging Self-Regulated Learning

Teachers' instructional techniques can enhance students' motivation and promote self-regulated learning. Kobayashi (2006) described four principles for instructors to embed SRL in instruction: guide learners to prepare and structure an effective learning environment; organize instruction and activities to facilitate cognitive and metacognitive processes; use instructional goals and feedback to present student monitoring opportunities; and provide learners with continuous evaluation information and occasions to self-evaluate.

• Direct instruction and modeling

Being explicit about how to use different learning strategies helps students to develop a suite of tools they can draw from as they work through the learning. Direct instruction of SRL involves explicitly explaining different strategies to students, as well as how to use those strategies. This kind of instruction focuses on modeling and demonstration; it can be the best initial strategy for encouraging students to be more self-regulative.

Teachers can act as role models in applying a strategy and verbalizing thought processes, or activate students to engage in strategic behaviour by asking questions. For example, in language classes teachers can show a text on the screen and tell students their thoughts about it as they read through it, pausing for questions and comments, such as: “Is this making sense? What’s the main idea here? I think I need to go back to the beginning of this paragraph to re-read so that I’m sure I understand.” Similarly, teachers can model the writing process by thinking aloud as they write on the board. The self-questions and comments might be: “Am I expressing my ideas clearly? Will my readers understand what I’m trying to say? Am I following my plan or outline? If not, do I need to make a new plan?” During the reading or writing process, students can make notes recording their reactions to indicate their understanding of the main idea, their questions about the learning contents and their personal opinions. On the other hand, teachers can explicitly tell students about a certain activity, by explaining how this strategy improves learning performance, and telling students how to employ, monitor, and evaluate this strategy . In primary classrooms, teachers can use dialogue to encourage students to share their ideas, by asking questions such as “What do you think? “ “Why do you think that?” They can also provide explicit instruction on collaborative skills and communicative behaviours that support shared meaning making

• Guided and Independent practice

Guided practice is another way teachers can help improving SRL and motivation. During guided practice, the responsibility for implementing the learning strategy shifts from teachers to students. Student-teacher conferencing is one-way teachers can help students in setting goals and monitoring their strategy use and progress. Independent practice should follow guided practice. During this process, students are given opportunities to practice the strategy on their own, which can ultimately reinforce autonomy.

Teachers should provide students with opportunities for self-reflective practice that improves their skills to monitor, evaluate, and adjust their performance during the learning process. The strategies include asking open-ended questions, requiring students to provide reflection, summarizing the key points of the learning content, and providing opportunities to discuss and answer their questions. For example, to increase SRL and reading achievement in language classes, teachers may request students to record titles of books they have read, record and graph minutes and pages read in reading log, set milestones for systematically increasing challenge level of book selections, and give weekly reflections in reading log.

Teachers should encourage students to practice effective strategies on a variety of learning tasks on an ongoing basis. This helps to promote both generalization and maintenance of the strategy, facilitates students to rehearse the use of strategies, develop ways to monitor and evaluate their performance, and actively engage students in the modification and construction of new strategies.

• Social support and feedback

Social support from teachers and peers can serve an important role as students are learning to be more self-regulative. Often, social support comes in the form of feedback. Labuhn et al's. (2010) research indicated that students who received feedback from their teachers were more likely to accurately use SRL strategies to improve their mathematics scores. Effective feedback includes information about what students did well, what they need to improve, and steps they can take to improve their work. Teachers' feedback helps students to evaluate progress and assess their internal constructions of goals, criteria and standards. Teachers should provide formative assessments that not only show students how they are doing, but also help them learn how to generate internal feedback and monitor their own progress. According to Nicol and Macfarlane-Dick's (2006), effective feedback should: clarify what good performance is; facilitate the development of self-assessment in learning; deliver high quality information to students about their learning; encourage teacher and peer dialogue around learning; encourage positive motivational beliefs and self-esteem; provide opportunities to close the gap between current and desired performance; and provide information to teachers that can be used to help shape teaching.

Teachers play a significant role in the development of effective self-assessment which is a metacognitive skill connected to students' attributions of success and motivation. To promote SRL, teachers can engage students into the assessment process, by clearly defining the measurable and attainable goals set with students' input, modeling and accounting for goals, and adjusting and differentiating questions to match students levels. Active involvement of students in the learning process can contribute crucially to constructing a positive learning climate. In higher education, a useful way to facilitate SRL is setting up an interactive forum on the course website, where students can discuss the course material and learn from each other, thereby enhancing the student-centered learning.

• Other instructional strategies

Self-observational technique is a tool for increasing student awareness in SRL. Self-recording can increase student's awareness of the errors they made, so that appropriate strategies could be developed and implemented. Graphing is an helpful method that help students develop the belief of control over the learning. One example is plotting grades and writing down the learning strategies used to achieve these grades to highlight the link between the strategies used and the performance outcomes.

Reflective practice is an important and effective tool for teachers to adapt and revise pedagogical styles to accommodate students' needs. This practice enables teachers to investigate the possible reasons that explain the effectiveness of a given instructional strategy. Through thoughtful reflection, experimentation, and evaluation, teachers can better create meaningful learning experiences for students.

Classroom Environment that Facilitates Self-Regulated Learning

An important approach to fostering SRL is arranging a supportive learning environment, which is made of student and teacher characteristics, the learning contents and tasks, and the teaching methods. Suitable learning environments can enable and encourage students to learn in a self-determined way. Young (2005) described the following guidelines to increase students’ motivation and foster SRL in classrooms, i.e., giving positive feedback that supports the development of competence and task mastery orientation, providing activity choice to support the development of self-determination and autonomy, encouraging social connections in learning, and providing feedback on learning performance for promoting motivation.

Students’ motivation can be significantly influenced by perceived learner control in the classroom, and by the way of teachers' feedback. A classroom environment with high task autonomy, together with positive feedback in an informational style, will maximally increase intrinsic motivation. Teachers should maintain an optimum balance of learner and teacher control in classrooms, and provide effective feedback. Students are more likely to take challenging tasks when teachers provide specific and qualitative feedback frequently and deemphasize the importance of grades. Participating meaningful activities, having choice of task and working cooperatively can help students in increasing self-efficacy

Teachers should promote a culture of generosity and respect for individual views, such as promote help seeking, help giving, and negotiation of different views, through the development of positive and supportive learning environments. This involves encouraging positive feelings towards challenging tasks, understanding mistakes as learning opportunities, acknowledging and responding to negative emotions connected to learning experiences, and helping students retrain helpless beliefs.

Activities to Foster Self-Regulated Learning in Classroom

There are various type of activities that can promote SRL. Complex collaborative activities promote students’ monitoring of their own performance and the others’ task-related activities. It helps students to plan actions, formulate ideas, check progress against goals, and reformulate understandings on the basis of group contributions. Meaningful tasks (i.e., tasks relate to students' past experiences, their interests, and have real implications for their learning) can promote motivation and foster SRL. Activities that include cognitive demands targeting individual zones of proximal development are associated with SRL as well. Multidimensional tasks allow student to find comfortable levels of challenge. Playful activities can provide engaging opportunities for self-regulation in primary classrooms.

Paris et al. (2001) described four types of principles that teachers can use to design activities in classrooms to promote students' SRL, which include self-appraisal that leads to a deeper understanding of learning; self-management of thinking, effort, and affect that promote flexible approaches to problem-solving; self-regulation that can be taught in diverse ways; and self-regulation that is woven into the narrative experiences and the identity strivings of each individual.

To help students in becoming self-regulated learners, teachers can create opportunities for students to share information in pair-work and group-work and transfer what they learned; organize open-class discussion about objectives and learning strategies; provide pre-learning activities to establish and share objectives, and post-learning activities to further practice strategy use and consolidate knowledge acquisition; and create reflection moments at the end of class for students to reflect about what they learned. Teachers can also use questionnaires such as the Motivated Strategies for Learning Questionnaire (MSLQ) or Learning and Study Strategies Inventory (LASSI) to give students feedback on their motivation beliefs and learning strategies.

Following are some examples of specific activities that teachers can incorporate into classroom to facilitate SRL: “Think-Pair-Share” activity allows students to reflect on questions, discuss responses with a partner and share thoughts with whole class. “Retrieval Practice” aids in self-observation and promotes meaningful, conceptual, and long-term learning. “Sorting-Chunking-Organizing Information” activity helps students to organize concepts and terminology to make sense from information. “Reading Reflections” can help students with self-monitoring and reflective thinking. “Exam Wrappers” activity prompts students considering the strategies they used to prepare for the test, and reflecting on the effectiveness.

Glossary

Action Control: Ability to control action (e.g. motivation, concentration) that help an individual self-regulate.

Cognitive Modeling: