# 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.

## References
# 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.

### Self-Determination Theory
## 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.

## References

* ↑ American Psychological Association, Coalition for Psychology in Schools and Education. (2015). Top 20 principles from psychology for preK-12 teaching and learning. Retrieved from http://www.apa.org/ed/schools/cpse/top-twenty-principles.pdf (PDF, 662KB).
* 1 2 Sarah Mae Sincero (2012). Nature and Nurture Debate. Retrieved Apr 05, 2016 from Explorable.com: https://explorable.com/nature-vs-nurture-debate
* 1 2 3 Bruning, R., & Schraw, G., & Norby, M., (2011). Cognitive Psychology and Instruction, 5th ed.
* ↑ McDevitt, T.M., & Ormrod, J.E.(2010). Nature and Nurture. Retrieved from www.education.com/reference/article/nature-nurture.html
* ↑ https://www.google.ca/?gws_rd=ssl#q=define+behaviourism
* ↑ McLeod, S. A. (2015). Skinner - Operant Conditioning. Retrieved from www.simplypsychology.org/operant-conditioning.html
* ↑ http://www.simplypsychology.org/operant-conditioning.html
* ↑ McLeod, S. A. (2016). Bandura - Social Learning Theory. Retrieved from www.simplypsychology.org/bandura.html
* ↑ http://psychology.about.com/od/profilesofmajorthinkers/p/bio_bandura.htm
* ↑ http://www.ascd.org/publications/educational-leadership/dec92/vol50/num04/What-Brain-Research-Says-About-Paying-Attention.aspx
* ↑ http://www.happy-neuron.com/brain-and-training/attention
* ↑ Lau, J., & Chan, J. (2004-2016). What is critical thinking. Retrieved from http://philosophy.hku.hk/think/critical/ct.php
* 1 2 3 Schraw, G., & McCrudden, M. (2013), Information Processing Theory. Retrieved from www.education.com/reference/article/information-processing-theory.html
* ↑ Salomon, G., & Perkins, D. N.. (1998). Individual and Social Aspects of Learning. Review of Research in Education, 23, 1–24. Retrieved from http://www.jstor.org.proxy.lib.sfu.ca/stable/1167286
* ↑ http://www.simplypsychology.org/Zone-of-Proximal-Development.html
* ↑ McLeod, S. (2010, December 25). Zone of Proximal Development - Scaffolding | Simply Psychology. Retrieved February 27, 2016, from http://www.simplypsychology.org/Zone-of-Proximal-Development.html
* ↑ http://www.ascd.org/publications/books/101039/chapters/A-Framework-for-Building-Shared-Responsibility.aspx
* ↑ Education Theory/Constructivism and Social Constructivism in the Classroom. (n.d.). Retrieved February 27, 2016, from http://www.ucdoer.ie/index.php/Education_Theory/Constructivism_and_Social_Constructivism_in_the_Classroom
* ↑ https://steinhardt.nyu.edu/scmsAdmin/uploads/007/642/McClowry%20et%20al%202013%20Cluster%20article%20.pdf
* ↑ http://www.instructionaldesign.org/theories/situated-learning.html
* ↑ Education Theory/Constructivism and Social Constructivism in the Classroom. (n.d.). Retrieved February 27, 2016, from http://www.ucdoer.ie/index.php/Education_Theory/Constructivism_and_Social_Constructivism_in_the_Classroom
* ↑ Facilitation Theory. (n.d.). Retrieved February 27, 2016, from http://teorije-ucenja.zesoi.fer.hr/doku.php?id=instructional_design:facilitation_theory

# 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

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

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.

### 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

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

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.
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 functioningControversial 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

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.

## References

* ↑ Resnick, L. B., Greeno, J. G., & Collins, A. M. (1996). Cognition and instruction.Handbook of Educational Psychology, 15-46.
* ↑ Sternberg, R. J., & Sternberg, K. (2012). Cognitive Psychology (6th ed.). Belmont, CA, America: Wadsworth.
* ↑ Gathercole & Alloway, 2006, Practitioner Review: Short-term and working memory impair-ments in neurodevelopmental disorders: diagnosis and remedial support. Journal of Child Psychology and Psychiatry, 47(1), 4–15.
* ↑ Watson, A. (1997). Why can’t a computer be more like a brain? Science, 277(5334), 1934-1936.
* ↑ Leventon & Bauer, 2016.
* 1 2 3 Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive Psychology and Instruction. Fifth Edition. Boston, MA: Pearson.
* 1 2 3 4 Howes, M. B. (2006). Human memory: structures and functions. SAGE Publishing.
* 1 2 Swanson, H. L. & Ashbaker, M. H. (2000). Working memory, short-term memory, speech rate, word recognition, and reading comprehension in learning disabled readers: Does the executive system have a role? Intelligence, 28(1), 1-30.
* 1 2 McLean, J. F. & Hitch, G. J. (1999) Working memory impairments in children with specific arithmetic learning difficulties. Journal of Experimental Child Psychology 74, 240–260.
* ↑ McLeod, S. (2012). Working memory. SimplyPsychology. Retrieved from http://www.simplypsychology.org/working%20memory.html
* 1 2 3 4 Baddeley, A. (1996). Exploring the central executive. The Quarterly Journal of Experimental Psychology, 49A(1), 5-28.
* 1 2 Swanson, H. L. & Ashbaker, M. H. (2000). Working memory, short-term memory, speech rate, word recognition, and reading comprehension in learning disabled readers: Does the executive system have a role? Intelligence, 28(1), 1-30.
* 1 2 McLeod, S. (2012). Working memory. SimplyPsychology. Retrieved from http://www.simplypsychology.org/working%20memory.html
* ↑ Reed, S. K. (2006). Cognitive Architectures for Multimedia Learning. Educational Psychologist, 41(2), 87-98.
* ↑ Schweppe, J., & Rummer, R. (2013). Attention, Working Memory, and Long-Term Memory in Multimedia Learning: An Integrated Perspective Based on Process Models of Working Memory. Educational Psychology Review Educ Psychol Rev,26(2), 285-306.
* ↑ Schweppe, J., & Rummer, R. (2013). Attention, Working Memory, and Long-Term Memory in Multimedia Learning: An Integrated Perspective Based on Process Models of Working Memory. Educational Psychology Review Educ Psychol Rev,26(2), 285-306.
* ↑ Reed, S. K. (2006). Cognitive Architectures for Multimedia Learning. Educational Psychologist, 41(2), 87-98.
* ↑ Reed, S. K. (2006). Cognitive Architectures for Multimedia Learning. Educational Psychologist, 41(2), 87-98.
* ↑ Eilam, B., & Poyas, Y. (2008). Learning with multiple representations: Extending multimedia learning beyond the lab. Learning and Instruction, 18(4), 368-378.
* ↑ Mayer, R.E. (2005). Cognitive Theory of Multimedia Learning. (2nd Ed.), The Cambridge handbook of multimedia learning. Cambridge, NY: Cambridge University Press.
* ↑ Mayer, R.E. (2005). Cognitive Theory of Multimedia Learning. (2nd Ed.), The Cambridge handbook of multimedia learning. Cambridge, NY: Cambridge University Press.
* ↑ Sternberg, R. J., & Sternberg, K. (2012). Cognitive Psychology (6th ed.). Belmont, CA, America: Wadsworth.
* ↑ Ruisel, I. (2010). Human Knowledge in the Context of Cognitive Psychology.Studia Psychologica, 52(4), 267-284.
* ↑ Atkinson, R. C. (1967). Human memory: A proposed system and its control processes. Stanford, CA: Institute for Mathematical Studies in Social Sciences.
* ↑ Hockley, W. (2002). The Modal Model Then and Now. Journal of Mathematical Psychology, Volume 44, 336–345. doi:10.1006/jmps.2000.1306
* ↑ Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education.
* ↑ Garcia, R., Mammarella, I., Pancera, A., Galera, C., & Cornoldi, C. (2015). Deficits in visual short-term memory binding in children at risk of non-verbal learning disabilities. Research in Developmental Disabilities, 45-46. Retrieved from http://www.sciencedirect.com.proxy.lib.sfu.ca/science/article/pii/S0891422215001213
* ↑ Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education.
* ↑ Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education.
* ↑ Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education.
* ↑ Baddeley, A. (2012). Working Memory: Theories, Models and Controversies. Annual Review of Psychology,63, 1-29.
* ↑ Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education.
* ↑ Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education.
* ↑ Bruning, R., Schraw, G., Norby, M., & Ronning, R. (2004). Cognitive psychology and instruction (4th ed.). Pearson Education.
* ↑ Woolfolk, A., Winne, P., & Perry, N. (2016). Educational Psychology. Ontario: Pearson Education.
* ↑ Woolfolk, A., Winne, P., & Perry, N. (2016). Educational Psychology. Ontario: Pearson Education.
* ↑ Woolfolk, A., Winne, P., & Perry, N. (2016). Educational Psychology. Ontario: Pearson Education.
* ↑ Woolfolk, A., Winne, P., & Perry, N. (2016). Educational Psychology. Ontario: Pearson Education.
* ↑ Yingxu,W. (2009). Formal Description of the Cognitive Process of Memorization. M.L. Gavrila et al. (Eds): Trans. on Comput. Sci. V, LNCS 5540, 81-98.
* ↑ Michael,C. C. Kuo, Karen, P. Y. Liu, Michelle, B., Jacqueline, W., Nikki, T., Rosalind,B., & Leung-Wing,C. (2014).Memory Encoding Processes in Young and Old Adults. Arch Neurosci, 2 (1): ei19813
* ↑ Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast frugal way: Models of bounded rationality. Psychological Review, 103, 650-669
* ↑ Patrick, H. , Thorsten, P., Lilian, A. E. W., & Kerstin, J. (2015). Neural Signatures of Controlled and Automatic Retrieval Process in Memory-based Decision-making. Journal of Cognitive Neuroscience, 28;1, 69-83
* ↑ Anderson,J. R. (1974). Retrieval of propositional information from long-term memory. Cognitive Psychology, 6, 451-474.
* ↑ Ericsson, K. a., & Kintsch, W. (1995). Long-term working memory. Psychological Review, 102, 211-245.
* ↑ Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive developmental inquiry. American Psychologist, 34 (10): 906-911
* ↑ Passer, M., Smith, R., & Atkinson, M. (2011). Psychology: Frontiers and Applications, McGraw-Hill Ryerson; 4th edition. ISB-13: 978-0-07-000526-6
* ↑ Passer, M., Smith, R., & Atkinson, M. (2011). Psychology: Frontiers and Applications, McGraw-Hill Ryerson; 4th edition. ISB-13: 978-0-07-000526-6
* ↑ Passer, M., Smith, R., & Atkinson, M. (2011). Psychology: Frontiers and Applications, McGraw-Hill Ryerson; 4th edition. ISB-13: 978-0-07-000526-6
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M.M. (2011). Cognitive Psychology and Instruction, Bostom, MA, Allyn & Bacon. ISB-13: 9780132368971
* ↑ Passer, M., Smith, R., & Atkinson, M. (2011). Psychology: Frontiers and Applications, McGraw-Hill Ryerson; 4th edition. ISB-13: 978-0-07-000526-6
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M.M. (2011). Cognitive Psychology and Instruction, Bostom, MA, Allyn & Bacon. ISB-13: 9780132368971
* ↑ Passer, M., Smith, R., & Atkinson, M. (2011). Psychology: Frontiers and Applications, McGraw-Hill Ryerson; 4th edition. ISB-13: 978-0-07-000526-6
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M.M. (2011). Cognitive Psychology and Instruction, Bostom, MA, Allyn & Bacon. ISB-13: 9780132368971
* ↑ Dosher, B. A., Han, S., & Lu, Z. (2010). Perceptual learning and attention: Reduction of object attention limitations with practice. Vision Research, 50(4), 402-415. doi:10.1016/j.visres.2009.09.010
* ↑ Wherry, R. J. (1932). The curve of forgetting: its statistical application. Journal Of Educational Psychology, 23(8), 621-624. doi:10.1037/h0070645
* ↑ Easley, H. (1937). The curve of forgetting and the distribution of practice. Journal Of Educational Psychology, 28(6), 474-478. doi:10.1037/h0057409
* ↑ Wherry, R. J. (1932). The curve of forgetting: its statistical application. Journal Of Educational Psychology, 23(8), 621-624. doi:10.1037/h0070645
* ↑ Passer, M., Smith, R., & Atkinson, M. (2011). Psychology: Frontiers and Applications, McGraw-Hill Ryerson; 4th edition. ISB-13: 978-0-07-000526-6
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M.M. (2011). Cognitive Psychology and Instruction, Bostom, MA, Allyn & Bacon. ISB-13: 9780132368971
* ↑ Miller, G. A. ( 1956 ) The magical number seven, plus or minus two: some limits on our capacity for processing information . Psychological Review , 101 ( 2 ), 343-352.
* ↑ Miller, G. A. ( 1956 ) The magical number seven, plus or minus two: some limits on our capacity for processing information . Psychological Review , 101 ( 2 ), 343-352.
* ↑ Obaidellah, U. H., & Cheng, P. C. (2015). The role of chunking in drawing Rey complex figure. Perceptual And Motor Skills,120(2), 535-555. doi:10.2466/24.PMS.120v17x6
* ↑ Collins, A.M., & Quillian, M. R. (1970). Does category size affect categorization time? Journal of Verbal Learning and Verbal Behavior, 9(4), 432-438. doi: 10.1016/S00225371(70)80084-6
* ↑ Easley, H. (1937). The curve of forgetting and the distribution of practice. Journal Of Educational Psychology, 28(6), 474-478. doi:10.1037/h0057409
* ↑ Carpenter, S. K., Pashler, H., Wixted, J. T., & Vul, E. (2008). The effects of tests on learning and forgetting. Memory & Cognition, 36(2), 438-448. doi:10.3758/MC.36.2.438
* ↑ Carpenter, S. K., Pashler, H., Wixted, J. T., & Vul, E. (2008). The effects of tests on learning and forgetting. Memory & Cognition, 36(2), 438-448. doi:10.3758/MC.36.2.438
* ↑ Obaidellah, U. H., & Cheng, P. C. (2015). The role of chunking in drawing Rey complex figure. Perceptual And Motor Skills,120(2), 535-555. doi:10.2466/24.PMS.120v17x6
* ↑ World Health Organization. (2016). Autism spectrum disorders. Retrieved from http://www.who.int/mediacentre/factsheets/autism-spectrum-disorders/en/
* ↑ Vos, T., Barber, R. M., Bell, B., Bertozzi-Villa, A., Biryukov, S., Bolliger, I., & ... Atkins, e. S. (2015). Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: A systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 386(9995), 743-800. doi:10.1016/S0140-6736(15)60692-4
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Gluck, M. A., Mercado, E., & Myers, C. E. (2014). Learning and Memory: From Brain to Behavior. (2nd Edition). Worth Publishing
* 1 2 3 American Psychiatric Association (2000, 2010). Diagnostic and statistical manual of mental disorders (Revised 4th ed.. and 5th ed.) Washington DC.
* 1 2 3 Fischbach, A., Konen, T., & Hasselhorn, M. (2014). What is not working in working memory of children with literacy disorders? Evidence from three-yer-longitudinal study. Read Writ, 27 267-286.
* 1 2 Bordignon, S., Giulini, E., Trentini, C.M., & Bosa, C.A. (2015). Memory in children and adolescents with autism spectrum disorder: a systematic literature review. Psychology and Neuroscience, 8, 211-245.
* ↑ Vries, M.D., & Geurts H.M. (2014). Beyond individual differences: are working memory and inhibition informative specifiers with ASD? Journal of Neural Transmission, 121,1183-1198.
* 1 2 3 Cui, J., Gao, D., Chen., Zou, X., & Wang. (2010). Working memory in early-school-age children with Asperger's syndrome. Journal of Autism and Developmental Disorders, 40, 958-967.
* ↑ Baddeley, A. (2003). Working memory: looking and looking forward. Neuroscience, 4, 829-839
* ↑ Loucas, T., Riches, N.G., Charman, T., Pickles, A., Simonoff, E., Chandler, S., & Baird, G. (2010). Speech perception and phonological short-term memory capacity in language impairment: preliminary evidence from adolescents with specific language impairment (SLI) and autism spectrum disorders (ASD). International Journal of language and Communication Disorders, 45, 275-286.
* 1 2 3 Williams, D.M., Jarrold C., Grainger, C., & Lind, S.E. (2014). Diminished time-based, but undiminished event-based, prospective memory among intellectually high functioning adults with autism spectrum disorder: relation to working memory ability. Neuropsychology, 28, 30-42.
* 1 2 Sache, M., Schlitt, S., Hainz, D., Ciaramidaro, A., Schirman, S., Walter, H., Poustka, F., Bolte, S., & Freitg., C.M. (2013). Executive and visuo-motor function in adolescents and adults with Autism spectrum disorder. Journal of Autism Developmental Disorders, 43, 1222-1235.
* 1 2 3 4 5 6 American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Arlington: American Psychiatric Publishing. pp. 59–65. ISBN 0890425558
* ↑ Simon, Harvey. (2013, July 03).Attention deficit hyperactivity disorder. Retrieved from umm.edu/health/medical/reports/articles/attention-hyperactivity-disorder
* ↑ Attitude Editors. (2015). ADHD Is Biological, What causes attention deficit disorder? Although researchers know much about ADHD, they can’t pinpoint its cure. Retrieved from www.additudemag.com/adhd-wed/article/5008.html
* ↑ Dovis, S., Van der Oord, S., Wiers, R. W., & Prins, P. M. (2015). ADHD subtype differences in reinforcement sensitivity and visuospatial working memory. Journal Of Clinical Child And Adolescent Psychology, 44(5), 859-874. doi:10.1080/15374416.2014.895940
* 1 2 3 Nyman, A., Taskinen, T., Grönroos, M., Haataja, L., Lähdetie, J., & Korhonen, T. (2010). Elements of working memory as predictors of goal-setting skills in children with attention-deficit/ hyperactivity disorder. Journal Of Learning Disabilities, 43(6), 553-562. doi:10.1177/0022219410375001
* 1 2 3 Jonsdottir, S., Bouma, A., Sergeant, J. A., & Scherder, E. A. (2005). The impact of specific language impairment on working memory in children with ADHD combined subtype. Archives Of Clinical Neuropsychology, 20(4), 443-456. doi:10.1016/j.acn.2004.10.004
* ↑ Andersen, P. N., Egeland, J., & Øie, M. (2013). Learning and memory impairments in children and adolescents with attention-deficit/hyperactivity disorder. Journal Of Learning Disabilities, 46(5), 453-460.
* 1 2 Clinical practice guideline: Treatment of the school-aged child with attention-deficit/hyperactivity disorder. (2002). Journal of the American Academy of Child & Adolescent Psychiatry, 41(5), 537
* ↑ Melby-Lervåg, M., & Hulme, C. (2013). Is working memory training effective? A meta-analytic review. Developmental Psychology, 49(2), 270-291. doi:10.1037/a0028228
* ↑ Sonuga-Barke, E., Brandeis, D., Holtmann, M., & Cortese, S. (2014). Computer-based cognitive training for ADHD: A review of current evidence. Child And Adolescent Psychiatric Clinics Of North America, 23(4), 807-824. doi:10.1016/j.chc.2014.05.009

# 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.”
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.
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

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

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

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

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.
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

## References

* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Bruning, R., & Schraw, G. (2011). Cognitive psychology and instruction (5th ed.). Pearson Education
* ↑ Khajah, M. M., Lindsey, R. V., & Mozer, M. C. (2014). Maximizing students' retention via spaced review: Practical guidance from computational models of memory. Topics in Cognitive Science, 6(1), 157-169. doi:10.1111/tops.12077
* 1 2 3 4 Bonner, M. F., & Grossman, M. (2012). Gray matter density of auditory association cortex relates to knowledge of sound concepts in primary progressive aphasia. The Journal of Neuroscience, 32(23), 7986-7991.
* 1 2 Chi, M.T.H., de Leeuw, N., Chiu, M., & La Vancher, C. (1994). Eliciting self-explanations improves understanding. Cognitive Science, 18, 439-477.
* ↑ Bruner, J.S., Goodnow, J.J., & Austin, G.A. (1956). A study of thinking. New York, NY: Wiley
* ↑ Remue, J., De Houwer, J., Barnes-Holmes, D., Vanderhasselt, M., & De Raedt, R. (2013). Self-esteem revisited: Performance on the implicit relational assessment procedure as a measure of self- versus ideal self-related cognitions in dysphoria. Cognition and Emotion, 27(8), 1441-1449. doi:10.1080/02699931.2013.786681
* ↑ Anderson, J.R. (2005). Cognitive psychology and its implications (6th ed.). New York: Worth
* ↑ Jui-Pi Chien. (2014). Schemata as the primary modelling system of culture: Prospects for the study of nonverbal communication. Sign Systems Studies, 42(1), 31-41. doi:10.12697/SSS.2014.42.1.02
* ↑ Le Grande, M. R., Elliott, P. C., Worcester, M. U. c., Murphy, B. M., Goble, A. J., Kugathasan, V., et al. (2012). Identifying illness perception schemata and their association with depression and quality of life in cardiac patients. Psychology, Health & Medicine, 17(6), 709-722. doi:10.1080/13548506.2012.661865
* ↑ Sternberg, R. J., & Sternberg, K. (2012). Cognitive psychology (6th ed.). Belmont, CA: Wadsworth.
* 1 2 van Kesteren, Marlieke T. R., Rijpkema, M., Ruiter, D. J., Morris, R. G. M., & Fernàndez, G. (2014). Building on prior knowledge: Schema-dependent encoding processes relate to academic performance. Journal of Cognitive Neuroscience, 26(10), 2250-2261. doi:10.1162/jocn_a_00630
* ↑ Rumelhart, D.E. (1981). The building blocks of cognition. In J.T Guthrie (Ed.), Comprehension and teaching: Research reviews (pp. 3-26). Newark, DE: International Reading Association.
* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 Anderson, J. R. (1993). Rules of the mind. Hillsdale, NJ: Lawrence Erlbaum.
* ↑ Trillingsgaard, A. (1999). The script model in relation to autism. European Child & Adolescent Psychiatry, 8(1), 45. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=4689586&site=ehost-live
* ↑ Craik, F.I.M. (1979). Human memory. Annual Review of Psychology, 30, 63-102.
* ↑ Craik, F.I.M., & Lockhard, R.S. (1986). CHARM is not enough: Comments on Eich's model of cued recall. Psychological Review, 93, 360-364.
* 1 2 Paivio, A. (1986). Mental representations: A dual-coding approach. New York, NY: Oxford University Press.
* ↑ Butcher, K.R. (2006). Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98, 182-197.
* 1 2 Mayer, R.E. (2001). Multimedia learning. New York, NY: Cambridge University Press.
* ↑ van Merrienboer, J.J.G., & Sweller, J. (2005). Cognitive load and complex learning: Recent developments and future directions. Educational Psycholog Review, 17, 147-177.
* ↑ Mayer, R. E. (2008). Applying the science of learning: Evidence-based principles for the design of multimedia instruction. Cognition and Instruction, 19, 177–213.
* 1 2 3 Anderson, J.R. (2010). Cognitive Psychology and its implications. (7th ed.). New York, NY: Worth.
* ↑ Koriat, A., Goldsmith, M. & Pansky, A. (2000). Toward a psychology of memory accuracy, In S.Fiske (Ed.), Annual review of psychology, (pp. 481-537). Palo Alto, CA: Annual Reviews.
* 1 2 Woolfolk, A., Winnie, P. H., & Perry, N. E. (2016). Educational Psychology (Custom Edition). Toronto, ON: Pearson Education.
* ↑ Erdelyi, M.H. (2010). The ups and downs of memory. American Psychologist, 65, 623-633.
* ↑ Anderson, J.R., Fincham, J.M., Qin, T., & Stocco, A. (2008). A central circuit of the mind. Trends in Cognitive Psychology, 12, 136-143.
* ↑ Ismail, M. N., Ngah, N. A., & Umar, I. N. (2010). The effects of mind mapping with cooperative learning on programming performance, problem solving skill and metacognitive knowledge among computer science students. Journal Of Educational Computing Research, 42(1), 35-61. doi:10.2190/EC.42.1.b
* ↑ McClelland, J. L. (1988). Connectionist models and psychological evidence. Journal of Memory and Language, 27, 107-123.
* ↑ Vickers, Douglas, & Lee, Michael D. (1997). Towards a dynamic connectionist model of memory. Behavioral and Brain Sciences, 20, 40-41. doi:10.1017/S0140525X97460016
* 1 2 3 Anderson, J. R., & Matessa, M. (1997). A production system theory of serial memory. Psychological Review, 104(4), 728-748. Retrieved from http://www.ebscohost.com/
* ↑ Anderson, J. R. (1990). Cognitive psychology and its implications. New York, NY: Freeman.
* ↑ Schraw, G. (2006). In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (pp. 825-847). Mahwah, NJ: Erlbaum.
* 1 2 3 4 5 6 7 8 Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned. The Journal of the Learning Sciences, 4(2), 167-207. Retrieved from: http://www.jstor.org/
* 1 2 3 4 5 6 7 8 9 Anderson, J. R. (1996). ACT: A simple theory of complex cognition. American Psychologist, 51(4), 355-365. Retrieved from http://www.ebscohost.com/
* ↑ Ritter, S., Anderson, J. R., Koedinger, K. R., & Pelletier, R. (2007). Cognitive tutor: Applied research in mathematics education. Psychonomic Bulletin & Review, 14(2), 249-255. Retrieved from http:// link.springer.com/

# 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.
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 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.

#### 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
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

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:
* 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
* 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)
* Finding the Moment
*   * Often thought as the moment of change in a person's story
* Seeing Your Story
*   * Thinking of visuals such as images to use in your digital story, special effects
* Hearing Your Story
*   * Recorded voice-over of the storyteller, tone of voice, layers of sound, music or ambient sound
* Assembling Your Story
*   * Compose script and storyboard
* 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.

# References

* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Bruning, R., & Schraw, G. (2011). Cognitive psychology and instruction (5th ed.). Pearson Education.
* 1 2 3 4 5 6 7 8 Putnam, A. L. (2015). Mnemonics in education: Current research and applications. Translational Issues In Psychological Science, 1(2), 130-139. doi:10.1037/tps0000023
* ↑ Bakker, J., & Simpson, C. (2011). Mnemonic Strategies: Success for the Young-Adult Learner. Human Resource and Adult Learning, 7(2).
* 1 2 3 4 5 6 7 8 9 Bjork, E., Kornell, N., Storm, B., Yue, C (2015). Highlighting and its relation to distributed study and students' metacognitive beliefs. Educational Psychology Review, 27(1), 69-78. http://dx.doi.org/10.1007/s10648-014-9277-z
* 1 2 3 Den Elzen-Rump, V., Leopold, C., Leutner, D (2007). Self-regulated learning with a text-highlighting strategy: A training experiment. Journal of Psychology, 215(3), 174-182. http://dx.doi.org/10.1027/0044-3409.215.3.174
* 1 2 3 4 5 Galli, G. (2014). What makes deeply encoded items memorable? Insights into the levels of processing framework from neuroimaging and neuromodulation. Frontiers In Psychiatry, 5doi:10.3389/fpsyt.2014.00061
* ↑ Nyberg L. Imaging episodic memory: Implications for cognitive theories and phenomena. Memory [serial online]. September 1999;7(5-6):585-597. Available from: PsycINFO, Ipswich, MA. Accessed December 7, 2015.
* 1 2 Van Blankenstein, F. M., Dolmans, D. M., Van der Vleuten, C. M., & Schmidt, H. G. (2013). Relevant prior knowledge moderates the effect of elaboration during small group discussion on academic achievement. Instructional Science, 41(4), 729-744. doi:10.1007/s11251-012-9252-3
* 1 2 3 4 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
* 1 2 3 4 5 Tok, S. (2013). Effects of the Know-Want-Learn strategy on students’ mathematics achievement, anxiety and metacognitive skills. Metacognition and Learning, 8(2), 193-212. http://dx.doi.org/10.1007/s11409-013-9101-z
* 1 2 3 Blunt, J (2014). Learning with retrieval-based concept mapping. Journal of Educational Psychology, 106(3), 849-858. http://dx.doi.org/10.1037/a0035934
* ↑ Hung, S., Ku, D., Shih, J (2014). The integration of concept mapping in a dynamic assessment model for teaching and learning accounting. Journal of Educational Technology & Society, 17(1), 141-153.
* 1 2 Liu, P (2014). Using eye tracking to understand learners' reading process through the concept-mapping learning strategy. Computers & Education, 78, 237-249. http://dx.doi.org/10.1016/j.compedu.2014.05.011
* 1 2 Bartlett, F.C. (1932). Remembering: A Study in Experimental and Social Psychology. Cambridge University Press.
* ↑ Nalbantian, edited by Suzanne; Matthews, Paul M., McClelland, James L. (2010). The memory process : neuroscientific and humanistic perspectives. Cambridge, Mass.: MIT Press. ISBN 978-0-262-01457-1.
* ↑ Waulhauser, G. (2011, July 11). Selective memory does exist. The Telegraph.
* 1 2 3 4 5 6 7 Robin, J., Wynn, J., & Moscovitch, M. (2016). The spatial scaffold: The effects of spatial context on memory for events. Journal Of Experimental Psychology: Learning, Memory, And Cognition, 42(2), 308-315.
* 1 2 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
* 1 2 Lanciano, T., Curci, A., Mastandrea, S., & Sartori, G. (2013). Do automatic mental associations detect a flashbulb memory?. Memory, 21(4), 482-493.
* 1 2 de Jonge, M., Tabbers, H. K., & Rikers, R. P. (2014). Retention beyond the threshold: Test-enhanced relearning of forgotten information. Journal Of Cognitive Psychology, 26(1), 58-64. doi:10.1080/20445911.2013.858721
* 1 2 Rohrer, D., & Taylor, K. (2006). The effects of overlearning and distributed practise on the retention of mathematics knowledge. Applied Cognitive Psychology, 20(9), 1209-1224. doi:10.1002/acp.1266
* ↑ Mumford, M. D., & Constanza, D. P. (1994). Influence of abilities on performance during practice: Effects of massed and distributed practice. Journal Of Educational Psychology, 86(1), 134.
* 1 2 Liu, K. Y., Chan, C. H., Lee, T. C., & Hui-Chan, C. Y. (2004). Mental imagery for relearning of people after brain injury. Brain Injury, 18(11), 1163-1172. doi:10.1080/02699050410001671883
* ↑ Rawson, K., Dunlosky, J., & Sciartelli, S. (2013). The Power of Successive Relearning: Improving Performance on Course Exams and Long-Term Retention. Educational Psychology Review, 25(4), 523-548. doi:10.1007/s10648-013-9240-4
* ↑ Rawson, K., & Dunlosky, J. (2012). When Is Practice Testing Most Effective for Improving the Durability and Efficiency of Student Learning?. Educational Psychology Review, 24(3), 419-435. doi:10.1007/s10648-012-9203-1
* ↑ Tran, R., Rohrer, D., & Pashler, H. (2014). Retrieval Practice: The Lack of Transfer to Deductive Inferences. Psychonomic Society. Retrieved December 1, 2015.
* ↑ Roy, M., & Chi, M. (2012). The Self-Explanation Principle in Multimedia Learning. The Cambridge Handbook of Multimedia Learning, 271-286. http://dx.doi.org/10.1017/CBO9780511816819.018
* ↑ 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
* ↑ Kabat-Zinn, J. (2015). On Lineage and the Uses and Limitations of Scaffolding. Mindfulness, 6(5), 1222-1225. doi:On lineage and the uses and limitations of scaffolding.
* ↑ Ukrainetz, T. (2015). Improving Text Comprehension: Scaffolding Adolescents into Strategic Reading. Semin Speech Lang Seminars in Speech and Language, 36(1), 17-30. http://dx.doi.org/10.1055/s-0034-1396443
* ↑ Mynard, J., & Almarzouqi, I. (2006). Investigating peer tutoring. ELT Journal, 60(1), 13-22. doi:10.1093/elt/cci077
* 1 2 Korner, M., & Hopf, M. (2014). Cross-Age Peer Tutoring in Physics: Tutors, Tutees, and Achievement in Electricity. Int J of Sci and Math Educ International Journal of Science and Mathematics Education, 13(5), 1039-1063. doi:10.1007/s10763-014-9539-8
* ↑ Ayvazo, S., & Aljadeff-Abergel, E. (2014). Classwide peer tutoring for elementary and high school students at risk: Listening to students' voices. Support for Learning, 29(1), 76-92. doi:10.1111/1467-9604.12047
* ↑ Brandt, C. & Dimmitt, N. (2015). Transfer of learning in the development of peer tutor competence. Learning and Teaching in Higher Education: Gulf Perspectives, 12(2). http://lthe.zu.ac.ae
* ↑ Dyer, J., Riley, J., & Yekovich, F. (1979). An Analysis of Three Study Skills: Notetaking, Summarizing, and Rereading 1. The Journal of Educational Research, 73, 3-7.
* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Andrews, D. H., Hull, T. D., & DeMeester, K. (2010). Storytelling as an instructional method: Research perspectives. Rotterdam; Boston: Sense Publishers.
* 1 2 3 4 5 6 7 8 Glaser, M., Garsoffky, B., & Schwan, S. (2009). Narrative-based learning: Possible benefits and problems. Communications, 34(4), 429-447.
* 1 2 3 4 5 6 7 8 9 10 11 Green, M. C. (2004). Storytelling in teaching. APS Observer, 17(4), 37-39.
* ↑ Storytelling. (n.d.). Dictionary.com Unabridged. Retrieved July 10, 2016 from Dictionary.com website http://www.dictionary.com/browse/storytelling
* ↑ Narrative. (n.d.). Retrieved July 12, 2016, from http://www.merriam-webster.com/dictionary/narrative
* 1 2 3 Halpern, D.E., Graesser, A., & Hakel, M. (2007). 25 Principles to Guide Pedagogy and the Design of Learning Environments. Washington, DC: Association of Psychological Science Taskforce on Lifelong Learning at Work and at Home
* 1 2 3 4 Nabben, J. (2014). Influence: What it really means and how to make it work for you. Harlow,England: Pearson. Quoted in The Science Behind Storytelling [online]. https://www.melcrum.com/research/strategy-planning-tactics/science-behind-storytelling
* ↑ Stephens, G. J., Silbert, L. J., & Hasson, U. (2010). Speaker–listener neural coupling underlies successful communication. Proceedings of the National Academy of Sciences, 107(32), 14425-14430
* 1 2 Cohen, D., & Prusak, L. (2001). In good company: How social capital makes organizations work. Harvard Business Press.
* 1 2 3 4 5 Katuscáková, M. (2015, September). Sharing Scientific Knowledge Through Telling Stories and Digital Storytelling. In European Conference on Knowledge Management (p. 408). Academic Conferences International Limited
* ↑ Wyer, R. S. (2014). Knowledge and memory: the real story: advances in social cognition (Vol.8). Psychology Press.
* ↑ Schilf, M. (n.d.). Ten Great Film Exposition Scenes. Retrieved July 12, 2016, from http://thescriptlab.com/features/the-lists/859-ten-great-film-exposition-scenes#
* ↑ Dahlstrom, M. F. (2014). Using narratives and storytelling to communicate science with nonexpert audiences. Proceedings of the National Academy of Sciences, 111(Supplement 4), 13614-13620.
* 1 2 3 4 Bower, G.H., & Clark, M.C. (1969). Narrative stories as mediators for serial learning. Psychonomic Science, 14, 181-182.
* ↑ Prensky, M. (2001). Digital game¬-based learning. New York: McGraw Hill
* 1 2 3 4 5 6 Rule, L. (2010). DIGITAL STORYTELLING: Never Has Storytelling Been So Easy or So Powerful. Knowledge Quest, 38(4), 56-57.
* 1 2 Lambert, J. (2010). Digital Storytelling Cookbook: January 2010. Digital Diner Press
* ↑ Mayer, R. E. (2008). Applying the science of learning: evidence-based principles for the design of multimedia instruction. American Psychologist,63(8), 760.
* 1 2 Robin, B. R. (2008). Digital storytelling: A powerful technology tool for the 21st century classroom. Theory into practice, 47(3), 220-228.
* 1 2 3 Petrucco, C., De Rossi, M., & Personeni, F. (2013). Digital Storytelling as a new meaningful teaching/learning strategy for mathematics and geometry.Learning & Teaching with Media & Technology, 503.
* ↑ Stenhouse, R., Tait, J., Hardy, P., & Sumner, T. (2013). Dangling conversations: reflections on the process of creating digital stories during a workshop with people with early‐stage dementia. Journal of psychiatric and mental health nursing, 20(2), 134-141.

# Sociocognitive Learning
## Social Cognitive Theory

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

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

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 efficacyAn 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

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 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.

### 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].
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

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 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.

## 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.

#### 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.

#### 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.

### References

* ↑ Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.
* ↑ Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
* ↑ Marsh, H. W. (1986). Global self-esteem: Its relation to specific facets of self-concept and their importance. Journal of personality and social psychology, 51(6), 1224.
* ↑ Bandura, A. (1978). Reflections on self-efficacy. Advances in behaviour research and therapy, 1(4), 237-269.
* ↑ 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
* ↑ Seijts, G. H., Latham, G. P., & Whyte, G. (2000). 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
* ↑ Bandura, A. (1989). Human agency in social cognitive theory. American psychologist, 44(9), 1175.
* ↑ Bandura, A. (2007). Much ado over a faulty conception of perceived self-efficacy grounded in faulty experimentation. Journal of Social and Clinical Psychology, 26(6), 641-658.
* ↑ Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.
* ↑ Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.
* ↑ Vancouver, J. B., Thompson, C. M., Tischner, E. C., & Putka, D. J. (2002). Two studies examining the negative effect of self-efficacy on performance. Journal of Applied Psychology, 87(3), 506.
* ↑ Ng, J. R., & Earl, J. K. (2008). Accuracy in self-assessment: the role of ability, feedback, self-efficacy and goal orientation. Australian Journal of Career Development, 17(3), 39-50.
* ↑ Athanasou, J. A. (2005). Self-Evaluations in Adult Education and Training.Australian Journal of Adult Learning, 45(3), 290-303.
* ↑ Ng, J. R., & Earl, J. K. (2008). Accuracy in self-assessment: the role of ability, feedback, self-efficacy and goal orientation. Australian Journal of Career Development, 17(3), 39-50.
* ↑ Williams, T., & Williams, K. (2010). Self-efficacy and performance in mathematics: Reciprocal determinism in 33 nations. Journal of educational Psychology, 102(2), 453.
* ↑ Locke, E. A., & Latham, G. P. (2002). Building a practically useful theory of goal setting and task motivation: A 35-year odyssey. American psychologist, 57(9), 705.
* ↑ Elliot, A. J., & Church, M. A. (1997). A hierarchical model of approach and avoidance achievement motivation. Journal of personality and social psychology, 72(1), 218.
* ↑ Button, S. B., Mathieu, J. E., & Zajac, D. M. (1996). Goal orientation in organizational research: A conceptual and empirical foundation. Organizational behavior and human decision processes, 67(1), 26-48.
* ↑ Vieira Jr, E. T., & Grantham, S. (2011). University students setting goals in the context of autonomy, self‐efficacy and important goal‐related task engagement. Educational Psychology, 31(2), 141-156.
* ↑ Ng, J. R., & Earl, J. K. (2008). Accuracy in self-assessment: the role of ability, feedback, self-efficacy and goal orientation. Australian Journal of Career Development, 17(3), 39-50.
* ↑ Bandura, A. (1982). Self-efficacy mechanism in human agency. American psychologist, 37(2), 122.
* ↑ Sideridis, G. D. (2001). The causal role of goal importance for the explanation of student study behaviour: Cross-validation with multiple samples. Educational Psychology, 21(3), 277-298.
* ↑ Lent, R. (1984). Relation of Self-Efficacy Expectations to Academic Achievement and Persistence. University of Minnesota.
* ↑ Lent, R. (1984). Relation of Self-Efficacy Expectations to Academic Achievement and Persistence. University of Minnesota.
* ↑ Bandera, A. (1977). Self-efficacy: Toward a Unifying Theory of Behavioral Change, 84(2), 191-215
* ↑ Lent, R. (1984). Relation of Self-Efficacy Expectations to Academic Achievement and Persistence. University of Minnesota.
* ↑ Zimmerman, B., & Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex. and giftedness to self-efficacy and strategy use. Estados Unidos: American Psychological Association.
* ↑ Zimmerman, B., & Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex. and giftedness to self-efficacy and strategy use. Estados Unidos: American Psychological Association.
* ↑ Zimmerman, B., & Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex. and giftedness to self-efficacy and strategy use. Estados Unidos: American Psychological Association.
* ↑ Onoda, S. (2014). Examining the relationships between self-efficacy, effort regulation strategy use, and English vocabulary skills. Studies in Self-Access Learning Journal, 5(4), 357-371.
* ↑ Zimmerman, B., & Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex. and giftedness to self-efficacy and strategy use. Estados Unidos: American Psychological Association
* ↑ Zimmerman, B., & Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex. and giftedness to self-efficacy and strategy use. Estados Unidos: American Psychological Association
* ↑ Onoda, S. (2014). Examining the relationships between self-efficacy, effort regulation strategy use, and English vocabulary skills. Studies in Self-Access Learning Journal, 5(4), 357-371
* ↑ 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
* ↑ 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
* ↑ 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
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Braaksma, M. H., Rijlaarsdam, G., & van den Bergh, H. (2002). Observational learning and the effects of model-observer similarity. Journal Of Educational Psychology, 94(2), 405-415. doi:10.1037/0022-0663.94.2.405
* ↑ Braaksma, M. H., Rijlaarsdam, G., & van den Bergh, H. (2002). Observational learning and the effects of model-observer similarity. Journal Of Educational Psychology, 94(2), 405-415. doi:10.1037/0022-0663.94.2.405
* ↑ 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
* ↑ Braaksma, M. H., Rijlaarsdam, G., & van den Bergh, H. (2002). Observational learning and the effects of model-observer similarity. Journal Of Educational Psychology, 94(2), 405-415. doi:10.1037/0022-0663.94.2.405
* ↑ Hoover, J. D., Giambatista, R. C., & Belkin, L. Y. (2012). Eyes on, hands on: Vicarious observational learning as an enhancement of direct experience. Academy Of Management Learning & Education, 11(4), 591-608. doi:10.5465/amle.2010.0102
* ↑ Braaksma, M. H., Rijlaarsdam, G., & van den Bergh, H. (2002). Observational learning and the effects of model-observer similarity. Journal Of Educational Psychology, 94(2), 405-415. doi:10.1037/0022-0663.94.2.405
* ↑ Hoover, J. D., Giambatista, R. C., & Belkin, L. Y. (2012). Eyes on, hands on: Vicarious observational learning as an enhancement of direct experience. Academy Of Management Learning & Education, 11(4), 591-608. doi:10.5465/amle.2010.0102
* ↑ Braaksma, M. H., Rijlaarsdam, G., & van den Bergh, H. (2002). Observational learning and the effects of model-observer similarity. Journal Of Educational Psychology, 94(2), 405-415. doi:10.1037/0022-0663.94.2.405
* ↑ 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
* ↑ 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
* ↑ Craig, S. D., Chi, M. H., & VanLehn, K. (2009). In a classroom, Learning can be enhanced by Collaboratively Observing Human Tutoring Videos While Problem Solving. Journal Of Educational Psychology, 101(4), 779-789. doi:10.1037/a0016601
* ↑ Craig, S. D., Chi, M. H., & VanLehn, K. (2009) Journal Of Educational Psychology, 101(4), 779-789. doi:10.1037/a0016601
* ↑ Craig, S. D., Chi, M. H., & VanLehn, K. (2009). Improving Classroom Learning by Collaboratively Observing Human Tutoring Videos While Problem Solving. Journal Of Educational Psychology, 101(4), 779-789. doi:10.1037/a0016601
* ↑ Craig, S. D., Chi, M. H., & VanLehn, K. (2009). Improving Classroom Learning by Collaboratively Observing Human Tutoring Videos While Problem Solving. Journal Of Educational Psychology, 101(4), 779-789. doi:10.1037/a0016601
* ↑ Craig, S. D., Chi, M. H., & VanLehn, K. (2009). Improving Classroom Learning by Collaboratively Observing Human Tutoring Videos While Problem Solving. Journal Of Educational Psychology, 101(4), 779-789. doi:10.1037/a0016601
* ↑ Braaksma, M. H., Rijlaarsdam, G., & van den Bergh, H. (2002). Observational learning and the effects of model-observer similarity. Journal Of Educational Psychology, 94(2), 405-415. doi:10.1037/0022-0663.94.2.405
* ↑ Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal Of Educational Psychology, 105(3), 774-786. doi:10.1037/a0032198
* ↑ Knoblauch, D., & Chase, M. A. (2015). Rural, suburban, and urban schools: The impact of school setting on the efficacy beliefs and attributions of student teachers. Teaching And Teacher Education 45104-114. doi:10.1016/j.tate.2014.10.001
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal Of Educational Psychology, 105(3), 774-786. doi:10.1037/a0032198
* ↑ Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal Of Educational Psychology, 105(3), 774-786. doi:10.1037/a0032198
* ↑ Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal Of Educational Psychology, 105(3), 774-786. doi:10.1037/a0032198
* ↑ 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
* ↑ 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
* ↑ 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
* ↑ Holzberger, D., Philipp, A., & Kunter, M. (2013). How teachers’ self-efficacy is related to instructional quality: A longitudinal analysis. Journal Of Educational Psychology, 105(3), 774-786. doi:10.1037/a0032198
* ↑ Mulvey, P. W., & Klein, H. J. (1998)
* ↑ Mulvey, P. W., & Klein, H. J. (1998)
* ↑ Seijts, G. H., Latham, G. P., & Whyte, G. (2000). 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
* ↑ Mulvey, P. W., & Klein, H. J. (1998). The impact of perceived loafing and collective efficacy in group goal processes and group performance. Organizational Behavior And Human Decision Processes, 74(1), 62-87. doi:10.1006/obhd.1998.2753
* ↑ Stajkovic, A. D., Lee, D., & Nyberg, A. J. (2009). Collective efficacy, group potency, and group performance: Meta-analyses of their relationships, and test of a mediation model. Journal Of Applied Psychology, 94(3), 814-828. doi:10.1037/a0015659
* ↑ Klein H. J., Mulvey P. W., (1995) Two Investigations of the Relationships among Group Goals, Goal Commitment, Cohesion, and Performance. Organizational Behaviour and Human Decision Processes, 61(1), 44-53. doi:10.1006/obhd.1995.1004
* ↑ Klein H. J., Mulvey P. W., (1995) Two Investigations of the Relationships among Group Goals, Goal Commitment, Cohesion, and Performance. Organizational Behaviour and Human Decision Processes, 61(1), 44-53. doi:10.1006/obhd.1995.1004
* ↑ Klein H. J., Mulvey P. W., (1995) Two Investigations of the Relationships among Group Goals, Goal Commitment, Cohesion, and Performance. Organizational Behaviour and Human Decision Processes, 61(1), 44-53. doi:10.1006/obhd.1995.1004
* ↑ Goncalo, J. A., Polman, E., & Maslach, C. (2010). Can confidence come too soon? Collective efficacy, conflict and group performance over time. Organizational Behavior And Human Decision Processes, 113(1), 13-24. doi:10.1016/j.obhdp.2010.05.001
* ↑ Seijts, G. H., Latham, G. P., & Whyte, G. (2000). 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
* ↑ Klein H. J., Mulvey P. W., (1995) Two Investigations of the Relationships among Group Goals, Goal Commitment, Cohesion, and Performance. Organizational Behaviour and Human Decision Processes, 61(1), 44-53. doi:10.1006/obhd.1995.1004
* ↑ Huang, C., (2009). Knowledge sharing and group cohesiveness on performance: An empirical study of technology R&D teams in Taiwan. Technovation, 29(11), 786-797. doi:10.1016/j.technovation.2009.04.003
* ↑ S., Howe, C., & Boyle, J. (2012). Exploring the characteristics of small groups within science and English secondary classrooms. Cambridge Journal Of Education, 42(4), 527-546. doi:10.1080/0305764X.2012.73334
* ↑ Klein H. J., Mulvey P. W., (1995) Two Investigations of the Relationships among Group Goals, Goal Commitment, Cohesion, and Performance. Organizational Behaviour and Human Decision Processes, 61(1), 44-53. doi:10.1006/obhd.1995.1004
* ↑ Goncalo, J. A., Polman, E., & Maslach, C. (2010). Can confidence come too soon? Collective efficacy, conflict and group performance over time. Organizational Behavior And Human Decision Processes, 113(1), 13-24. doi:10.1016/j.obhdp.2010.05.001
* ↑ Klein H. J., Mulvey P. W., (1995) Two Investigations of the Relationships among Group Goals, Goal Commitment, Cohesion, and Performance. Organizational Behaviour and Human Decision Processes, 61(1), 44-53. doi:10.1006/obhd.1995.1004
* ↑ Goncalo, J. A., Polman, E., & Maslach, C. (2010). Can confidence come too soon? Collective efficacy, conflict and group performance over time. Organizational Behavior And Human Decision Processes, 113(1), 13-24. doi:10.1016/j.obhdp.2010.05.001
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Seijts, G. H., Latham, G. P., & Whyte, G. (2000). 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
* ↑ S., Howe, C., & Boyle, J. (2012). Exploring the characteristics of small groups within science and English secondary classrooms. Cambridge Journal Of Education, 42(4), 527-546. doi:10.1080/0305764X.2012.73334
* ↑ Seijts, G. H., Latham, G. P., & Whyte, G. (2000). 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
* ↑ Mulvey, P. W., & Klein, H. J. (1998). The impact of perceived loafing and collective efficacy in group goal processes and group performance. Organizational Behavior And Human Decision Processes, 74(1), 62-87. doi:10.1006/obhd.1998.2753
* ↑ Mulvey, P. W., & Klein, H. J. (1998). The impact of perceived loafing and collective efficacy in group goal processes and group performance. Organizational Behavior And Human Decision Processes, 74(1), 62-87. doi:10.1006/obhd.1998.2753
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Ning, H., & Downing, K. (2012). Influence of student learning experience on academic performance: The mediator and moderator effects of self-regulation and motivation (2nd ed., Vol. 38, pp. 219-237). British Educational Research Journal.
* ↑ Craig, S. D., Chi, M. H., & VanLehn, K. (2009). Improving Classroom Learning by Collaboratively Observing Human Tutoring Videos While Problem Solving. Journal Of Educational Psychology, 101(4), 779-789. doi:10.1037/a0016601
* ↑ Denham, Susanne A. (2014). How Preschoolers' Social-Emotional Learning Predicts their Early School Success: Developing Theory-Promoting, Competency-Based Assessments. 426-454.
* ↑ SEL Defined. (n.d.). Retrieved November 1, 2015
* ↑ Jackson, Cassandra McKay. (2014). A Critical Approach to Social Emotional Learning Instruction Through Community-Based Service Learning. 292-312.
* ↑ SEL Defined. (n.d.). Retrieved November 1, 2015
* ↑ SEL Defined. (n.d.). Retrieved November 1, 2015
* ↑ Alan Davis, V. Scott Solberg, Christine de Baca & Taryn Hargrove Gore (2014) Use of Social Emotional Learning Skills to Predict Future Academic Success and Progress Toward Graduation, Journal of Education for Students Placed at Risk (JESPAR), 19:3-4, 169-182, DOI: 10.1080/10824669.2014.972506
* ↑ Alan Davis, V. Scott Solberg, Christine de Baca & Taryn Hargrove Gore (2014) Use of Social Emotional Learning Skills to Predict Future Academic Success and Progress Toward Graduation, Journal of Education for Students Placed at Risk (JESPAR), 19:3-4, 169-182, DOI: 10.1080/10824669.2014.972506
* ↑ Alan Davis, V. Scott Solberg, Christine de Baca & Taryn Hargrove Gore (2014) Use of Social Emotional Learning Skills to Predict Future Academic Success and Progress Toward Graduation, Journal of Education for Students Placed at Risk (JESPAR), 19:3-4, 169-182, DOI: 10.1080/10824669.2014.972506
* ↑ Alan Davis, V. Scott Solberg, Christine de Baca & Taryn Hargrove Gore (2014) Use of Social Emotional Learning Skills to Predict Future Academic Success and Progress Toward Graduation, Journal of Education for Students Placed at Risk (JESPAR), 19:3-4, 169-182, DOI: 10.1080/10824669.2014.972506
* ↑ Alan Davis, V. Scott Solberg, Christine de Baca & Taryn Hargrove Gore (2014) Use of Social Emotional Learning Skills to Predict Future Academic Success and Progress Toward Graduation, Journal of Education for Students Placed at Risk (JESPAR), 19:3-4, 169-182, DOI: 10.1080/10824669.2014.972506
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jackson, Cassandra McKay. (2014). A Critical Approach to Social Emotional Learning Instruction Through Community-Based Service Learning. 292-312.
* ↑ Jackson, Cassandra McKay. (2014). A Critical Approach to Social Emotional Learning Instruction Through Community-Based Service Learning. 292-312.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ Jones, S.M., Bailey, R., Jacob, R. (2014). Social Emotional Learning is Essential to Classroom Management. 19-24.
* ↑ John Meiklejohn (2012) Integrating Mindfulness Training into K-12 Education: Fostering the Resilience of Teachers and Students. 291–307.
* ↑ Robert C. Pianta (2012) Recent trends in research on teacher–child relationships Institute for Policy Research, Northwestern University, Evanston, IL, USA; C213–231
* ↑ Ann Sanson, S. A. (2004) Connections between Temperament and Social Development: A Review. 143-170.
* ↑ Mary E. Gifford-Smitha, Celia A. Brownell (2003) Childhood peer relationships: social acceptance, friendships, and peer networks Journal of School Psychology 41, 235–284
* ↑ Weinstein, C.S., Romano, M. (2015) Knowing Your Students and Their Special Needs 110-145
* ↑ Mary E. Gifford-Smith, C. A. (2002). Childhood Peer Relationships: Social Acceptance, Friendships, and Peer networks. 41, 235–284.
* ↑ WENDY M. CRAIG & DEBRA J. PEPLER (2007) Understanding Bullying: From Research to Practice; Canadian Psychology Vol. 48, No. 2, 86-93
* ↑ WENDY M. CRAIG & DEBRA J. PEPLER (2007) Understanding Bullying: From Research to Practice; Canadian Psychology Vol. 48, No. 2, 86-93
* ↑ Kenneth W. Merrell, R.B. (2006). Relational Aggression in Children and Adolescents: A review with implications for school settings. Psychology in the Schools , 43, 345-361.
* ↑ Kenneth W. Merrell, R. B. (2006). Relational Aggression in Children and Adolescents. A review with implications for school settings. Psychology in the Schools, 43, 345-361
* ↑ Xinyin Chen and Doran C. French (2008) Children’s Social Competence in Cultural Context 591-616
* ↑ Ann Sanson, S. A. (2004). Connections between Temperament and Social Development: A Review. 143-170.
* ↑ Joseph E. Zins, M. J. (2006). Social and Emotional Learning: Promoting the Development of All Students. Journal of Educational and Psychological Consultation, 17, 233–255.
* 1 2 Dilani S. P. Gedera and P. John Williams Eds. (2016) Activity Theory in Education: Research and Practice. Rotterdam, The Netherlands: Sense Publishers.
* ↑ Bedney & Meister 1997 Bedny, Gregory; Meister, David (1997). The Russian Theory of Activity: Current Applications to Design and Learning. Series in Applied Psychology. Psychology Press. ISBN 978-0-8058-1771-3.
* 1 2 3 4 Roth, W., & Yew-Jin Lee. (2007) "Vygotsky's Neglected Legacy": Cultural-Historical Activity Theory. Review of Educational Research, 77(2), 186-232. Retrieved from http://www.jstor.org.proxy.lib.sfu.ca/stable/4624893
* 1 2 3 4 5 6 Engeström, Y. (2008) Expansive Learning: Toward an activity-Theoretical Reconceptualization. In Illeris, Knud (Ed.) (2008) Contemporary Theories of Learning: Learning Theorists … In Their Own Words. New York, NY: Routledge.
* ↑ Warwick Institute for Employment Research (2011) Activity Theory. Retrieved on July 12, 2016, from https://www2.warwick.ac.uk/fac/soc/ier/glacier/learning/theories/activitytheory/cg/
* 1 2 Nardi, B. (1996). Context and consciousness: Activity theory and human-computer interaction. Cambridge, MA: MIT Press.
* 1 2 3 Belland, B.R. (2011). Distributed Cognition as a Lens to Understand the Effects of Scaffolds: The Role of Transfer of Responsibility. Educational Psychology Review, 23(4), 577-600.
* 1 2 Clark, A. and D. Chalmers. The extended mind. Analysis 58(1): 7–19, 1998.
* 1 2 Kellie Williamson & Rochelle Cox (2014) Distributed Cognition in Sports Teams: Explaining successful and expert performance, Educational Philosophy and Theory, 46:6, 640-654.
* 1 2 Hutchins, E., Klausen, T. (2000) Distributed Cognition in an Airline Cockpit. In Y. Engeström and D. Middleton (Eds.) Cognition and communication at work. New York: Cambridge University Press. pp. 15-34.
* ↑ I.Dror & S. Harnad. (2008) Offloading Cognition onto Cognitive Technology. In I.Dror & S. Harnad (Eds.) Cognition Distributed: How Cognitive Technology Extends Our Minds (pp 1–23). Amsterdam: John Benjamins Publishing.

# 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.

# 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-understandingand 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?

### 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
## 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

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?

## Winne’s Phase model of SRL
# 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

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.
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.
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.
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.
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
## 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.
* 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 hypermediaMetaTutor 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.
* 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.
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.

### 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: Procedure for developing students' performance that involves giving a rationale for the performance, demonstrating the performance, and providing opportunity of practice.
Cognitive Processing: A term used to describe thinking and applying knowledge.
Collaborative learning: sharing and learning knowledge through peers/groups.
Critical Thinking: A type a reflective thinking consisting of weighing, evaluating and understanding information.
Forethought Phase: Strategies taking place before learning. Self-assessment, goal setting and strategic planning.
Metacognition: Thinking about thinking; awareness and understanding of one's thought processes.
Metacognitive Knowledge: Declarative knowledge such as language and memory.
Metacognitive experiences: What the person is aware of and what she or he feels when coming across a task and processing information related to it.
Metacognitive skills: Deliberate use of strategies (i.e. Procedural knowledge) in order to control cognition.
Motivation:Behaviours and thoughts that drive individuals to perform.
Performance Phase: Strategies taking place during learning. Strategy implementation, and strategy monitoring.
Purpose of Engagement:The self-process, the purpose, and the possible actions that are relevant in a specific situation.
Relativist: Knowledge is flexible and changeable. It can be questioned.
Self-Efficacy: How the individual perceives own abilities and the level of confidence for achieving goals from the perceived abilities
Self-Evaluation: Evaluating self according to a standard
Self-Regulated Action: The means by which regulation is conducted.
Self-Regulated Learning:Ability to control and explicitly understand all aspects of one's learning.
Self-Regulated Phase: Strategies after learning has taken place. Evaluation.

# Extra Resources

Readings
Schunk, D.H. & Zimmerman,B.J.(2008).Motivation and self-regulated learning: theory, research, and applications.Taylor&Francis Group, LLC.
Zimmerman, B. J. &Schunk, D H. (2010).Self-regulated learning and academic achievement: theoretical perspectives 2nd ed.Lawrence Erlbaum Associates.
Videos
Metacognition, Effective Teaching & Learning.Retrieved from https://www.youtube.com/watch?v=yo-c-Q3KHlA
Good Thinking! — That’s so Meta(cognitive)! Retrieved from https://www.youtube.com/watch?v=f-4N7OxSMok

# Motivation, Attribution and Beliefs About Learning

Our motivations drive and direct our thought processes and actions. People in developed countries spend about 15,000 hours in school by the time they are 20. It is important to understand the effects this extended school experience has on students' lives and well-being. Research has repeatedly found that as adolescents get older, there is a decrease in their motivation to learn. Researchers are now focusing on ways to sustain students' motivation throughout their school experience. This chapter explains how theories and research on motivation and beliefs about one's self can be applied to teaching and learning. It emphasizes the importance of motivation in learning, and how teachers can motivate students by accommodating and adapting to their needs. Motivation has two aspects that are inter-related. One aspect looks at how much motivation a person has, and the second looks onto what type of motivation it is. There are many theories of motivation, and here we examine three that offer understanding of teaching and learning. The first theory we look at is Self-Determination theory, which looks at two types of motivation and the factors that facilitate them by fulfilling psychological needs. The second theory we examine is Goal-Orientation theory, which looks at the power of goals in relation to the environments they are constructed within. The structure of the environment generally aligns with the type of motivational goal students strive to achieve. The third theory we examine is Expectancy-Value theory, which explains motivation in terms of the expectations individuals have for their performance in particular activities, and what value performance in those activities holds for them.

## Self-Determination Theory

Self- Determination Theory, first introduced by Edward L. Deci and Richard M. Ryan, primarily looks at two different types of motivation. It states that each type of motivation is built upon a reason or goal that eventually develops into a certain behaviour. The first type of motivation is intrinsic motivation, which is motivation that comes within one’s self for enjoyment and self interest without external pressures or reasons. A student who decides to read a textbook for full pleasure and takes interest in the topic, does so because of intrinsic motivation.On the other hand, extrinsic motivation comes from doing something because it leads to an external outcome.This would be a student who solely reads a textbook because he or she knows there is going to be a test at the end and wants to do well on the test.
Both of these types of motivation topics are extremely important to researchers and educators within the theory, because over the years there have been numerous conclusions, under the view of the Self - Determination Theory, intrinsic motivation facilitated the highest quality of learning because it included using creativity and the existence of psychological needs. With recent research however, there are a few approaches that state that although the highest quality of learning does still involve the core aspects of intrinsic motivation, there are ways to include extrinsic motivators to achieve the same purpose. This will be talked about more in later sections, after defining Intrinsic and Extrinsic motivation more in depth.

#### Intrinsic Motivation

As mentioned above, intrinsic motivation has been concluded to facilitate the highest quality of learning, as it stimulates creativity and satisfies important yet basic psychological needs. According to the Self-Determination Theory there are three personal physiological needs every human tries to fulfill. The first need is Autonomy. Autonomy is defined as being self regulation and self initiating of ones own behaviour and actions. A student who is autonomous would know exactly what is needed to achieve a given task and feels that they have the individual freedom to do so with effort.The second physological need is Competence which is defined as being the ability to attain different outcomes both externally and internally and is successful in doing so using the environment they are surrounded by. For a student this would mean the ability to do well on a difficult exam with the skills that they already have built from previous experiences. Finally, the third physiological need is Relatedness. Relatedness, would be the level of connection one feels from their social environment, where in the case of a student, would be when a student feels that they can relate and connect to what they’re learning as well as with the subjects around them.

#### Extrinsic Motivation

The Self- Determination Theory explains that there are 4 specific types of extrinsic motivation that are different to the degree which they hold autonomy.Starting from the left side of the spectrum, motivation is completely external, and as it moves in the right side direction it moves towards becoming internal. The least autonomous extrinsic motivation on the left spectrum is External regulation. This form of motivation is where behaviours are done to receive an incentive or to avoid a sort of punishment. This would be a student who decides to study for an exam strictly to get a good grade, avoid a punishment by their family members , or not be mocked by external subjects for being incapable. Now moving one to the right side, the next type is Introjected regulation, where motivation would occur solely to fulfill self/internal power and avoid guilt. A student here would change their motive of studying for an exam to elevate their ego and protect their image. Identified regulation comes next, which moves to a more autonomous motivation as its main reason towards acting on something is because it is seen as being valuable and useful for the future. For example studying hard for an exam because you want to do well for your future career would be identified regulation. The final type of extrinsic motivation which is the most autonomous is Integrated regulation, which is closely tied with topics that are being learned combined with one’s self interests. For example, a student might want to study chemistry because it will help them become a doctor which will in turn help others and society.

### Promoting and Changing Extrinsic Motivation into Intrinsic Motivation

A key reason why it is important for students to improve their intrinsic motivation is that it leads to overall improvements in both psychological health and academic success. Knowing this, many educational psychologists are constantly working towards finding ways to promote the benefits of intrinsic motivation for students in both their school subjects and emotional health. We can look at literacy as a clear example. On average, 73% of American children do not read for enjoyment and we can assume that this rate is high due to students not realizing the benefits of finding intrinsic motivation in literacy. Results show that students who enjoy reading perform higher in comprehension and overall feel more contended; similarities were found with Math. Over the K-12 schooling years for students, academic intrinsic motivation for math shows to have the highest decline. Math has shown to be able to energize students, and those who have intrinsic motivation have higher problem solving skills as well as higher confidence levels when solving complex problems in different aspects of life Students with special needs and special education also proved to show higher rates of confidence. For these students, this positive impact can result in higher hopes for high school completion rates and achievements after finding intrinsic motivation. In regards to emotional behaviour and health, students who were found to have high levels of intrinsic motivation were overall happier with life, and further created a friendlier and positive school environment. Increased motivation also promoted positive social qualities such as being helpful, friendly, and caring. Considering this aspect, results also showed a positive decline in drug use, violence and vandalism.
With understanding the benefits of promoting internal motivation and also acknowledging the degrees of extrinsic motivation, we can now work towards looking at a common concern as to how to change external motivation into internal motivation in the classroom. Let us use an example of a grade 7 class that is spending time on a specific chapter in science. Some of the students feel that the content they are being taught is extremely uninteresting and pointless. The approach teachers should take in a scenario like this is the process of Internalization and Integration, which aims to promote and discover the value of what is being taught. Internalization is the method of analyzing the explicit reasons as to why one chooses to do something with external motivation. Integration is process of taking those external reasons and converting them to come from one’s own self. Here we will see motivation transform from something that was once external (left spectrum) to something that becomes more internal (right spectrum). However, this process can only be achieved or facilitated once students are placed into environments that are allowing them to feel self determined and fulfilled in all three psychological needs of competence, relatedness, and autonomy. The ways that teachers can support this is by allowing students to have a voice and a choice in the academic activities that they engage in., assigning learning activities that are challenging with providing them with the tools and information needed to succeed in the activity., in addition, creating an environment that makes them feel valued, respected, and regarded positively by their teachers and peers.

### Cognitive Evaluation Theory and Rewards

On a similar note, The Cognitive Evaluation Theory (CET) is a sub section of the Self Determination Theory that was presented by Deci and Ryan (1985). It states that any event that becomes interpersonal or relational, and helps to promote the feeling of both competence and autonomy, will in turn cause intrinsic motivation.The theory however, stresses that they’re both interrelated and that the feelings of competence will not promote intrinsic motivation unless it is aided by the sense of autonomy. Moving this theory into the classroom, when teachers look at assigning a given task or assignment, they should look to see if the guidelines will fulfill the needs of competence and autonomy in the student. For example, this can be seen when a teacher assigns an individual project or presentation for science class. If the teacher allows the students to chose their own topic and pick between giving a project or presentation, it allows them to have control (feeling of autonomy), which in effect allows them pave their own pathway towards feeling successful (feeling of competence).
Under the CET, research has been continuously worked on to find the results of using rewards, feedback, and other external events on intrinsic motivation to see if it further promoted or decreased the feeling of competence and autonomy. The Cognitive Evaluation Theory explains that external events can do either, depending on how one’s self determination or competence is perceived. If an event decreases the way one perceives them, it will decrease intrinsic motivation whereas if it increases the way they perceive them it will increase it. The Cognitive Evaluation Theory also claims that the two aspects of rewards, wether they are either informational or controlling, can answer this question Informational increases intrinsic motivation and controlling decreases it. To determine if a reward is either controlling or informational, it is first important to define the difference between verbal and tangible rewards.
Verbal Rewards are often replaced with the common term “positive feedback ” It has strongly been suggested and assumed that positive feedback will increase intrinsic motivation as it is likely to fulfill a student’s need to feel competence and be informational. However it is important to realize that verbal rewards may have a controlling aspect as well. It can lead to a student doing actions for the sole purpose to gain appraisal and approval. (i.e. teacher or peers). The CET therefore claims that the rewards must be looked at in the terms of interpersonal context which looks at the social atmosphere that students are surrounded by( i.e. a classroom)
Tangible Rewards are opposite of verbal words and are rewards that are strongly associated with being controlling and contributing to decreasing intrinsic motivation. For them to be controlling, they have to be looked at as rewards that are offered as incentives for students to do things that are out of their regular norm. This could mean that the student would be motivated to do something because they knew what the expected outcome of the reward was going to be.The CET takes this understanding and explains that expected tangible rewards are broken specifically based upon the tasks or circumstances that students are asked to participate in. It outlines that there are three types of reward circumstances.
| Task-Non Contingence | Rewards that are given to students for participating in an activity |
| Task-Contingence | Rewards given to students for completing an activity |
| Performance-Contingence | Rewards given to students for completing an activity, showing success, and performing well |
The use of rewards in the classroom has been a long term debated topic, as both researchers and teachers aim to consider what kinds of rewards are best to give to students and when they are the most appropriate to give as well. A recent meta analysis study was done to test the effects of verbal and tangible rewards in the classroom. The effect of verbal rewards showed exactly what was claimed above. When verbal rewards were informational they increased intrinsic motivation and if they were to be controlling it decreased it. They also found that verbal rewards had a higher significance in increasing intrinsic motivation in adolescents in college than younger students in primary school. Similarly, similar results were found with experiment with tangible rewards as they showed to diminish intrinsic motivation. However, in this situation, the effect on students was higher in younger students than for adolescents in college. In regards to teachers and educators giving rewards, it can be implemented in the classroom but only to be used in an appropriate manner. Verbal Rewards are highly recommended however only when it informational. Although Tangible Rewards show negative results, they too can also be implemented in the classroom, however the best method for implementing them include making the rewards unexpected so that the students are not aware of what will be rewarded to them.

### Teachers

While working to apply the Self Determination theory in the classroom with students, it is important to analyze the environments that students are exposed to and look at the effects of how the external environment plays when working towards creating an environment that creates intrinsic motivation. Here we will look at the effects of teachers, and the effects that teachers have directly upon students.
Because teachers play a major role in a student’s life , there are many ways teachers can influence students. The first way teachers can achieve this is by simply being intrinsically motivated themselves A study was done to see if intrinsic motivation from teachers could disseminate to students in a high school physical education class. Results positively showed that when working with a intrinsically motivated teacher, higher levels of intrinsic motivation in physical education were achieved than working with a teacher who was extrinsically motivated for external rewards( i.e. being paid) Similar results to promote an intrinsic environment were shown when a study was done to look at how teacher’s support for basic needs effected school bullying levels. The study included looking at 536 students, grades 7-9 in different Hong Kong secondary schools, where students were asked to fill out a questionnaire based on different measures they had felt throughout that semester. Some of the questions included asking students how often they excluded someone, how often they felt they were a bully victim, and how often they felt their teacher showed support in relatedness, autonomy, and competence. Results showed that the lowest amount of bullying took place in schools when teachers had shown high levels of support in relatedness, and students had felt that they had a personal bond and open relationship with their teachers.

## Expectancy Value Theory

In a school and learning environment, students are always making choices when it comes to what motivates them and how they act on that motivation. These choices often revolve around how much effort to put into different activities; for example one student might not put any effort into their schoolwork, but may try exceptionally hard in sports, while another student puts effort into every class but physical education. Expectancy Value Theory; which was developed by Atkinson and built upon largely by Eccels and Wigfield, tries to explain this concept by stating that performance and choice are most strongly influenced by the specific understanding a person has on what they are capable of in different fields, and on what they find important to them. Culture, emotion, and outside parties such as parents or teachers have also been deemed by researchers such as Richard Pekrun to be influential in adding value to certain activities.

### Wigfield and Eccels' Model of Expectancy Value Theory

One of the most well received models of the Expectancy Value Theory was initially developed in 1983 by Wigfield and Eccels, with the model still being further developed. What makes Wigfield and Eccel’s model of Expectancy-Value Theory significant is that it easily applicable to teaching and learning as it examines the individual more closely. As a whole, Wigfield and Eccles' model examines ability and expectancy beliefs and personal values as significant to the expectancy-value theory. Expectancies and values are influenced by an individual’s beliefs in their abilities, which tasks they define difficult or simple, goals, and past learning. Expectancies and values are then in turn seen as directly influencing an individual’s choices, performance, effort, and persistence.
When discussing the model in the context of schooling, Eccels and Wigfield identify four different values that are present in the classroom.
| Intrinsic Value | Utility Value | Attainment Value | Cost |
| The level of enjoyment a specific activity or task gives an individual. | An individual finding a certain task or activity to have a quality of usefulness; whether it is related to a present or future goal, or to please parents or friends. | An individual recognizing the success in a certain activity as important. | How an individual views a certain task or activity in terms of its cons, such as if any other opportunities will be lost in the place of doing this one and the amount of effort it will take to complete. |
These values further tie in with ability beliefs to create the certain expectancies and levels of motivation an individual sets to a certain task. Ability Beliefs are defined as an individual’s insight on how capable they are at certain kinds of tasks. Wigfeild and Eccels state that while ability beliefs are based on present ability, expectancies are based on what they expect of themselves in the future.
Another important aspect of the model to examine in regards to the classroom is how a student’s expectancies and values develop over the years, and when they start to develop. Wigfield and Eccles state that children start recognizing what activities they are good or bad at and what value different activities have from as early as kindergarten or grade one. This includes the various domains found within a school environment, such as math, reading, music, and sports. These insights on what areas they were successful in also changes over the years as students continue learning. For example, ability and expectancy beliefs for reading generally increase from a grade four student to a grade seven or grade ten student, meaning that an individual who does not view themselves as a strong and confident reader may eventually become confident in their reading skills. This is especially significant because it shows that teachers can try to help students develop their values and expectancies in different subjects. However, an individual can also experience a decline in their values and expectancies for different subjects. For example, students tend to value math more in elementary school than they do in high school. Wigfield and Eccel’s model describes that this can be due to two main reasons; first that children become better at self-assessment through criticism and comparison and can therefore highlight weaknesses in their abilities and in what they value. The second explanation is that the school environment changes over the years of elementary and high school by becoming more competitive, which leads to students adjusting their expectancies for achievement.

### Raising Value in the Classroom

A first question to ask when examining ways to increase student value towards class material is what makes value something worth spending time on? In 2012, a research study by Gregory Liem and Bee Leng Chua was done to examine expectancy and value in the classroom and which were more effective in student raising motivation and performance. The study consisted of a sample of 1664 Indonesian high school students in Civic Education classes in West Java. Selected from a total of six schools, the 1664 students included 812 males and 852 females from the Year 7 to Year 12. In order to assess whether expectancies or values were more influential, Liem and Chua gave the students a set of questionnaires that assessed what the students expected from themselves in their civic education classes, how they valued the class, their future goals, their civic capital, and factors such as gender and school level. Overall, the questionnaires showed that though expectancies were effective, values had a much stronger effect on individual students’ motivation and performance (304). This means that in a teaching setting, it is important to try to make connections to the different values that may be present in students. As discussed earlier, Wigfield and Eccles’ model identifies four different values; utility value, intrinsic value, attainment value, and cost.
1. Strengthening Utility and Attainment Values
In Liem and Chua’s study, it was also found that motivation to learn and interest in material in the civic education class were especially strong if the student’s future and career goals were related to civic education. This means that as the subject material had a direct quality of usefulness to those students, they possessed a higher utility value for the subject material. Furthermore, the direct utility value the students shared with the material also raised a higher attainment value, meaning that it was important for them to do well in that class. Therefore an effective way to get students more motivated to engage with certain material is to teach them why education is important, why the specific lesson is useful to them, and how it connects to the future role they will play in society. Though university students have a better understanding of which courses offer utility value, high school and elementary students may need extra help from teachers to explain why the connection is useful to them. For example, for high school students some ways to engage utility value is to discuss any university programs or future career goals they may be interested in and make explain how that course is relevant to them; such as explaining that a programming class can provide a good knowledge base for any who want to go into Information technology. For activities such as essay writing, this may make a student more eager to learn and motivate them to do well in the class; especially as students are starting to look into universities. Engaging utility value in elementary students may be extra beneficial for the students as an early understanding of why education of certain material is important may increase the students overall desire to learn and engage in schooling. As elementary teachers work more closely with the same group of students in every subject, they have a unique opportunity to appeal to their students as to why certain material is important. One of the best ways to do this is to be selective of what material is chosen to teach and how to teach it while still staying within curriculum, which can also be taken into account for high school students. For example, Jere Brophy of the University of Michigan states there are three significant steps that can be taken to aid in this.
| Step | Significance |
| 1. Curriculum Development | Careful selection of curriculum to make sure everything the students are learning is worth learning while still following school requirements. |
| 2. Scaffolding Application | Apply scaffolding techniques to make sure students are given plenty of opportunities to develop new skills and learn for themselves how to apply what was learned while making sure they are applying their knowledge in beneficial ways. |
| 3. Lesson Framing | Frame Lessons in a way that makes sure to explain the value and application of all material and skills being taught. |
Overall, explanation and understanding is the key to engaging student’s utility and attainment value by having students understand how to apply the skills they are learning and why they should want to succeed in learning them.
2. Engaging Intrinsic Value
As stated earlier, intrinsic value is the level of interest or enjoyment a student finds in lesson material. Intrinsic Value is closely related to the Self-Determination Theory aspect of intrinsic motivation that was described earlier, as whenIan individual finds intrinsic value in a task, it can become intrinsic motivation. Intrinsic value and intrinsic motivation can be very varied depending on the individual as all individuals have their own specific interests. This also means that what an individual finds intrinsic value in cannot be changed with extrinsic factors. Therefore, as teachers cannot change what a student finds interest in, one of the most effective ways for a teacher to raise student intrinsic value is to build and maintain a good relationship with their students. Overall, studies show that an emotionally and academically supportive teacher can lead to higher interest and intrinsic motivation, and therefore higher academic effort. A study conducted by Julia Dietrich a, Anna-Lena Dicke , Barbel Kracke and Peter Noack with math teachers shows how positive and negative relationships with teachers can affect both the individual and the classroom. On an individual level, a supportive teacher led to higher positive associations with intrinsic value, effort, and long-term development in math; while an unsupportive teacher led to a negative association and lower development. On a classroom level, a shared perception between students of a teacher as supportive led to a positive association of class levels of intrinsic value and motivation, with increased skill development over the year. However, if a class deemed a teacher unsupportive, class levels of motivation were lowered. This shows that it is overall beneficial try to provide students with emotional and academic support. However, this is further developed by good relationships with multiple teachers, as it leads to positive comparison. Dietrich et al’s paper describes this Comparative Process as comparing one’s own achievements with their own achievements in other classes or with other students. This process can also occur between experiences with other teachers. For example, if a student finds the teacher of one grade or subject to be less supportive than another teacher, their motivation in the class of the less-supportive teacher may decrease. This shows that while it is important to ensure that a teacher is creating an emotionally and academically supportive environment, it is equally important that all teachers and staff work together to ensure that they are all setting a similar teaching standard for their students.
3. Overcoming Cost Of all of the values described in Wigfield and Eccle's model, cost is the most unique as it is influenced by both intrinsic and extrinsic factors. Though engagement of the other values may limit the amount of negatives a student identifies in a task by increasing its importance, many of the qualities of a task that influence an individual student to make decisions are outside of the teachers control. This increases as students get older and begin to be in charge of more decisions, for example a high school or university student picking their courses. An article by Jessica Flake et al describes how cost can be split into four identifications, Task Effort Cost, Outside Effort Cost, Loss of Valued Alternatives Cost, and Emotional Learning Cost. The chart below is an adaptation of a chart shown in Flake's article that explains how different costs lead to different decisions and behavior.
Though teachers may not be able to limit what cost values a student might be influenced by, learning how these costs affect student's decisions can provide a deeper understanding of why students make specific choices. This can then better prepare a teacher to cater to the needs of individual students and identify specific problems.

### Raising Expectancy in the Classroom

Though the Wigfield and Eccle’s model of Expectancy-Value theory focuses more strongly on values, expectancy and ability beliefs are still fundamental the theory’s application. As mentioned earlier, building on student values within the classroom is an effective way to also raise expectancy. However, there are still other ways that expectancies can be built upon. Furthermore, as expectancy and ability beliefs are considered to be more domain specific over activity specific, they can be improved in a much more general way than values. For example, one of the most applicable ways to raise expectancy in the classroom is by building on base skills.
The Importance of Reading Programs
Improving students' expectancy and motivation in reading is one of the most effective and easily applied ways to increase an individual’s overall expectancy. As reading is a base skill, a higher expectancy in reading skills can then make a student more confident in their overall learning abilities. A study by Christopher Nkechi showed that the implication of Extensive Reading Programs; a program that requires students to read several books over a span of a few months, were beneficial in increasing motivation through raising self-expectancy in reading. Many researchers have done studies showing that these programs are extremely beneficial for students whose first language is not the language being taught, with many examples using English as the second language. However, Nkechi showed that these Extensive Reading Programs are also extremely beneficial for students who already speak the language being taught, using native English speakers in his study. One of the main aspects of the Extensive Reading Program used in Nkechi’s study was Literary Circles. Literary Circles is a group activity that uses scaffolding strategies by assigning each group of students with a novel to read, and then requiring each student to go through a rotation of assigned roles. These roles not only encourage the students that might otherwise be disengaged or unwilling to read the novel in order to keep up with their group, but require the students to find meaning and message in their readings.
For Nkechi’s study, 96 students were split into groups and rotated through three to four novels. Each student was then asked to fill out a questionnaire about their expectations for the program both before and after completion of the program. Once the students completed their program after several months, the results showed that the program overall raised student expectancies and beliefs in their reading ability. Overall, the study showed that Extensive Reading programs help students become more capable in different aspects of language and how to use their capabilities in different forms of media and activities. These ER programs also help develop vocabulary, which is significant as in order to make sense and meaning of texts an individual needs a 97-98% vocabulary coverage. Though lessons can target and teach certain specific words, these reading programs supply students with general vocabulary and how to recognize it. Extensive Reading programs also supply a student with more exposure to grammatical laws, which can provide deeper examples after the basics have been taught to them. As reading provides a base from which to learn many different subjects, increase in expectancy in reading abilities can also help raise expectancy in other subjects where reading in order to understand subject material is required. Therefore, as a whole these programs are easily implemented in a classroom and are successful in increasing students expectancies in reading and comprehension in all subjects.

## Goal Orientation Theory

Early conceptualizations of goal orientation theory are derived from James A. Eison's work on dimensions of student's learning and grade orientations. Eison looked at the structure of student's educational and personal differences; he viewed them in relation to learning for genuine acquirement of knowledge, versus performing for attaining high grades. Subsequently, Dweck postulated similar ideas categorizing mastery and performance goal orientation. Dweck's work established goal orientation theory as a two-dimension construct wherein students either approach situations with the motivation to master and acquire new skills, or perform in order to gain approval and do better in comparison to others. People have different reasons for setting goals and as such, each person approaches their goals differently. Goal-orientation theory seeks to explain the underlying implications of motivation in academics. Students are categorized by their mastery goal orientations or performance goal orientations. A mastery goal orientation reflects genuine purpose as people work towards mastering a set of skills in order to accomplish a task. Students with mastery goal orientations pursue goals for their own sake. It is important for teachers to structure lessons that assist students in obtaining a mastery goal orientation. Teachers can accomplish this by relating learning to personal growth and by co-constructing objectives that are relevant to the student's interests. Consequently, by focusing on personal growth in the learning process, teachers can increase intrinsic motivation which activates a mastery goal orientation.
Studies have also found students that adopt mastery goal orientations demonstrate more adaptive self-regulatory behaviors and social attitudes, which contribute to an increased interest in learning. Teachers must be willing to continually adjust their methods and instructions in order to create optimal learning conditions. In doing so, they create an environment that aligns with their student's goal orientations. The instructional approach must avoid tasks that encourage memorization and rehearsal, for example. However, how can teachers ensure their students are learning appropriate information without incorporating tests and exams? Teachers can facilitate in-class discussions, group projects, papers, and presentations in order to gauge the level of understanding and also the amount of content being absorbed by students.
Performance goal orientations highlight how well an individual can demonstrate success in tasks and understanding. Performance-oriented individuals are competitive and focused on personal gain prompted by extrinsic rewards. Furthermore, mastery and performance goals can be divided into subcategories of avoidance. The former, describes students who wish to avoid misunderstanding tasks, lessons, or instructions; the latter, describes students who wish to avoid appearing incompetent during performance. Overall, students with mastery avoidance and performance avoidance goals fear failure. Teachers must avoid creating a class atmosphere that is high risk and high reward. That is, they must place less emphasis on external motivation and achievement in relation to others. The structure of the classroom is contingent on the teacher's representations of goals, values, and beliefs; for example, does the teacher focus on how well students perform in comparison to one another, or how the students improve throughout the year?
Students can have adaptive goal orientations because they engage in multiple goal paths. Studies also identify a combination of learning and performance cues that exist outside the classroom; two prime examples are the ways in which parents and peers influence student motivation. Consequently, teachers need to be aware of how parents and peers contribute to shaping of a mastery goal orientation. In a longitudinal study conducted by Juyeon Song, Mimi Bong, Kyehyoung Lee, and Sung-il Kim, surveys were administered to assess variables in learning and home environments that influenced student's motivation; psychological attitudes students felt towards school were included in the assessment as well. Subsequently, the data was used to measure the degree of perceived support from parents and teachers; they found that certain types of support promoted different types of goal orientations. Parents and teachers that stressed achievement increased test anxiety, compared to parents and teachers who supported students with emotional encouragement. The preceding study supports the notion that teachers need to foster intrinsic motivation in the classroom. They can do so by continuing to nurture student's emotional development so there is no discrepancy between the care and support they receive at home and at school; in this way, teachers are also able to combine the student's home and school lives representing a comfortable space for students to develop their learning. Moreover, offering emotional support shows student's that they are worthy of care and this can reverse adverse effects of achievement pressure.
Another study by Javier Fernandez-Rio, Jose A. Cecchini, and Antonio Mendez-Gimenez tested cooperative intervention programs against traditional teaching programs in order to find out which method generated more intrinsic motivation. The study participants were university students between their early twenties and early forties. The participants were split into either an experimental condition in which they were taught through cooperative reciprocal learning, or they were placed in the control condition wherein traditional unilateral instruction was applied. The cooperative intervention program influenced positive perceptions of competence and enhanced intrinsic motivation. In addition, cooperative learning encouraged students to work with one another and problem solve together. If applied in a classroom setting, cooperative learning supports mastery goal orientations through peer to peer interaction as they learn to work together and not against each other; as they are required to solve problems and work through differences to achieve a common goal.
Both of the studies presented above hold important implications for the classroom. Finding the source of motivation can also assist in guiding future goals, achievement goals, social goals, and personal well-being goals towards a more mastery oriented goal state. Parents and peers are significant influences in a student's motivation and as such, teachers must learn to implement their influence in the class. The studies presented above provide teachers with strategies and techniques to approach their class with. Applying social goals in particular, can create more opportunities for peer to peer involvement and can foster a cooperative class climate as well. Feeling comfortable and connected to peers helps students discover meaning which enhances the development of a mastery goal orientation. The sociocultural framework helps teachers investigate motivation through its use in cross-cultural contexts. It enables teachers to identify aspects of the class climate that sustain mastery; for example, by allocating more time for group work and discussions. Parent, teacher and peer involvement are intertwined; teachers must always keep this in mind so they can understand their students and their intentions for learning. Consequently, teachers can support mastery by guiding future goals, achievement goals, social goals, and personal well-being goals if they involve all aspects of the student's home, school, and social life.
Assessment and intervention are two methods in goal-orientation theory that can help identify and shape the types of goal orientations that will persist in the class. One way teachers can assess whether a mastery or performance goal orientation exists is by applying interventions such as the Likert scale. Questionnaires help teachers get a feel for the class’ impressions and expectations. Surveys also assist teachers in acquiring important information about their student’s beliefs regarding success in the class. Teachers can use this data to reorder their instructional process and better explain a path to meaningful success. Surveys are beneficial because they can vary in specificity and target information. For example, asking students to share their aspirations and motivations can provide insight into student conceptualization of the learning process and therefore, assist teachers in setting classroom objectives that support a mastery goal orientation. In the same way mastery goal orientations can balance performance goal orientation, qualitative methods can complement quantitative methods. In applying a diverse range of methodology such as open and structured observations, talk-aloud protocols, conversation analysis, life history and ethnography, teachers can gain a fuller understanding of the nature and origins of goal orientations.

### Goal Structures

There are two types of goal structures that align with the mastery and performance goal orientations. The goal structure however, refers to the environment and the ways in which outside conditions can affect student’s motivation, cognitive engagement and achievement. It emphasizes the specific goals to be achieved in the classroom by way of instruction and practice. The teacher must be cautious when organizing the curriculum as the types of tasks delegated and marking process influence goal structure. In addition, the level of freedom students are given to explore and group arrangement, both contribute to forming a particular classroom goal structure.
As noted above there are two types of goal structures known as mastery goal structure and performance goal structure. A mastery goal structure embodies a learner focused environment wherein the standards and policies encourage students to try hard and do their best. Teachers can create a mastery goal structure through clear explanations of the objectives; for instance, by telling students the purpose of performing tasks is to expand their knowledge. Teachers that offer choice in their activities, such as allowing students to pick their own essay or presentation topics, piques interest by targeting subjects students are passionate about. Students are taught to value themselves as well as the learning process in this way as well.
A performance goal structure creates an atmosphere of rivalry and competition. Success comes from obtaining extrinsic rewards and performing competently in various tasks. Teachers can better shape their classrooms by determining which goal structures foster approach and avoidance goals. For example, mastery goal structures foster mastery approach goals. Teachers can administer anonymous surveys and the questions can help indicate whether students acquire more of a mastery or performance goal orientation. In addition, because goal structures usually mirror the environmental conditions, they are observed as impacting the specific goal orientations that students adopt. Applying this to a classroom setting, teachers must remain cognizant of the goals students perceive as being important in the class because they will correspond to their personal goal orientations.
Research has proposed that teachers who placed higher worth on learning and working hard resulted in students viewing their environment as mastery structured; therefore, students were more likely to assume a mastery goal orientation. Teachers can implement classroom contracts at the beginning of the school year to solidify the working conditions. Cultivating mastery goal structure enhances student drive for more challenging work and they are better able to adapt in order to succeed. Students learn to effectively employ learning strategies in the presence of mastery goal structures as well. Self-report measures assist teachers in identifying connections and discrepancies within student’s goal structures and goal orientations; they are able to analyze reported levels of choice, effort and persistence in order to understand a student’s adaptive motivational engagement. Ultimately, mastery goal structures promote mastery goal orientations that encourage intrinsic motivation, cognitive engagement and achievement.

### Mastery Goal Orientation and Performance Goal Orientation

Goal orientations originate in schemas and can be made purposeful in context. Students perceive cues and prompts from the situation that leads them to adopt either mastery goal orientations or performance goal orientations. Asking student’s questions about their past can trigger positive intrinsic experiences that reactivate their schemas for mastery goals. By asking students to draw upon experiences of happiness and success during their academic careers, teachers place more of an emphasis on mastery goal orientation that can be similarly attained in the class. Questions that require deep reflection also help students continually adapt and challenge their goals to coincide with their mastery goal structures. Students can recognize differential emphases on mastery goal orientation and performance goal orientation. Subsequently, they align their perspectives and behaviors accordingly.
Tasks, authority, recognition, grouping, evaluation and time are all aspects of the class setting that influence goal orientation. The following examples illustrate the implications and relationships to instruction.
Tasks
Teachers must consider what they are asking their students to do when assigning specific tasks. What is the outcome they wish to obtain? If teachers are asking students to listen to a lecture and soon after write a quiz, students will adopt a performance goal orientation. The demand level and structure of such a task places external pressure on students and detracts from a meaningful experience. In order to prevent this from happening, the teacher can engage students through a more flexible task structure. For example, allowing students to participate in an online discussion forum allows them to go at their own pace and use their creativity. Discussion forums are powerful because students can internalize input from their peers in order to create meaning.
Authority
Authority refers to the teacher’s dominancy or openness towards the structure of the class rules and regulations. Strict regulations and rules reflect intolerance for change insinuating students are not active participants in decision-making for their own learning. However, teachers can create contracts with students in order to layout guidelines and responsibilities. Furthermore, instructors can assign a date in the middle of the school year to request feedback and make revisions if necessary. In this way teachers demonstrate their concern for student's wellbeing and personal growth.
Recognition
Recognition addresses the outcomes and actions that must be attended to in order to foster mastery goal structures. Extending effort, taking risks, being creative, sharing ideas and learning from mistakes are all acceptable and functional behaviors to encourage within the classroom. In addition, teachers should express praise in private because publically commending students can foster competition and undermine the abilities of others.
Grouping
Grouping takes different dynamics into consideration. Criteria includes appreciating differences by grouping students with different domains of interests together; in doing so, students are given the opportunities to share, interact and interpret perspectives outside their own. Groups represent the inherently social climate embedded in the class. Mastery is co-constructed as teachers and peers participate in guided meaning making.
Evaluation
Evaluation communicates much about task, teacher and overall course objectives. Therefore the manner in which evaluation is carried out holds vast implications for both instructors and students. Teachers must avoid comparing students based on final outcomes and they can do so by evaluating based on progress, creativity and mastery of skills. Much like recognition, evaluations should also be conducted in private. Teachers can implement weekly progress reports and students can track their personal growth. Allowing students to measure their mastery of skills also allows the teacher to gauge what types of adjustments and provisions could be offered.
Time
Time is a critical factor in establishing a mastery goal structure and mastery goal orientation appropriately. Time restrictions communicate completion over quality. For this reason, teachers should be accommodating by letting students work at their own pace. Teachers must also be open to allocating time according to the level of task difficulty. For example, although some students can complete their work by the end of class, other students may feel anxious from the time pressure and thus, require more time. Moreover, teachers can leave more class time to complete work, but allow students to take the material home as homework if work remains incomplete. Mastery goal orientations maintain a stronger motivation to learn because they nurture personal growth in the learning process while fostering an ongoing desire to improve.
| Tasks | Authority | Recognition | Grouping | Evaluation | Time |
| Allowing student to choose their own topics for research | Instructor is open to collaborating with students | Identifying creativity and learning from mistakes | Grouping by diversity; pairing students with a variety of learning strategies | Holistic approach reviewing progress and development; encouraging reflection | Allocating an adequate amount of time for learning and structuring knowledge |

### Mastery Avoidance and Performance Avoidance

Mastery avoidance goals and performance avoidance goals are concerned with the image one reflects. For example, students with a desire to avoid performing poorly and appearing incompetent in comparison to others are concerned with performance avoidance goals; whereas, students concerned with mastery avoidance goals strive to avoid misunderstanding the task or material presented. Performance avoidance goals have been tied to negative outcomes and low achievement. Generally, performance orientations are less adaptive than mastery orientations regardless of the approach or avoidance orientation that results. Moreover, in relation to the self, performance avoidance goals are associated with negative emotions and overall, wellbeing. Subsequently, students characterized by mastery avoidance fear becoming incompetent as a task and strive to evade it at all costs. Akin to performance avoidance goals, findings have revealed that mastery avoidance goals are also linked to maladaptive outcomes including poor implementation of cognitive strategies and procrastination. It is not enough to encourage mastery goal structures and mastery goal orientations in the class; teachers must also understand the roles that avoidance orientations play and their implications for instruction.

## Summary of Motivation

The purpose of this chapter is to demonstrate how motivation can be increased in the classroom through certain popular theories such as the Self-Determination Theory, the Expectancy Value Theory, and the Goal Orientation Theory. In general, we can see that a good reason to encourage intrinsic motivation is because it leads to increased levels in both psychological health and academic success. Setting the context for learning is an important aspect of the teaching environment because it influences the goals set out for the class. Encouraging intrinsic motivation supports student's genuine purpose and passion to master skills. In the self-determination theory we saw that intrinsic motivation is triggered once students feel fulfilled in three psychological needs which are autonomy, competence, and relatedness. In the expectancy value theory we looked at how a student's performance and choice are influenced by what they expect of themselves as well as what they value. More importantly, we look at how to increase expectancy and value in the classroom in order to raise motivation. In the goal-orientation theory we saw that evaluations hold important implications in the classroom by allowing time for reflection on the development of mastery. Through this chapter we hope that present and future educators can use these applications as a way to increase motivation in the class.

## Suggested Reading

Motivational Beliefs, Values, and Goals
Eccles, Jacquelynne, & Wigfield, Allan. (2002). Motivational Beliefs, Values, and Goals. Annual review of Psychology, 53. 109-132.
Expectancy-Value Theory of Achievement Motivation
Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 68-81.
The Contributions and Prospects of Goal Orientation Theory
Kaplan, A., & Maehr, M. L. (2007). The Contributions and Prospects of Goal Orientation Theory. Educational Psychology Review, 19(2), 141-184.
Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students' Motivation, Cognition, and Achievement
Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
Intrinsic Versus Extrinsic Goal Contents in Self-Determination Theory: Another Look at the Quality of Academic Motivation.
Vansteenkiste, M., Lens, W., & Deci, E. L. (2006). Intrinsic versus extrinsic goal contents in self-determination theory: Another look at the quality of academic motivation. Educational Psychologist, 41(1), 19-31. doi:10.1207/s15326985ep4101_4

## Glossary

Ability Beliefs: Ability Beliefs are the beliefs an individual has on how capable they are at certain kinds of tasks.
Attainment Value: Attainment Value is the value an individual finds in a certain task or activity from recognizing that success in that activity is important to them.
Autonomy: Autonomy is the ability to be self regulated and self initiating of ones' own behaviour and actions.
Cognitive Evaluation Theory (CET): The Cognitive Evaluation Theory is a sub section of the Self Determination Theory that was presented by Deci and Ryan (1985). It states that any event that becomes interpersonal or relational, and helps to promote the feeling of both competence and autonomy, will in turn cause intrinsic motivation.
Competence: Competence is the ability to attain different outcomes both externally and internally by using the environment they are surrounded by.
Cost: Cost is the negative qualities that an individual attaches to certain activities or tasks. Examples of this include missed opportunities from selection of that task over others, and the amount of effort the activity will take.
Curriculum Development: Curriculum Development is the careful selection of curriculum and content to ensure that everything students are being asked to learn is worth learning.
Expectancy Value Theory: Expectancy Value Theory is a theory first developed by Atkinson that defines performance and choice as being influenced by the certain values and self-expectations an individual has for certain activities.
Extensive Reading Programs: Extensive Reading Programs are programs that require students to read several books over a span of a few months, and are beneficial in increasing motivation through raising self-expectancy in reading.
Goal-Orientation Theory: Goal-orientation theory explains the reasons and choices individuals make that maintain motivation. The theory states that individuals have two major goal orientations; mastery goal orientations and performance goal orientations.
Goal Structure: Goal structures embody the learning environment. Goal structures are shaped by the language used by an instructor, the assigned tasks, and the incentives employed to facilitate learning.
Intrinsic Value: Intrinsic Value is the level of enjoyment and interest an individual finds in a specific activity or task.
Lesson Framing Lesson Framing is the structuring of lessons in a way that makes sure to explain the value and application of all material and skills being taught.
Literary Circles: Literary Circles is a method used in Extensive Reading Programs that uses scaffolding strategies by splitting students into groups, assigning each group of students with a novel to read, and then requiring each student to go through a rotation of assigned roles. Each group typically reads more than one novel together.
Mastery Avoidance: Mastery avoidance is the desire to avoid misunderstanding tasks and information.
Mastery Goal Orientation: Mastery goal orientation focuses on intrinsic growth and development. Individuals who acquire a mastery goal orientation are genuinely motivated and value the learning process.
Mastery Goal Structure: Mastery goal structures influence mastery goal orientations. Mastery goal structures foster learner focused environments based on intrinsic motivation.
Performance Avoidance: Performance avoidance is the desire to avoid performing poorly and appearing incompetent in comparison to others.
Performance Goal Orientation: Performance goal orientation focuses on extrinsic rewards such as grades, prizes, and praise. Individuals who acquire a performance goal orientation only wish to appear competent in relation to others.
Performance Goal Structure: Performance goal structures influence performance goal orientations. Performance goal structures foster competitive environments based on extrinsic reward.
Relatedness: Relatedness is the level of connection one feels from their social environment.
Scaffolding Application: Scaffolding Application is the application of scaffolding techniques to ensure that students are given opportunities to develop new skills and learn for themselves how to apply the skills they have learned.
The Self- Determination Theory: Self- Determination Theory , first introduced by Edward L. Deci and Richard M. Ryan, and is a sub section of motivation that primarily looks at two different types of motivation. It states that each type of motivation is built upon a reason or goal that eventually develops into a certain behaviour.
Utility Value: Utility value an individual finds in a task or activity related to the degree to which an individual finds a certain task or subject to be useful to any short term or long term goals.
Wigfield and Eccles' Model of Expectancy Value Theory: Wigfield and Eccles' model of the Expectancy-Value theory states that expectancies and values are influenced by an individual’s beliefs in their abilities, which tasks they define difficult or simple, goals, and past learning.
This chapter examines the role of attribution and emotion in teaching and learning. We will be discussing attribution theory, the four stages of the attributional process, methods for helping students cope with emotions, attributional retraining and implications for instruction. Any event that occurs in our everyday lives can be interpreted in a variety of ways, depending on what we identify as the cause of the event. Our causal attributions have consequences for our emotions and behaviours which, in turn, affect learning and achievement. Attribution theory classifies emotions and links them to types of attributions. As educators, we can take our student's affective and behavioural responses into consideration to ensure that they know how to cope with their emotions. In addition to our student's emotions, we should also be aware of our own feelings and how they are expressed towards our students. Attribution theory can be applied in the classroom environment by providing attributional retraining to students identify and change their maladaptive attributional responses.

## Attribution Theory

We often come across events in our lives that can be interpreted in several different ways. The explanation that we come up with to describe the cause of an event is referred to as an attribution. The way an event is attributed causes us to react with a variety of responses. To study how people interpret events taking place in their lives, researchers use attribution theory. Attribution theory gives insight into why people have different responses to the same outcomes.
To illustrate the theory, imagine that two students take a math test and both end up receiving 60 percent. One student is very disappointed with herself and vows to create a study group in order to earn a better grade for the next test. She also goes to her teacher for extra help. The second student is angry when she sees her test grade and goes to her friends to see how they did. When she discovers that a few of her friends also performed poorly, she attributes her failure to a poorly written test. Although the outcomes of the situation are the same for both students, the way they interpret and respond to the experience is very different. Later on in the chapter, we will take a more in-depth look into how different attributions affect the way we cope with failure. We can gain a deeper understanding of why people make specific attributions, what the most common attributions are, what types of affective responses are elicited and the effect that attributional judgments have on our behaviour by studying the attributional process.

## Importance of Attributions as a Predictor of How People Cope with Failure

The significance of attributions is highlighted in the study "Importance of Attributions as a Predictor of How People Cope with Failure" done by Follette and Jacobson. The purpose of this study is to replicate and expand on the research of Metalsky et al. (1982), which focuses on the reformulated learned helplessness model (RHL). Measuring general attributional style, specific attributions for examination performance and the prediction of motivational deficits, this study aims to emphasize the significance of attributions to help predict how people cope with failure in a classroom setting. We will be referring back to this study throughout the attribution theory section of this chapter.
One hundred and ten subjects from an upper division, undergraduate psychology course participated in the study. There were 28 men and 82 women. The participants were asked to complete the Beck Depression Inventory (BDI; Beck, 1967), the Expanded Attributional Style Questionnaire (EASQ; Peterson & Seligman, 1984) and an additional questionnaire including the following questions: “What grade do you expect to get on the next exam?”, “What grade would make you happy?” and “What grade would make you unhappy?” The questionnaire period was labelled as Time 1. Following this, the students completed an adjective checklist (Zuckerman & Lubin, 1965). It was used to assess three types of moods, including anxiety, hostility and depression. This assessment took place 12 days after Time 1 and 2 days before the actual examination. Seven days after Time 2, students completed the last step in the study, designated as Time 3. Their actual examination grades were returned along with the final package of questionnaires. The package included the checklist for assessing mood, two forms soliciting the students’ attributions for their examination performance, a questionnaire asking about their future plans to help prepare the next examination and a request asking them to report their actual grade. The study concluded with a final debriefing of the participants.
The materials used in this study include the Expanded Attributional Style Questionnaire, Mood Affect Adjective Check List, Exam attributional measures and the Planned Behaviours Questionnaire.
The EASQ distributed to the students measures attributional style for negative hypothetical events. The participants were asked to imagine themselves in each situation and write down a possible cause for the event. There was an equal distribution of both affiliative and achievement situations. Examples include, “You have been looking for a job unsuccessfully for some time” and “You meet a friend who acts hostilely to you”. The participants were then asked to rate the cause of each situation using a 7-point Likert scale. The first three dimensions are internal-external (ranging from completely due to others to completely due to my own efforts), stable-unstable (ranging from will never be present again to will always be present) and global-specific (ranging from influences only this situation to influences all situations). Peterson and Seligman added a fourth dimension, control-no control that asked subjects of the study to rate the degree of control that they believed they would have in each event. The calculated score of this study was only based on the first three scales.
The Mood Affect Adjective Check List Today Form (MAACL; Zuckerman & Lubin, 1965) is comprised of 132 items that are used to detect the subjects’ moods based on three dimensions: depression, anxiety and hostility. In addition to measuring depressed mood at one point in time, the MAACL was also used to assess the change in participants’ mood over a short period of time in this study.
Students’ attributions for their grade on the examination were measured in two ways. Firstly, participants were given an indirect probe, which requested that they list their thoughts and feelings about their performance on the exam. There were several boxes on the form, in each of which subjects were asked to list one thought or feeling. The participants were told that they did not have to fill in all the boxes. This method of examining attributions allowed for more spontaneous thinking and was potentially less reactive compared to some of the instruments traditionally used in attribution research. For the second part of the exam attributional measures, the subjects were then asked to rate the cause of their examination performance with the Likert ratings that were used in the EASQ. The cause of the event was rated on the four dimensions: internal-external, stable-unstable, global-specific, and control-no control dimensions. For each of the student’s responses to the indirect probe, two trained undergraduate coders rated whether an attributional thought was developed. Statements that explained possible causes for a participant’s examination performance were coded as attributions. Examples include: “The test was deceptively easy,” “My score reflected the fact that I had two midterms and an assignment due on the same day,” and “I should study harder.”
The Planned Behaviours Questionnaire (PBQ) was designed by the authors specifically to use in this study. Participants were asked to give an estimate of the number of hours they spent studying for the examination they had just completed. Following this, they were then asked to estimate the number of hours they intended to spend studying in preparation for the next exam. Finally, the questionnaire was concluded with this final question: “Do you intend to do anything different from what you did to prepare for this exam when studying for any future exams in this class? “Please list anything new that you plan to do in preparation for the next exam” The new behaviours listed were counted to see how many participants chose the same method in order to study for the next exam in this class.
The regression analyses of the study were comprised of several factors. The preexamination MAACL depression score was a covariate that was entered in into the equation first. Next, the degree of stress was added into the equation. This variable was the difference between the score that would make the participant happy and the actual examination grade that they received, based on the traditional 0.0-4.0 grading scale. The greater the discrepancy between the two grades, the higher the stress score the participant received. The third component that was entered into the equation was the composite attributional style variable. This variable was calculated by taking the sum of internal-external, stable-unstable and global specific dimensions for hypothetical situations on the EASQ. The final and most important component entered into the equation was the product vector of the interaction between attributional style and stress level to test the diathesis-stress model of depressed mood. Table 1 and Table 2 can be found with the study here.
Additional results of the study showed that under high stress conditions, the tendency to make internal, stable and global attributions resulted in greater depression. For students that received a grade within close proximity to the grade that would make them happy, their attributional styles did not have an effect on their mood. Because no correlation was found between the attributions made for hypothetical events and real life stressors, a similar correlation was calculated for the study. The results showed that only the attributions made based on real life situations were useful in explaining variability in mood.

## The Four Stages of the Attributional Process

The attributional process is comprised of four main components. One is outcome evaluation, the process of determining whether or not an outcome is favoured. The second is attributional responses, the explanations we attribute to causing the result. The third is affective responses, the emotional responses that follow the interpretation of the outcome and the last is behavioural responses, the course of action that we take to respond to the experience. One main aspect of the attributional process to keep in mind is that specific events do not trigger behavioural reactions directly. These responses only take place after the outcome is cognitively interpreted. All four of these stages can be observed in the previously mentioned study.

### Outcome Evaluation

Outcome evaluation refers to the process by which we determine whether an outcome is desired or not. These evaluations are based on several criteria. One is the individual’s prior history to encountering similar outcomes. An example of this could be a student that consistently excels in math class, but receives an average test score on his final exam. He could interpret this outcome as undesirable. Another aspect of outcome evaluation is performance feedback. A student that falls below a pre-established standard may view his performance as unfavourable. Evaluations of various outcomes are also dependent on the characteristics of the person, such as the need for success, the perceived value of the task and the expectations of others. The final standard for outcome evaluation is based on cues from others. When a student regularly exceeds expectations, submitting an average assignment may be deemed unfavourable by their teacher, while his classmates can turn in work of the same quality and receive praise from the teacher. These four components make up the criteria for outcome evaluation. Using our previous example from above, we can say that both of the students deemed their math test outcome unfavourable, leading them to make their own attributional responses.

### Attributional Responses

The second step of the attributional process is explaining the outcome with a particular cause. Follette and Jacobson's study shows examples of various attributional styles using the hypothetical situations of the EASQ and the exam attributional measures. Examples from the study show attributions based on internal and external sources, stable and unstable conditions and global-specific influences. We can also consider our previous example. Upon seeing her mediocre test mark, the first student attributes her poor performance to her lack of preparedness. The second student responds by putting the blame on the quality of the test written by her teacher. The difference in the two students’ responses can be better explained by the locus of control.

#### Locus of Control

Attributional responses are interpreted in three dimensions. The first dimension is the locus of control, which defines the outcome as being caused by an internal or external source. One example of an internal cause is mood. The performance of a student can be affected by mood, which is controlled by the student himself. An external variable affecting performance may be the student’s parents. This is an example of an external variable because the student’s parents have an effect on his performance but the student himself has no control over the situation. In reference to the study, both internal and external attributions were made about the students' examination scores. One student attributes their score to having two midterms and an assignment due on the same day. This student attributes their failure to an external source rather than considering a lack of preparedness. An example of an internal cause from the study is a student that attributes their below average test score to the minimal effort that they put into studying for the exam.

#### Stability

The second dimension of attributional responses is stability. It is defined by how consistent the factor is when encouraging success. Various aspects of performance such as ability, effort and luck can be ranked in terms of how stable each condition is. The dimension of stability is frequently connected to a person’s expectancy of success. If a student attributes their success to a typically stable variable such as ability, it is highly plausible that past success will occur again. On the other hand, if a student attributes their success to a more random cause such as luck, there is a much smaller chance of seeing repeated success. Participants of the study were asked to rate the cause of an event ranging from never being present again to always being the reason for this situation to occur.

#### Controllability

Controllability is the third and final dimension of the attributional responses. It describes the degree to which the individual can influence the cause behind the outcome. Several factors such as effort and strategy use can be highly controlled whereas ability and interest are considered less controlled. Uncontrollable causes, such as the difficulty of a task and luck do not contribute to an individual’s repeated success. There is a strong connection between the controllability dimension and the amount of effort and persistence an individual puts into completing a task. Outcomes deemed more uncontrollable tend to encourage anxiety and avoidance strategies while more controlled variables can lead to increased effort and persistence.that appear from the matrix can elicit numerous affective and behavioural responses.

### Affective Responses

The various attributional combinations that result from the three dimensions produce different, though highly predictable emotional responses. The locus of control is most commonly linked to the affective response an individual experiences after a specific outcome. Drawing back on our previous example, the first student attributes her poor performance to lack of preparedness, which is an internal cause involving the amount of effort put into the task. This results in the student feeling a sense of disappointment or shame because effort is a controllable factor. With these same conditions, pity is most appropriate to be elicited by others. In contrast, the second student interprets her mediocre grade as being caused by an external factor. She experiences anger because the situation has external, controllable and stable causes. Other combinations of the three dimensions can produce different results. For a student feeling gratitude, it is most likely due to an external, uncontrollable and unstable factor such as an easy test. For all the emotional responses that are elicited, they are followed by a behavioural course of action. Follette and Jacobson's study showed that participants displayed feelings of disappointment following the reveal of their exam scores.

### Behavioural Responses

The understanding of an outcome determines what an individual will do after the situation is interpreted. For attributions in which the locus of control is the prominent dimension, the individual elicits internal feelings of confidence, satisfaction and pride. The behavioural responses resulting from an external locus are help seeking in a positive manner, learned helplessness, avoidance, and lack of persistence when the situation is interpreted negatively. With attributions critically relating to stability, the behavioural response elicited commonly results in higher success expectancies. In turn, the individual develops higher levels of task engagement, seeks out challenges more often and performs to a higher standard. When attributions are more closely linked to controllability, the individual becomes more persistent and puts in a greater amount of effort to complete a task. The two students from our above example display contrasting behavioural responses to their same outcome. The first student vows to put in more work to receive the grade she deserves. In the future, if she succeeds in earning a higher grade on her next math test, she can attribute her success to her increased effort and persistence. In turn she will feel more confident and proud of her abilities. For the second student, her attributions cause her to feel anger due to an external source. Because her interpretation of the event is negative, it is highly predictable that she will develop a sense of learned helplessness, become avoidant towards taking math tests and lack persistence in preparing for test taking. Drawing on the study "Importance of Attributions as a Predictor of How People Cope with Failure," students showed different behavioural responses based on what they attributed their test scores to. Responses from the study included: "I will ask the teacher what I did wrong," "I plan to do the reading earlier in preparation for the next exam," and "I will stay on campus to study with my friends that are also in the class." These are some of the behavioural responses that can occur due to a variety of attributions.

## Emotions

Emotion is a state of feelings. It represents human reactions and responses to the stimuli. It can foster humans well-being, or can contribute to psychological and physical function. There are two main types of emotions that can be classified: positive emotions and negative emotions. Positive emotions can include happiness, compassion, gratitude, hope, interest, enjoyment, joy, love and pride.Whereas negative emotions can include anger, fear, disgust, sadness, anxiety, shame, hopelessness and boredom. These two emotions both consist of a pattern of cognitive, physiological and behavioural reactions to events that have relevance to important goals such as learning. In order to understand the reason why people respond to learning differently, we could look at the impact of emotions. There are four types of components: attribution response, emotion, learning and achievement. We will first look at how emotion is a response to learning, and vice versa. Different learning patterns, styles and outcomes that people are attributing will represent different emotions. Also, different emotions will impact different academic achievements.
| Positive Emotions | Negative Emotions |
| happiness | sadness |
| joy | fear |
| gratitude | disgust |
| hope | hopelessness |
| interest | anxiety |
| enjoyment | boredom |
| pride | anger |

### Attribution and Learning about Emotions
#### Learning and Emotion

In the Learning Theory, it states that effective learning is depending on emotional responses. In different learning environment and situations, it will trigger different emotions in learning. Individuals differ in emotional responses to situations. When the learners are feeling comfortable and in control with the learning environment, learners will have a better performance. It is because the learners would adapt the environment when they are learning. They would feel comfortable and help increase the learning process. In contrast, if learners are feeling uncomfortable and not in control of the environment, the learner will not perform as well. It is because the learners can not adapt the learning environment while learning, which negatively affects the learning process. Therefore, they may perform worse. In the learning environment, it is necessary to have certain emotions present: Learners must be able to control and overcome negative emotions like fear, anxiety and sadness. Therefore, positive emotions such as the sense of accomplishment and enthusiasm can be increased. It is because negative emotions are negatively affecting the learning and positive emotions are positively affecting the learning. This mean that, positive emotions are more likely to achieve higher academic performance while negative emotions are more likely to achieve lower academic performance.
In a study of The relations between students' approaches to learning, experienced emotions and outcomes if learning, it stated that there is a relationship between the emotions and academic performance in students experience. The sample of this study was studying the first year biology course in University of Sydney. They are all age 18 to 25 years. The participants took The Revised Study Process Questionnaire to self-report their learning strategy and learning motives. The researchers linked emotions with intrinsic and extrinsic motivation, which are associated to learning performance. The study showed that students with anger and boredom avoided engaging in learning the resulting learning outcome. Also, students with anxiety and shame reduced their intrinsic motivation in learning activities that lower their academic achievement. Students who were angry and frustrated were less likely to adopt strategies in learning and have a more negative learning outcome. In contrast, all students with positive emotions engaged in learning, being motivation during activities and adopted strategies in learning, which have a more positive learning outcome. This means that, motivation and self-efficacy are also related the students' emotions in learning. The following table shows different emotions affect learning patterns and styles and results different learning outcomes in the study.
| Emotions | Learning patterns and styles | Learning outcomes |
| Pride, hope, confidence, enjoyment, optimistic and proud | prepare the assessment for the course contribute course materials make sure everything is going well for the course feeling pride and confident of the result follow the progress in the course | increase academic performance |
| Frustration, anger and boredom | feeling bored of the course get angry with the course get annoyed when trying the learning activities for the course | decrease academic performance |
| Anxiety and shame | ashamed thinking for the assessment become panicky about the course feeling embarrassed for not contributing to learning activities ashamed of not preparing for course do not contribute to class discussion do not ask question during class | decrease academic performance |

#### Attribution and Emotion

Rainer Reisenzein, a psychologist in University of Greifswald, who interests in computational belief-desire theory of emotion. He focuses research on theoretical and empirical questions related to emotion and motivation by interdisciplinary orientation toward philosophy and cognitive science. In one of his attributional approach studies, he addresses that our belief is based on the causes of the events that determine emotion and behaviors. He also states that the attributional theory of emotion provides a cognitive analysis for the cause of emotions. The appraisal dimensions related to causal attribution is also generally the appraisal theory of emotion. Different from other cognitive appraisal theories, the attributional theory of emotion provides not just the analysis of the cognitive causes of emotions, but also emphasizes the effects of the emotions, especially focusing on the functional effects in emotions. There are two effects in emotions. First, the motivational emotion effect. It means that emotions evoke people’s action tendencies to the situation as appraised. Second, the communicative emotion effect. It means the emotions provide information about people's experiences in situation appraisals and action tendencies to the environment. It can show that attribution is related to emotions. Moreover, the impact of attributions and emotions are connected in learning behavior, which in turn, influences subsequent academic achievement. Self-control is one of the characteristic in attribution. Individual differences in self-control associates different self-regulatory abilities. It is defined as the capacity to modify one’s internal responses of impulses, emotions, thought and behaviors. The conceptualization of self-control guides individuals towards goals and standards. This mean that, self-control can alter learners to achieve their desired goals. In King et al’s 2014 study, it investigated how self-control is related to students' experience of academic emotions by taking individual differences for the examination. It found that self-control is positively predicts positive academic emotions. Having higher self-control can predict more positive emotions, with better engagement and higher achievement in school. In the Control-Value Theory, control and values-related appraisals are the predictors of achievement emotions. When learners have a high control-related appraisal and high value-related appraisal, they will be more likely to experience positive academic emotions. When learner has a low control-related appraisal and low value-related appraisal, they will be more likely to experience negative academic emotions. . Figure 1 shows the basic propositions of the control-value theory.
Furthermore, self control has proved that it can be a negative predictor of behavioral and emotional disaffection. It can inhibit learners to display disengaged behaviors and emotions. This means that self-control had a direct affect on academic achievement, which will be discussed later.

### Emotions and Attributional Responses

Different attributions in individual can predict emotions. A study from Follette and Jacobson shows that different learning styles and patterns that attribute to examination could predict emotion reactions. They examined that the causal attributions were predictive of depressed mood in college students who experienced the negative event. They found that internal, global and stable attributional responses have a tendency toward depression. In order to understand how emotions and attributional responses are related, individuals need to understand more about their own self.
In 2006, Bar-On addressed that understanding of yourself and others, relating well to people and adapting to attribute with the immediate surroundings will help you to be more successful in dealing with environmental demands. Adapting attribution associates to our emotional intelligence (EI). Emotional Intelligence is an ability to monitor one's own and other people's emotions. It can discriminate different emotions and label them appropriately and to use the emotional information to guide thinking and behavior. There are three components that contribute to EI: persistent effort, locus of control and self-efficacy. If learners are high in these three components, they will have a high EI and they will more likely to be successful. In contrast, if learners are low in those three components, they will have a low EI and they will more likely to have failure and emotion problem. To maintain and develop a high EI, learners can focus on their stress management, which is emotional management and regulation. There are two elements in stress management: stress tolerance and impulse control. Learners need to manage and control emotions effectively and constructively to achieve the stress management.
Attributions vary along three dimensions: locus of control, stability and controllability. Each dimension is related with a type of affective response. Different combinations of the dimension will have different emotional reactions. This means that, different combinations in attributions dimensions will result different emotions. In Weiner's attribution theory, the three dimensions shows different emotion results. For example, internal, controllable and stable factors will experience pride and confidence; external, uncontrollable and unstable factors will experience gratitude; external, controllable and stable will cause anger; and internal, uncontrollable and stable will cause a feeling of shame. As different attributional responses will cause different emotions, in turn, it is affecting the academic achievement as well. The following table shows attributional dimension emotions.

### Emotion and Academic Achievement

Emotion and psychological state can determine learning productivity. Higher learning productivity will more likely to have a more positive emotion. As positive emotions can stimulate self-motivation, it is saying that learner’s self-control would be stimulated as well. Learners that have a higher self-control are more successful in school because it is also relating how learners feel in school, and which of the emotions are affecting school activities. In addition, to study the relationship between emotions and academic achievement, academic emotions are involved. Academic emotions are identified in enjoyment, hope, pride, anger, anxiety, shame, boredom and hopelessness. Different emotions can be classified into different valence and activation circumstances. Positive-activating emotions are enjoyment, hope and pride; the positive-deactivating emotion is relief; negative-activating emotions are anger, hope and pride; and negative-deactivating emotions are hopelessness and boredom.
Emotions can also facilitate academic engagement, which in turn, influences subsequent academic achievement. Positive emotions are more likely to increase learning engagement, which is positively to achieve a higher academic grade. In contrast, negative emotions are more likely to be disengaged from schooling process, which is negatively to receive a lower academic grade. Learners who passively withdraw and feel boredom and anxiety in school will increase disaffection. Therefore, they will be more likely to experience low academic achievement. Emotions and academic achievement have a direct relation. Reason why learners who experience low school outcome are because their negative emotions promote withdrawal and disengagement in school. As learners who experience positive emotions will engage in their studies, which is beneficial to their academic career. However, there are exceptions too. Emotions and academic achievement can be affected inversely.
King et al.’s study examined the possibility that positive emotions lower academic achievement. There is a diminishing return on emotions and achievement. When the learner’s positive emotions achieved to the optimal level of academic score, his or her academic achievement will return to the marginal. However, differences are individual as different people experiences different circumstances. Moreover, a study found that students in China who dispose negative emotions such as anger would increase their grade point average (GPA). Yet, there are no relation between anger and GPA.Furthermore, lacking school attention has shown that positive emotions would increase. However, the experimenter explained that positive emotions are difficult to recognize. Even though experiments can be recorded in a digital way, many positive emotions share a similar facial expression. There are no significant differences that can be recognize in positive emotions, as a result, the outcome might not be accurate. Also, many studies stated that positive emotions usually appear after a solving problem task, which people are less likely to be aware of. Negative emotions are generally to be viewed as more troublesome in children’s development and functioning. This is saying that, negative emotions are more likely to have investigating attention.
In conclusion, emotion is associated with academic competences. Individual differences in emotions are engaging into different attribution styles. Self-control, self-motivation, engagement, locus of control and stability are affecting learners and which behaviors they present. Positive emotions are more likely to increase academic achievement, while negative emotions are less likely to decrease academic achievement. Emotions are related to academic success because it contains a useful information to guide and predict cognition and actions. In addition, to help low academic achievement learners to improve their learning, educators should encourage students to minimize the experiences of negative emotions. Students should engage in positive thinking to attribute for their academic styles. Furthermore, student can seek help from family and professionals. To discuss more about how students attribute learning and emotions, a classroom setting can be looked at.

## Attributions and Emotions in the Classroom

Students all bring different emotions and attributions with them into the classroom. Although many of these students may bring in positive attributions, equally as many students may carry negative attributions with them into their academic lives. The teacher plays an essential role in helping students figure out their emotions at school, why they feel them, and how they could possibly improve. By helping students learn about their emotions in the classroom, the students are better able to focus on how emotions and what other extraneous factors may affect how they learn. Once students understand how their emotions affect their learning, they are better able to create a learning environment and figure out which strategies for dealing with their emotions work best. It is important that teachers show students’ how emotions affect how they attribute both positive and negative situations and to learn about unfavorable behaviors and attributions early on so that they are better able to learn to avoid them throughout their academic career.

### Attributional Retraining

One of the main ways teachers can help students improve their mindset is by attributional retraining which is helping students get a better understanding of their attributional responses and how to change their response so that they are more encouraged to stay focused. The main focus of attributional retraining is to shift student’s focus from their ability shown to the effort put forth in the classroom. By doing this it emphasizes to students that their performance and success or failures in class are due to controllable factors such as their effort. Whereas if students attributed their successes and failures on something uncontrollable such as their ability, they would quickly become discouraged after receiving negative feedback or a low score as many students attribute one’s ability with self-efficacy. As a result, attributional retraining could help assist students in motivation, task persistence and achievement levels. There are many ways that teachers can help students understand their attributions. One of the main ways this can be done is simply by reminding the students that their scores are not attributed to their ability. School is becoming increasingly competitive and many students are focused on the marks that they are receiving. By constantly reminding students that any low mark they are receiving is attributed to their effort in the classroom, it may encourage them to try harder during their next assignment.
There are four main steps to attributional retraining. The first step is getting individuals to identify undesirable behaviors that they may have. These behaviors could include things like task avoidance. Being able to identify these behaviors early allows these behaviors to be easily evaluated and changed. It is important for both the student and the teacher to work together on identifying these behaviors early on. By not identifying these problems early, students may lose learning opportunities that could be easily fixed. The second step is evaluating the underlying negative behavior. This could be evaluating how serious the situation and behavior may be and what could be causing the student to have such behaviors. Generally these could be due to internal factors, which require immediate attention or could be caused by extraneous factors that may be hindering that student’s performance at that moment. The third step is considering how to change the student’s attributional response. It is important to figure out what is best for the student and what kind of attributions could take its place. By implementing the wrong new attribution, it could potentially hinder the student’s performance further. Depending on the student, finding a new attribution could be a difficult task or it may be very clear. Every student is different. And the last step is implementing the new attribution, which must be done by finding the most suitable way to implement the new attribution. It is not beneficial for students to implement the new attribution if it does not work well with their learning style. Students and teachers must work collaboratively to ensure that the new attributions being implemented are what is most suitable for the student.

### Understanding Our Own Emotional Reactions

Teachers should be wary of how their students perceive success and failure and which ones make negative attributions after experiencing failure. Showing negative emotion is normal, however some emotions can be perceived as more harmful than others. It is important for the teacher to educate and remind students that learning how to redirect their attributional thinking can change their emotions. However it is equally as important to teach students about emotional intelligence, which is learning to understand one and others emotions, relating to people, and learning to deal with environmental demands by adapting to the new surroundings. By teaching emotional intelligence, students and teachers are better able to understand their emotions in the classroom and why they feel them in different situations. Students would also learn to control their emotions during both success and failures in and around the classroom. It is important to emphasize positive emotions as it has been seen to have more positive effects on students. The broaden-and-build theory states that positive emotions can help expand a student’s engagement in activities as well as encourage students to delve deeper into learning materials and expand their focus whereas negative emotions narrow the focus of students and do not allow for optimal learning. Having positive emotions towards learning provides a better learning environment for students, which may allow for more positive attributional thinking when feedback is given. Although it is important to emphasize positive emotions, it is also important to remind students that it is okay to feel negative emotions as well. Negative emotions are a regular occurrence in the classroom and should not be discouraged. All students handle situations differently and showing negative emotion might be a way for the student to cope with a situation that they are not used to. As educators, it is important to figure out with the student why they may be feeling this negative emotion and how to best handle it.

## Implications for Instruction
### Effects on Students

One of the most important things educators can do is begin discussing attributions and their effects from an early age. It is an integral part of the classroom and is something that should be focused on. By explaining to students the subtle differences between attributing something to ability rather than lack of effort, you remind them that knowledge is not innate and is something that can be learned. This is especially important when students are first beginning school so that it builds a strong foundation for them as they progress through the grades. It should also be reminded to students throughout the school year as students can often become discouraged when they find tasks difficult or receive unfavorable marks. Since school is becoming increasingly competitive in terms of admission standards to post secondary institutions, it is important to remind students constantly that although grades are important, they are not tied to a low mark that they may receive.
Commonly, students may find that they experience difficulty in the classroom, which is due to many controllable factors. These factors may include a lack of prior knowledge, and automaticity. It is important to remind these students that the difficulty they experience is due to extraneous factors and not themselves so that they do not become discouraged when learning new material or understanding new concepts. By creating a student-centered approach in the classroom, we are creating a learning environment where personal growth and change are prioritized. This kind of approach allows the students to be less frustrated when they do not understand a concept right away or when they receive negative feedback. The emphasis of this approach is that knowledge can always be learned and is not dependent on your innate ability or prior knowledge. By approaching learning in this kind of way, it is teaching and instilling in students to be persistent and to keep trying even if it takes them longer to understand concepts or they do not succeed the first time around. Students may also seek help if they believe that what is holding them back is an environmental factor rather than a personal one. This is because they do not hold their difficulties personally but rather believe other things cause them. Whereas many students may not seek assistance in class if they are struggling because they do not want to be perceived as incompetent in the eyes of their peers or their professor. It is important to instill early on in students that the difficulties they face are due to controllable factors.
There are many extraneous factors that could be affecting student performance. Students may be struggling in class for many different reasons. One of the main reasons that students could be struggling is by not knowing how to best apply appropriate strategies that maximize their learning potential. As educators it is important to try to help students learn what methods work best for them in acquiring new information. Another reason that students could be struggling is lack of prior knowledge. If students are unable to best apply learning strategies and it is not noticed by an educator, students may fall behind and not have the appropriate prior knowledge to learn new concepts. It is important as an educator to remember that these extraneous factors are controllable causes which may be hindering the student’s ability to reach their fullest potential. Reminding the students that these things can be changed as well is important so that the students may not become discouraged for something that can be fixed. Monitoring and discussing with students regularly what may be affecting their performance is important as it allows the teacher to have a better understanding of how the student is doing and how it can be bettered.
When teachers are providing feedback to students, it is important to be mindful of how it is given. Students who have a lower self-esteem may benefit from feedback that is given privately rather than in front of the class. It may also be beneficial when directing praise in front of the class as it may cause provide low-ability cues to students unintentionally. One way to effectively provide feedback is to provide information-oriented feedback rather than performance-oriented feedback. Information-oriented feedback emphasizes how a student’s performance can improve where as performance-oriented feedback emphasizes how a student is progressing in relation to their peers. If students are provided feedback in relation to the other students, they may attribute their lower score to their ability and become discouraged in class, as they may not be progressing as quickly as some of the other students. As educators it is important to try to keep students from comparing themselves to each other as students will be discouraged and feel negatively about school. But by providing feedback basked solely on the students’ progress, it allows for personal growth rather than comparison to others, which is more beneficial for students with low self-esteem. This also teaches students that education is about personal progress and knowledge acquisition rather than comparing themselves to other students. The lack of comparison may keep students motivated to continue pursuing new knowledge.
According to the control value theory, emotions are directly related achievement, cognitive, motivational processes. Generally positive emotions are correlated with an increase in students’ motivation while negative emotions reduce students’ motivation. It is important that students use these positive emotions to attempt to become intrinsically motivated in school. When students are intrinsically motivated, they are more likely to persist when they encounter difficult problems or concepts in their learning. Teachers are a large part of helping students develop these behaviors. It is important that teachers create a learning environment that sets a positive example for the student. Students are greatly influenced by the teacher and the environment of the classroom. By creating a positive learning environment, students may feel more inclined to be positive about their learning. The teacher student relationship is also one of the most important things that can help students academically. By having positive, nurturing and supportive teachers, students are able to develop self-confidence and a sense of self-determination, which will in turn affect their learning behaviors. Once students are intrinsically motivated to do well in school, they will be more likely to create positive attributions between themselves and what they are learning.
However it is important to remember that all students begin with different attributions and ways to deal with them and they learn and process information differently. Techniques used in helping students change their attributions and learn to control their emotions vary greatly between students. As with all techniques, it is important for the teacher and student to work collaboratively in finding out what works best for that individual. One of the ways that this can be done is by discussing with the student different learning strategies that may work best for them and having the teacher monitor the student in class to see if it is effective. This can also be done through trial and error of different techniques until one is found to be most effective for that student or group of students. Once an effective method is found, it can be implemented not only in academic situations, but also in all aspects of a student’s life. By learning what methods works best and really understanding the student, it creates an easier learning environment that is more beneficial for everyone involved.The most important aspect is merely teaching students about their attributions and how it affects them in the classroom. Learning how to affectively attribute their successes and failures will help to further their academic career. Even though it may take some time to fully understand their attributions, the mere knowledge of it will help students to become aware of why they may feel a certain way in class. It definitely will take time for students to fully learn what methods work best for them but by teaching them about their attributions early, they are better able to carry this knowledge with them throughout their academic career.

## Suggested Readings

* Zahed-Babelan, A., & Moenikia, M. (2010). The role of emotional intelligence in predicting students’ academic achievement in distance education system. Procedia - Social and Behavioral Sciences, 2, 1158-1163.
* Valiente, C., Swanson, J., & Eisenberg, N. (2011). Linking Students’ Emotions and Academic Achievement: When and Why Emotions Matter. Child Development Perspectives, 6(2), 129-135.
* King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* Follette, V. M., & Jacobson, N. S. (1987). Importance of attributions as a predictor of how people cope with failure. Journal Of Personality And Social Psychology, 52(6), 1205-1211. doi:10.1037/0022-3514.52.6.1205
* Trigwell, K., Ellis, R., & Han, F. (2011). The relations between students' approaches to leaning, experienced emotions and outcomes if learning' Studies in Higher Education, 37(7), 811-824.
* Matuliauskaite, A., & Zemeckyte, L. (2011). Analysis of interdependencies between students’ emotions, learning productivity, academic achievements and physiological parameters. Science - Future of Lithuania, 3(2), 51-56.
* Naude, L. n., Bergh, T., & Kruger, I. (2014). 'Learning to like learning': an appreciative inquiry into emotions in education. Social Psychology Of Education, 17(2), 211-228.

## Glossary

* Affective responses: the emotional responses that follow the interpretation of the outcome
* Behavioural responses: the course of action is taken to respond to the experience
* Controllability: the degree to which a factor can be influenced
* Attribution: explanation to describe the cause behind an event
* Attributional responses: the explanations attributed to causing a specific result
* Attribution theory: the study of how people interpret various events
* Locus of control: defines the outcome as being caused by an internal or external source
* Outcome evaluation: the process by which an outcome is considered a success or a failure
* Stability: how consistent the factor is in encouraging success
* Learning theory: a conceptual frameworks on how information is absorbed, processed, and retained during learning
* Control- value theory: relationship between level of controllability and value and the achievement in emotions
* Achievement emotions: the mental state of feeling that attribute to achievement
* Emotion Intelligence: ability to identify, use, understand, and manage emotions in positive ways to relieve stress, to communicate effectively and to overcome challenges
* Diminishing Return: a decreasing effect on a product that passing to marginal level after the optimal point
* Attributional Retraining: helping students better understand their attributional responses
* Information-oriented Feedback: feedback regarding how an individual student's performance can be improved
* Performance-oriented Feedback: feedback regarding how a student is progressing in comparison to their peers

## References

* 1 2 3 4 Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA:Pearson
* 1 2 3 4 5 6 7 8 9 10 11 Zimmerman, B. J. (2002). Becoming a self-regulated learner: an overview. Theory Into Practice.41(2):64–70. doi:10.1207/s15430421tip4102\2.
* ↑ Purdie, N., & Hattie, J. (1996). Cultural Differences in the Use of Strategies for Self-regulated Learning. American Educational Research Journal 33.(4): 845–871.
* 1 2 3 4 5 6 7 8 9 García, F. C., García, Á., Berbén, A. G., Pichardo, M. C., & Justicia, F. (2014). The effects of question-generation training on metacognitive knowledge, self regulation and learning approaches in Science. Psicothema, 26(3), 385-390.
* ↑ Winne, P. H. & Baker, R. J. D. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. Journal of Educational Data Mining, 5(1).
* ↑ Zumbrunn, S., Tadlock, J., & Roberts, E. D. (2011). Encouraging self-regulated learning in the classroom: A review of the literature. Metropolitan Educational Research Consortium (MERC).
* 1 2 Furnes, B., & Norman, E. (2015). Metacognition and reading: Comparing three forms of metacognition in normally developing readers and readers with dyslexia. Dyslexia: An International Journal Of Research And Practice, 21(3), 273-284
* ↑ Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906-911.
* 1 2 3 4 5 6 7 Efklides, A. (2008). Metacognition: Defining its facets and levels of functioning in relation to self-regulation and co-regulation. European Psychologist, 13(4), 277-287.
* ↑ Jacobs, J.E., & Paris, S.G. (1987). Children's metacognition about reading: Issues in definition, measurement, and instruction.Educational Psychologist 22: 225–278.
* ↑ Schraw, G. (1998). Promoting general metacognitive awareness. Instructional Science 26: 113–125.doi:10.1023/A:1003044231033.
* ↑ Son, L.K., & Metcalfe, J. (2005). Judgments of learning: Evidence for a two-stage process. Memory & Cognition, 33(6), 1116-1129.
* ↑ Nelson, T. O., & Dunlosky, J.. (1991). When People’s Judgments of Learning (JOLs) Are Extremely Accurate at Predicting Subsequent Recall: The “Delayed-JOL Effect”. Psychological Science, 2(4), 267–270. .
* ↑ Bembenutty, H. (2009). Feeling-of-Knowing Judgment and Self-Regulation of Learning. Education, 129(4), 589-598.
* 1 2 3 Kolić-Vehovec, S., Zubković, B. R., & Pahljina-Reinić, R. (2014). Development of metacognitive knowledge of reading strategies and attitudes toward reading in early adolescence: The effect on reading comprehension. Psychological Topics, 23(1), 77–98.
* ↑ Siadaty, M., Gašević, D., & Hatala, M. (2016). Trace-based micro-analytic measurement of self-regulated learning processes. Journal of Learning Analytics, 3(1), p. 183-220.
* 1 2 Butcher, K. R. (2006). Learning from text with diagrams: Promoting mental model development and inference generation. Journal of Educational Psychology, 98(1), 182-197. DOI: 10.1037/0022-0663.98.1.182
* 1 2 3 Chi, M. T. H. (2000). Self-explaining expository texts: The dual process of generating inferences and repairing mental models. In R. Glaser (Ed.), Advances in instructional psychology (pp. 161–238). Mahwah, NJ: Lawrence Erlbaum.
* 1 2 3 4 5 6 7 Bisra, K., Liu, Q., Salimi, F., Nesbit, J.C. & Winne, Ph, H. Inducing self-explanation: A meta-analysis. Unpublished manuscript, Simon Fraser University, Burnaby, BC.
* 1 2 3 4 5 6 7 8 9 10 11 Wylie, R., & Chi, M. T. H. (2014). The Self-explanation principle in multimedia learning. In R. E. Mayer (Ed.), The Cambridge handbook of multimedia learning (2nd ed., pp. 413e432). New York, NY: Cambridge University Press
* ↑ VanLehn, K., Jones, R. M., & Chi, M. T. (1992). A model of self-explanation effect. Journal of the Learning Sciences, 2(1), 1-59. doi:10.1207/s15327809jls0201_1.
* 1 2 3 4 5 6 7 8 9 10 11 12 13 Ionas, I. G., Cernusca, D., & Collier, H. L. (2012). Prior knowledge influence on self-explanation effectiveness when solving problems: An exploratory study in science learning. International Journal of Teaching and Learning in Higher Education, 24(3), 349-358.
* ↑ Yeh, F., Y, Chen, C., M, Hung, H., P., & Hwang, J., G. (2010). Optimal self-explanation prompt design in dynamic multi-representational learning environments. Journal computers and education 54, 1089–1100.
* ↑ Atkinson, R. K.,Renkl , A. , & Merrill , M. M. (2003). Transitioning from studying examples to solving problems: Effects of self-explanation prompts and fading worked-out steps.Journal of Educational Psychology, 95(4), 774–783.
* ↑ Van der Meij , J. , & de Jong , T. (2011). The effects of directive self-explanation prompts to support active processing of multiple representations in a simulation-based learning environment. Journal of Computer Assisted Learning, 27(5), 411–423.
* 1 2 3 4 5 6 7 Schraw, G., Crippen, K. J., & Hartley, K. (2006). Promoting Self-Regulation in Science Education: Metacognition as Part of a Broader Perspective on Learning. Research In Science Education, 36(1-2), 111-139.
* 1 2 3 4 Zimmerman, B. J., & Campillo, M. (2003). Motivating self-regulated problem solvers. The psychology of problem solving, 233-262.
* ↑ Kuhl, J. (1985). Volitional mediators of cognitive behavior consistency: Self-regulatory processes and action versus state orientation. In J. Kuhl & J. Beckman (Eds.), Action control (pp.101–128). New York: Springer.
* ↑ Bruning, R. H., Schraw, G. J., & Ronning, R. R. (1995). Cognitive psychology and instruction (2nd ed.). Upper Saddle River, NJ: Merrill.
* ↑ Zimmerman, B. J., & Paulsen, A. S. (1995). Self-monitoring during collegiate studying: An invaluable tool for academic self-regulation. In P. Pintrich (Ed.), New directions in college teaching and learning: Understanding self-regulated learning (No.63, Fall, pp.13–27). San Francisco, CA: Jossey-Bass, Inc.
* ↑ Zimmerman, B. J., & Kitsantas, A. (1996). Self-regulated learning of a motoric skill: The role of goal setting and self-monitoring. Journal of Applied Sport Psychology, 8, 60–75.
* ↑ Bandura, A. (1991). Self-regulation of motivation through anticipatory and self-reactive mechanisms. In R. A. Dienstbier (Ed.), Perspectives on motivation: Nebraska symposium on motivation (Vol. 38, pp.69–164). Lincoln: University of Nebraska Press.
* ↑ Schunk, D.H. (2001). Social cognitive theory and self-regulated learning. In B.J. Zimmerman & D.H Schunk (Eds.), Self-regulated learning and academic achievement: Theoretical perspectives (2nd ed., pp. 125-152). Mahwah, NJ: Erlbaum.
* ↑ Schunk, D. H. (1983 c). Progress self-monitoring: Effects on children’s self-efficacy and achievement.Journal of Experimental Education, 51, 89–93.
* 1 2 Purdie, Nola, and John Hattie.(1996). “Cultural Differences in the Use of Strategies for Self-regulated Learning”. American Educational Research Journal 33.4 (1996): 845–871.
* ↑ Winne, P. H. (1996). A metacognitive view of individual differences in self-regulated learning. Learning and Individual Differences, 8(4), 327-353.
* ↑ Kelemen, W. L., Frost, P. J., & Weaver, C. A. (2000). Individual differences in metacognition: Evidence against a general metacognitive ability. Memory & Cognition, 28(1), 92-107.
* 1 2 3 Lin, X., Schwartz, D. L., & Hatano, G. (2005). Toward Teachers' Adaptive Metacognition. Educational Psychologist, 40(4), 245-255.
* 1 2 3 4 Siemens, G. (2013). Learning analytics: The emergence of a discipline. American Behavioral Scientist, 57 (10), p. 1380 - 1400.
* ↑ Solar Society of Learning Analytics Research. (n.d.).Retrieved from http://solaresearch.org
* ↑ Larusson, J. A., & White, B. (2014). Learning Analytics. Springer
* ↑ Martin, T., & Sherin, B. (2013). Learning analytics and computational techniques for detecting and evaluating patterns in learning: An introduction to the special issue. Journal of the Learning Sciences, 22(4), p. 511-520.
* ↑ Koedinger, K. R., Baker, R. S., Cunningham, K., Skogsholm, A., Leber, B., & Stamper, J. (2010). A data repository for the EDM community: The PSLC DataShop. In Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. (Eds.). (2010) Handbook of educational data mining. CRC Press. p. 43-56.
* ↑ Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining, 1(1), p. 3-17.
* ↑ Dekker, G., Pechenizkiy, M., & Vleeshouwers, J. (2009, July). Predicting students drop out: A case study. In Proceedings of the 2nd International Conference on Educational Data Mining, EDM 2009, p. 41-50.
* ↑ Ming, N., & Ming, V. (2012). Predicting student outcomes from unstructured data. In Proceedings of UMAP Workshops, p. 11-16.
* ↑ Baker, R. S., Corbett, A. T., & Koedinger, K. R. (2004, August). Detecting student misuse of intelligent tutoring systems. In International Conference on Intelligent Tutoring Systems, p. 531-540.
* ↑ Amershi, S., & Conati, C. (2009). Combining Unsupervised and Supervised Classification to Build User Models for Exploratory. JEDM-Journal of Educational Data Mining, 1(1), p. 18-71.
* ↑ Dawson, S. (2008). A study of the relationship between student social networks and sense of community. Educational Technology & Society, 11(3), p. 224-238.
* ↑ Baker, R. S., de Carvalho, A. M. J. A., Raspat, J., Aleven, V., Corbett, A. T., & Koedinger, K. R. (2009, June). Educational software features that encourage and discourage “gaming the system”. In Proceedings of the 14th international conference on artificial intelligence in education, p. 475-482.
* ↑ Perera, D., Kay, J., Koprinska, I., Yacef, K., & Zaïane, O. R. (2009). Clustering and sequential pattern mining of online collaborative learning data. IEEE Transactions on Knowledge and Data Engineering, 21(6), p. 759-772.
* ↑ Bowers, A. J. (2010). Analyzing the longitudinal K-12 grading histories of entire cohorts of students: Grades, data driven decision making, dropping out and hierarchical cluster analysis. Practical Assessment Research and Evaluation, 15(7), p. 1-18.
* 1 2 3 4 Winters, F. I., Greene, J. A., & Costich, C. M. (2008). Self-regulation of learning within computer-based learning environments: A critical analysis. Educational Psychology Review, 20(4), 429-444.
* ↑ Siadaty, M., Gašević, D., & Hatala, M. (2016). Trace-based micro-analytic measurement of self-regulated learning processes. Journal of Learning Analytics, 3(1), p. 183-220.
* ↑ Sha, L., Looi, C. K., Chen, W., Seow, P., & Wong, L. H. (2012). Recognizing and measuring self-regulated learning in a mobile learning environment. Computers in Human Behavior, 28(2), p. 718-728.
* ↑ Cutumisu, M., Blair, K. P., Chin, D. B., & Schwartz, D. L. (2015). Posterlet: A game-based assessment of children’s choices to seek feedback and to revise. Journal of Learning Analytics, 2(1), p. 49-71.
* ↑ Azevedo, R., Harley, J., Trevors, G., Duffy, M., Feyzi-Behnagh, R., Bouchet, F., & Landis, R. (2013). Using trace data to examine the complex roles of cognitive, metacognitive, and emotional self-regulatory processes during learning with multi-agent systems. In International handbook of metacognition and learning technologies, Springer New York, p. 427-449.
* ↑ Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2015). Using coherence analysis to characterize self-regulated learning behaviours in open-ended learning environments. Journal of Learning Analytics, 2(1), p. 13-48.
* 1 2 3 4 Parrott, H. M., & Cherry, E. (2015). Process Memos: Facilitating Dialogues about Writing between Students and Instructors. Teaching Sociology, 43(2), 146-153.
* ↑ Abd-El-Khalick, F., Bell, R. L. and Lederman, N. G. (1998). The nature of science and instructional practice: Making the unnatural natural. Sci. Ed., 82: 417–436.
* ↑ Rosman, Tom; Mayer, Anne-Kathrin; Krampen, Günter. (2015). Combining Self-Assessments and Achievement Tests In Information Literacy Assessment: Empirical Results And Recommendations For Practice. Assessment & Evaluation in Higher Education, Vol 40(5), pp. 740-754.
* 1 2 Paris, S.G. & Paris, A.H. (2001). Classroom applications of research on self-regulated learning. Educational Psychologist, 36 (2), 89-101.
* ↑ Leutner,D., Leopοld, C., & Elzen-Rump, V.D. (2007). Self-regulated learning with a text-highlighting strategyːA training experiment. Journal of Psychology, 215 (3), 174-182.
* ↑ Beishuizen, J. (2007). Self-regulated Learning in Technology Enhanced Learning Environments – a European review. In R. Carneiro, P. Lefrere, K. Steffens. (Eds). Kaleidoscope Seed Project, Retrieved from http://www.lmi.ub.es/ taconet/documents/srlinteles3.pdf.
* 1 2 3 4 5 6 Simao, A.M.V., Duarte, F.C., &Ferreira, P.C. (2008). Self- regulated Learning in Technology Enhanced Environments: Perspectives and Practice. Education – Line: European Conference on Educational Research, University of Goteborg.
* 1 2 Prensky, M. (2001, September). Digital natives, digital immigrants part 1. On the Horizon 9(5).
* ↑ Kitsantas, A. (2013). Fostering college students’ self-regualted learning with learning technologies. Hellenic Journal of Psychology, 10(2013), pp. 235 – 252.
* 1 2 3 4 Carneiro, R., Lefrere,P., Steffens, K., &Underwood, J. (2011). Self-regulated learning in technology enhanced learning environments A European perspective. Sense Publishers Rotterdam/ Boston/ Taiper.
* 1 2 Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901-918.
* 1 2 3 4 Nussbaumer, A., Dahn, I., Kroop, S.,Mikroyannidis, A.,& Albert, D. (2015 a). Support self-regulated learning. S. Kroop et al. (eds.), Responsive Open Learning Environments, pp. 17- 49. DOI 10.1007/978-3-319-02399-1_2.
* 1 2 3 4 Nussbaumer, A., Hillemann, E., Gütl, C., & Albert, D. (2015 b). A competence-based service for supporting self-regulated learning in virtual environments. Journal of Learning Analytics, 2(1), 101–133.
* 1 2 3 4 Kauffman, D.F., Zhao, R., & Yang, Y. (2011). Effects of online note taking formats and self-monitoring prompts on learning from online text: Using technology to enhance self-regulated learning. Contemporary Educational Psychology 36 (2011) 313–322.
* ↑ Azevedo Roger. (2005). Using Hypermedia as a Metacognitive tool for Enhancing Student Learning? The role of Self-Regulated Learning. Educational Psychologist, 40(4), 199-209. DOI: 10.1207/s15326985ep4004_2.
* 1 2 3 4 5 6 Kitsantas, A., Dabbagh, N., Hiller, S., &Mandell, B.(2015). Learning Technologies as supportive contexts for promoting college student self-regulated learning. In Cleary, Timothy (Ed), (2015). Self-regulated learning interventions with at-risk youth: Enhancing adaptability, performance, and well-being. School psychology series., (pp. 277-294). Washington, DC, US: American Psychological Association, xv, 305 pp. http://dx.doi.org/10.1037/14641-013.
* 1 2 Leelawong, K. & Biswas,G. (2008). Designing Learning by Teaching Agents: The Betty’s Brain System.Retrieved from http://www.ijaied.org/pub/997/file/997_Biswas08.pdf
* 1 2 3 4 Roscoe, R.D. , Segedy, J.R., Sulcer, B., Jeong, H., Biswas, G. (2013). Shallow strategy development in a teachable agent environment designed to support self-regulated learning. Computers & Education 62, pp.286–297. doi:10.1016/j.compedu.2012.11.008. Retrieved from http://www.sciencedirect.com/science/article/pii/S0360131512002734
* ↑ Winne, P.H., Nesbit, J.C., Kumar, V., Hadwin, A.F., Lajoie, S.P., Azevedo, R., & Perry, N.E. (2006). Supporting self-regulated learning with gStudy software: The learning kit project. Tech, Inst., Cognitive and Learning, 3, 105-113.
* 1 2 3 4 5 6 Khosravifar, B., Bouchet, F., Feyzi-Behnagh, R., Azevedo,R., & Harley, J.M. (2013). Using Intelligent Multi-Agent Systems to model and foster Self-Regulated Learning:A Theoretically-Based Approach using Markov Decision Process. IEEE 27th International Conference on Advanced Information Networking and Applications.pp.413-420.DOI 10.1109/AINA.2013.70.
* 1 2 3 4 5 Azevedo, R., Witherspoon, A.,Chauncey, A., Burkett, C.,&Fike, A. (2009). MetaTutor: A MetaCognitive Tool for enhancing Self-Regulated Learning. Cognitive and Metacognitive Educational Systems: Papers from the AAAI Fall Symposium (FS-09-02). Retrieved from https://www.aaai.org/ocs/index.php/FSS/FSS09/paper/viewFile/995/1253.
* 1 2 3 4 Winne, P. H., & Hadwin, A. F. (2013). nStudy: Tracing and supporting self-regulated learning in the Internet. In R. Azevedo & V. Aleven (Eds.), International handbook of metacognition and learning technologies. New York: Springer.
* ↑ Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45 ,166–183.
* 1 2 3 Winne, P. H. (2011). A cognitive and metacognitive analysis of self-regulated learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Handbook of self-regulation of learning and performance (pp. 15–32). New York: Routledge.
* 1 2 3 Winne, P. H., & Nesbit, J. C. (2009). Supporting self-regulated learning with cognitivetools. In D. J. Hacker, J. Dunlosky & A. C. Graesser (Eds.), Handbook of metacognitionin education (pp. 259-277). New York: Routledge
* 1 2 3 4 5 6 7 8 9 10 11 12 Zumbrunn, S., Tadlock, J., & Roberts, E. D. (2011). Encouraging self-regulated learning in the classroom: A review of the literature. Metropolitan Educational Research Consortium (MERC).
* 1 2 Nicol, D. J., & Macfarlane‐Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in higher education, 31(2), 199-218.
* 1 2 3 4 English, M. C., & Kitsantas, A. (2013). Supporting student self-regulated learning in problem-and project-based learning. Interdisciplinary journal of problem-based learning, 7(2), 6.
* ↑ Dignath, C., & Büttner, G. (2008). Components of fostering self-regulated learning among students. A meta-analysis on intervention studies at primary and secondary school level. Metacognition and Learning, 3(3), 231–264.
* ↑ Masui, C., & De Corte, E. (2005). Learning to reflect and to attribute constructively as basic components of self-regulated learning. British Journal of Educational Psychology, 75(3), 351–372.
* ↑ Perels, F., Gürtler, T., & Schmitz, B. (2005). Training of self-regulatory and problem-solving competence.Learning and Instruction, 15(2), 123–139.
* ↑ Schunk, D. H., & Ertmer, P. A. (2000). Self-regulation and academic learning: Self-efficacy enhancing interventions. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation(pp. 631–649). San Diego:Academic.
* 1 2 Labuhn, A.S., Zimmerman, B.J., & Hasselhorn, M. (2010). Enhancing students’ self- regulation and mathematics performance: The influence of feedback and self- evaluative standards Metacognition and Learning , 5 (2), 173-19.
* ↑ Waeytens, K., Lens, W., & Vandenberghe, R. (2002). Learning to learn': teachers' conceptions of their supporting role. Learning and instruction, 12(3), 305-322.
* ↑ Graham, S., & Harris, K.R. (2005). Improving the writing performance of young struggling writers: Theoretical and programmatic research from the center on accelerating student learning. Journal of Special Education , 39 (1), 19-33.
* 1 2 3 Kistner, S., Rakoczy, K., Otto, B., Dignath-van Ewijk, C., Büttner, G., & Klieme, E. (2010). Promotion of self-regulated learning in classrooms: Investigating frequency, quality, and consequences for student performance. Metacognition and learning, 5(2), 157-171.
* 1 2 Lapan, R. T., Kardash, C. M., & Turner, S. (2002). Empowering students to become self-regulated learners. Professional School Counseling, 5(4), 257-265.
* 1 2 3 Zimmerman, B. J. (2000). Self-efficacy: An essential motive to learn. Contemporary educational psychology, 25(1), 82-91.
* ↑ de Boer, H., Donker-Bergstra, A. S., Kostons, D. D. N. M., Korpershoek, H., & van der Werf, M. P. (2013). Effective strategies for self-regulated learning: A meta-analysis. GION/RUG.
* 1 2 3 4 Kobayashi, M. (2006). Facilitating Academic Achievement in High School Interactive Television Programs by Promoting Self-Regulated Learning (Doctoral dissertation, Virginia Polytechnic Institute and State University).
* 1 2 3 4 Pino-Pasternak, D., Basilio, M., & Whitebread, D. (2014). Interventions and classroom contexts that promote self-regulated learning: Two intervention studies in United Kingdom primary classrooms. Psykhe, 23(2).
* 1 2 Housand, A., & Reis, S. M. (2008). Self-regulated learning in reading: Gifted pedagogy and instructional settings. Journal of Advanced Academics, 20(1), 108-136.
* ↑ Omorogiuwa, K. O. (2012). Facilitating self-regulated learning through effective feedback. Manzoor Ahmed, 7.
* ↑ Encouraging self-regulated learning in the classroom. (n. d.). Retrieved from http://serc.carleton.edu/sage2yc/studentsuccess/self_regulated/encouraging.html
* ↑ Ogawa, A. (2011). Facilitating Self-Regulated Learning: An Exploratory Case of Teaching a University Course on Japanese Society. International Journal of Teaching and Learning in Higher Education, 23(2), 166-174.
* ↑ Cleary, T. J., & Zimmerman, B. J. (2004). Self‐regulation empowerment program: A school‐based program to enhance self‐regulated and self‐motivated cycles of student learning. Psychology in the Schools, 41(5), 537-550.
* ↑ Young, M. R. (2005). The motivational effects of the classroom environment in facilitating self-regulated learning. Journal of Marketing Education, 27(1), 25-40.
* ↑ Paris, S. G., & Paris, A. H. (2001). Classroom applications of research on self-regulated learning. Educational psychologist, 36(2), 89-101.
* ↑ Ferreira, P. C., & Simão, A. M. V. (2012). Teaching Practices that Foster Self-regulated Learning: a case study. Educational Research eJournal, 1(1), 1-16.
* ↑ Kuo, Y. H. (2010). Self-Regulated Learning: From Theory to Practice. Online Submission.
* ↑ Activities that develop self-regulated learning. (n. d.). Retrieved from http://serc.carleton.edu/sage2yc/studentsuccess/self_regulated/activities.html
* ↑ Deci, E.L., Vallerand, R.J., Pelletier, L.G., & Ryan, R. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346.
* ↑ Deci, E.L., Vallerand, R.J., Pelletier, L.G., & Ryan, R. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346.
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The Self-Determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Perie, M., Grigg, W. & Donahue, P. (2005). The nation’s report card: Reading 2005 (U.S. Department of Education, National Center for Educational Statistics, NCES 2006-451). Washington, DC: U.S. Government Printing Office.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.
* ↑ Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_6
* ↑ Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and education: The self-determination perspective. Educational Psychologist, 26(3-4), 325-346. doi:10.1207/s15326985ep2603&4_6
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, Competence, and Relatedness in the Classroom: Applying Self-Determination Theory to Educational Practice. Theory And Research In Education, 7(2), 133-144.
* ↑ Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.
* ↑ Deci, E. L., & Ryan, R. M. (1985). Intrinsic motivation and self-determination in human behavior. New York: Plenum.
* ↑ deCharms, R. (1968). Personal causation. New York: Academic Press.
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 54-67. doi:10.1006/ceps.1999.1020
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-2
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-2
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-2
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-2
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-2
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Ryan, R. M., Miniis, V., & Koestner, R. (1983). Relation of reward contingency and interpersonal context to intrinsic motivation: A review and test using cognitive eval- nation theory. Journal of Personality and Social Psychology, 45, 736 750
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Deci, E. L., Koestner, R., & Ryan, R. M. (2001). Extrinsic Rewards and Intrinsic Motivation in Education: Reconsidered Once Again. Review Of Educational Research, 71(1), 1-27.
* ↑ Froiland, J. M., Oros, E., Smith, L., & Hirchert, T. (2012). Intrinsic motivation to learn: The nexus between psychological health and academic success. Contemporary School Psychology, 1691-100.
* ↑ Radel, R., Sarrazin, P., Legrain, P. & Wild, T.C. (2010). Social contagion of motivation between teacher and student: Analyzing underlying processes. Journal of Educational Psychology, 102, 577-587.
* ↑ Lam, S., Law, W., Chan, C., Wong, B. H., & Zhang, X. (2015). A Latent Class Growth Analysis of School Bullying and Its Social Context: The Self-Determination Theory Perspective. School Psychology Quarterly, 30(1), 75-90
* ↑ Lam, S., Law, W., Chan, C., Wong, B. H., & Zhang, X. (2015). A Latent Class Growth Analysis of School Bullying and Its Social Context: The Self-Determination Theory Perspective. School Psychology Quarterly, 30(1), 75-90
* ↑ Bembenutty, H. (2012). An Interview With Allan Wigfield: A Giant on Research on Expectancy-Value, Motivation, and Reading Achievement. Journal of Advanced Academics, 23(2). 186.
* ↑ Bembenutty, H. (2012). An Interview With Allan Wigfield: A Giant on Research on Expectancy-Value, Motivation, and Reading Achievement. Journal of Advanced Academics, 23(2). 186.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 72.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 72.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 72.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 72.
* ↑ Haichun, S., Haiyong, D., & Ang, C. (2013). NOTHING BUT BEING THERE MATTERS: EXPECTANCY-VALUE MOTIVATION BETWEEN U.S. AND CHINESE MIDDLE SCHOOL STUDENTS. International Education, 42(2), 9.
* ↑ Haichun, S., Haiyong, D., & Ang, C. (2013). NOTHING BUT BEING THERE MATTERS: EXPECTANCY-VALUE MOTIVATION BETWEEN U.S. AND CHINESE MIDDLE SCHOOL STUDENTS. International Education, 42(2), 9.
* ↑ Haichun, S., Haiyong, D., & Ang, C. (2013). NOTHING BUT BEING THERE MATTERS: EXPECTANCY-VALUE MOTIVATION BETWEEN U.S. AND CHINESE MIDDLE SCHOOL STUDENTS. International Education, 42(2), 9.
* ↑ Haichun, S., Haiyong, D., & Ang, C. (2013). NOTHING BUT BEING THERE MATTERS: EXPECTANCY-VALUE MOTIVATION BETWEEN U.S. AND CHINESE MIDDLE SCHOOL STUDENTS. International Education, 42(2), 9.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 70.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 70.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 75.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 75.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 75.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 76.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 76.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 77.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 77.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 77.
* ↑ Liem, Gregory, & Chua, Bee Leung. (2013). Educational Psychology: An International Journal of Experimental Educational Psychology, 33, 290.
* ↑ Liem, Gregory, & Chua, Bee Leung. (2013). Educational Psychology: An International Journal of Experimental Educational Psychology, 33, 291.
* ↑ Liem, Gregory, & Chua, Bee Leung. (2013). Educational Psychology: An International Journal of Experimental Educational Psychology, 33, 291.
* ↑ Liem, Gregory, & Chua, Bee Leung. (2013). Educational Psychology: An International Journal of Experimental Educational Psychology, 33.
* ↑ Liem, Gregory, & Chua, Bee Leung. (2013). Educational Psychology: An International Journal of Experimental Educational Psychology, 33.
* ↑ Liem, Gregory, & Chua, Bee Leung. (2013). Educational Psychology: An International Journal of Experimental Educational Psychology, 33.
* ↑ Brophy, J. (2008). Developing Students' Appreciation for What Is Taught in School. Educational Psychologist, 43(3), 132-141.
* ↑ Brophy, J. (2008). Developing Students' Appreciation for What Is Taught in School. Educational Psychologist, 43(3), 132-141.
* ↑ Brophy, J. (2008). Developing Students' Appreciation for What Is Taught in School. Educational Psychologist, 43(3), 132-141.
* ↑ Brophy, J. (2008). Developing Students' Appreciation for What Is Taught in School. Educational Psychologist, 43(3), 132-141.
* ↑ Dietrich, Julia, Dicke, Anna-Lena, Kracke, Barbel, and Noack, Peter. Teacher support and its influence on students' intrinsic value and effort: Dimensional comparison effects across subjects. Institute of Educational Science, Jena. (2014) 45-54
* ↑ Dietrich, Julia, Dicke, Anna-Lena, Kracke, Barbel, and Noack, Peter. Teacher support and its influence on students' intrinsic value and effort: Dimensional comparison effects across subjects. Institute of Educational Science, Jena. (2014) 45-54
* ↑ Dietrich, Julia, Dicke, Anna-Lena, Kracke, Barbel, and Noack, Peter. Teacher support and its influence on students' intrinsic value and effort: Dimensional comparison effects across subjects. Institute of Educational Science, Jena. (2014) 45-54
* ↑ Dietrich, Julia, Dicke, Anna-Lena, Kracke, Barbel, and Noack, Peter. Teacher support and its influence on students' intrinsic value and effort: Dimensional comparison effects across subjects. Institute of Educational Science, Jena. (2014) 45-54
* ↑ Dietrich, Julia, Dicke, Anna-Lena, Kracke, Barbel, and Noack, Peter. Teacher support and its influence on students' intrinsic value and effort: Dimensional comparison effects across subjects. Institute of Educational Science, Jena. (2014) 45-54
* ↑ Dietrich, Julia, Dicke, Anna-Lena, Kracke, Barbel, and Noack, Peter. Teacher support and its influence on students' intrinsic value and effort: Dimensional comparison effects across subjects. Institute of Educational Science, Jena. (2014) 45-54
* ↑ Dietrich, Julia, Dicke, Anna-Lena, Kracke, Barbel, and Noack, Peter. Teacher support and its influence on students' intrinsic value and effort: Dimensional comparison effects across subjects. Institute of Educational Science, Jena. (2014) 45-54
* ↑ Flake, Jessica, Barron, Kenneth, Hulleman, Christopher, McCoach, Betsy, and Welsh, Megan. "Measuring cost: The forgotten component of expectancy-value theory" Contemporary Educational Psychology. 41, 232-244.
* ↑ Flake, Jessica, Barron, Kenneth, Hulleman, Christopher, McCoach, Betsy, and Welsh, Megan. "Measuring cost: The forgotten component of expectancy-value theory" Contemporary Educational Psychology. 41, 232-244.
* ↑ Wigfield, Allan, & Eccles, Jacquelynne. (2000). Expectancy-Value Theory of Achievement Motivation. Contemporary Educational Psychology, 25, 72
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Christopher, N. M. (2014). Expectances and Outcomes of an Extensive Reading Programme Carried out among University Students. Arab World English Journal, 5(3), 154-169.
* ↑ Eison, J A. (1982). Educational and personal dimensions of learning and grade-oriented students. Psychological Reports, 51, 867-870.
* ↑ Eison, J A. (1982). Educational and personal dimensions of learning and grade-oriented students. Psychological Reports, 51, 867-870.
* ↑ Fan, J., Meng, H., Billings, R S., Litchfield R C., & Kaplan, I. (2008). On the role of goal orientation traits and self-efficacy in the goal-setting process: distinctions that make a difference. Human Performance, 21, 354-382.
* ↑ Fan, J., Meng, H., Billings, R S., Litchfield R C., & Kaplan, I. (2008). On the role of goal orientation traits and self-efficacy in the goal-setting process: distinctions that make a difference. Human Performance, 21, 354-382.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Leondari, A., & Gialamas, V. (2002). Implicit theories, goal orientations, and perceived competence: Impact on students’ achievement behavior. Psychology in The Schools, 39(3), 279-291.
* ↑ Leondari, A., & Gialamas, V. (2002). Implicit theories, goal orientations, and perceived competence: Impact on students’ achievement behavior. Psychology in The Schools, 39(3), 279-291.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Leondari, A., & Gialamas, V. (2002). Implicit theories, goal orientations, and perceived competence: Impact on students’ achievement behavior. Psychology in The Schools, 39(3), 279-291.
* ↑ Leondari, A., & Gialamas, V. (2002). Implicit theories, goal orientations, and perceived competence: Impact on students’ achievement behavior. Psychology in The Schools, 39(3), 279-291.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Leondari, A., & Gialamas, V. (2002). Implicit theories, goal orientations, and perceived competence: Impact on students’ achievement behavior. Psychology in The Schools, 39(3), 279-291.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Song, J., Bong, M., Lee, K., & Kim, S. (2015). Longitudinal investigation into the role of perceived social support in adolescents’ academic motivation and achievement. Journal Of Educational Psychology, 107(3), 821-841.
* ↑ Song, J., Bong, M., Lee, K., & Kim, S. (2015). Longitudinal investigation into the role of perceived social support in adolescents’ academic motivation and achievement. Journal Of Educational Psychology, 107(3), 821-841.
* ↑ Song, J., Bong, M., Lee, K., & Kim, S. (2015). Longitudinal investigation into the role of perceived social support in adolescents’ academic motivation and achievement. Journal Of Educational Psychology, 107(3), 821-841.
* ↑ Song, J., Bong, M., Lee, K., & Kim, S. (2015). Longitudinal investigation into the role of perceived social support in adolescents’ academic motivation and achievement. Journal Of Educational Psychology, 107(3), 821-841.
* ↑ Song, J., Bong, M., Lee, K., & Kim, S. (2015). Longitudinal investigation into the role of perceived social support in adolescents’ academic motivation and achievement. Journal Of Educational Psychology, 107(3), 821-841.
* ↑ Song, J., Bong, M., Lee, K., & Kim, S. (2015). Longitudinal investigation into the role of perceived social support in adolescents’ academic motivation and achievement. Journal Of Educational Psychology, 107(3), 821-841.
* ↑ Fernández-Río, J., Cecchini, J. A., & Méndez-Giménez, A. (2014). Effects of cooperative learning on perceived competence, motivation, social goals, effort and boredom in prospective primary education teachers. Infancia Y Aprendizaje / Journal For The Study Of Education And Development, 37(1), 57-71
* ↑ Fernández-Río, J., Cecchini, J. A., & Méndez-Giménez, A. (2014). Effects of cooperative learning on perceived competence, motivation, social goals, effort and boredom in prospective primary education teachers. Infancia Y Aprendizaje / Journal For The Study Of Education And Development, 37(1), 57-71
* ↑ Fernández-Río, J., Cecchini, J. A., & Méndez-Giménez, A. (2014). Effects of cooperative learning on perceived competence, motivation, social goals, effort and boredom in prospective primary education teachers. Infancia Y Aprendizaje / Journal For The Study Of Education And Development, 37(1), 57-71
* ↑ Fernández-Río, J., Cecchini, J. A., & Méndez-Giménez, A. (2014). Effects of cooperative learning on perceived competence, motivation, social goals, effort and boredom in prospective primary education teachers. Infancia Y Aprendizaje / Journal For The Study Of Education And Development, 37(1), 57-71
* ↑ Fernández-Río, J., Cecchini, J. A., & Méndez-Giménez, A. (2014). Effects of cooperative learning on perceived competence, motivation, social goals, effort and boredom in prospective primary education teachers. Infancia Y Aprendizaje / Journal For The Study Of Education And Development, 37(1), 57-71
* ↑ Fernández-Río, J., Cecchini, J. A., & Méndez-Giménez, A. (2014). Effects of cooperative learning on perceived competence, motivation, social goals, effort and boredom in prospective primary education teachers. Infancia Y Aprendizaje / Journal For The Study Of Education And Development, 37(1), 57-71
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Wosnitza, M., & Volet, S. (2012). Editorial introduction: Multiple goals in learning contexts. Applied Psychology: An International Review, 61(4), 513-519.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Wolters, C. A. (2004). Advancing Achievement Goal Theory: Using Goal Structures and Goal Orientations to Predict Students’ Motivation, Cognition, and Achievement. Journal of Educational Psychology, 96(2), 236-250.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Kaplan, A., & Maehr, M. L. (2007). The contributions and prospects of goal orientation theory. Educational Psychology Review, 19(2), 141-184.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Madjar, N., Kaplan, A., & Weinstock, M. (2011). Clarifying mastery-avoidance goals in high school: Distinguishing between intrapersonal and task-based standards of competence. Contemporary Educational Psychology, 36(4), 268-279.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education
* ↑ Follette, V. M., & Jacobson, N. S. (1987). Importance of attributions as a predictor of how people cope with failure. Journal Of Personality And Social Psychology, 52(6), 1205-1211. doi:10.1037/0022-3514.52.6.1205
* ↑ Follette, V. M., & Jacobson, N. S. (1987). Importance of attributions as a predictor of how people cope with failure. Journal Of Personality And Social Psychology, 52(6), 1205-1211. doi:10.1037/0022-3514.52.6.1205
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education
* ↑ Follette, V. M., & Jacobson, N. S. (1987). Importance of attributions as a predictor of how people cope with failure. Journal Of Personality And Social Psychology, 52(6), 1205-1211. doi:10.1037/0022-3514.52.6.1205
* ↑ Valiente, C., Swanson, J., & Eisenberg, N. (2011). Linking Students’ Emotions and Academic Achievement: When and Why Emotions Matter. Child Development Perspectives, 6(2), 129-135.
* ↑ Valiente, C., Swanson, J., & Eisenberg, N. (2011). Linking Students’ Emotions and Academic Achievement: When and Why Emotions Matter. Child Development Perspectives, 6(2), 129-135.
* ↑ Valiente, C., Swanson, J., & Eisenberg, N. (2011). Linking Students’ Emotions and Academic Achievement: When and Why Emotions Matter. Child Development Perspectives, 6(2), 129-135.
* ↑ Zahed-Babelan, A., & Moenikia, M. (2010). The role of emotional intelligence in predicting students’ academic achievement in distance education system. Procedia - Social and Behavioral Sciences, 2, 1158-1163.
* ↑ Trigwell, K., Ellis, R., & Han, F. (2011). The relations between students' approaches to leaning, experienced emotions and outcomes if learning' Studies in Higher Education, 37(7), 811-824.
* ↑ Reisenzein, R. (2014). The Attributional Approach to Emotion and Motivation: Introduction to a Special Section of Emotion Review. Emotion Review, 6(4), 332-335.
* ↑ Reisenzein, R. (2014). The Attributional Approach to Emotion and Motivation: Introduction to a Special Section of Emotion Review. Emotion Review, 6(4), 332-335.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ Follette, V., & Jacobson, N. (1987). Importance of attributions as a predictor of how people cope with failure. Journal of Personality and Social Psychology, 52(6), 1205-1211.
* ↑ Follette, V., & Jacobson, N. (1987). Importance of attributions as a predictor of how people cope with failure. Journal of Personality and Social Psychology, 52(6), 1205-1211.
* ↑ Zahed-Babelan, A., & Moenikia, M. (2010). The role of emotional intelligence in predicting students’ academic achievement in distance education system. Procedia - Social and Behavioral Sciences, 2, 1158-1163.
* ↑ Zahed-Babelan, A., & Moenikia, M. (2010). The role of emotional intelligence in predicting students’ academic achievement in distance education system. Procedia - Social and Behavioral Sciences, 2, 1158-1163.
* ↑ Zahed-Babelan, A., & Moenikia, M. (2010). The role of emotional intelligence in predicting students’ academic achievement in distance education system. Procedia - Social and Behavioral Sciences, 2, 1158-1163.
* ↑ Bruning, R., Schraw, G., & Norby, M. (2011). Cognitive Psychology and Instruction, Fifth Edition. Pearson.
* ↑ Matuliauskaite, A., & Zemeckyte, L. (2011). Analysis of interdependencies between students’ emotions, learning productivity, academic achievements and physiological parameters. Science - Future of Lithuania, 3(2), 51-56.
* ↑ Matuliauskaite, A., & Zemeckyte, L. (2011). Analysis of interdependencies between students’ emotions, learning productivity, academic achievements and physiological parameters. Science - Future of Lithuania, 3(2), 51-56.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ King, R., & Gaerlan, M. (2013). High self-control predicts more positive emotions, better engagement, and higher achievement in school. European Journal of Psychology of Education Eur J Psychol Educ, 29, 81-100.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Zahed-Babelan, A., & Moenikia, M. (2010). The role of emotional intelligence in predicting students’ academic achievement in distance education system. Procedia - Social and Behavioral Sciences, 2(2), 1158-1163.
* ↑ Naude, L. n., Bergh, T., & Kruger, I. (2014). 'Learning to like learning': an appreciative inquiry into emotions in education. Social Psychology Of Education, 17(2), 211-228.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Naude, L. n., Bergh, T., & Kruger, I. (2014). 'Learning to like learning': an appreciative inquiry into emotions in education. Social Psychology Of Education, 17(2), 211-228.
* ↑ Weiner, B. (1979). A theory of motivation for some classroom experiences. Journal Of Educational Psychology, 71(1), 3-25.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Bruning, R. H., Schraw G. J., Norby M. M., (2011) Cognitive Psychology and Instruction. Boston, MA: Pearson Education.
* ↑ Naude, L. n., Bergh, T., & Kruger, I. (2014). 'Learning to like learning': an appreciative inquiry into emotions in education. Social Psychology Of Education, 17(2), 211-228.
* ↑ Seifert, T. L. (2004). Understanding student motivation. Educational Research, 46(2), 137-149.
In order for education to be the most successful, educators need to understand not only the various ways in which intelligence and knowledge is acquired, but also the beliefs surrounding them which are held by students and teachers. These beliefs are influenced by hope and impact students' behaviors and what they believe they can achieve academically. The way teachers view these beliefs will influence the way they structure their classrooms and curriculum, which in turn has an effect on students educational experiences. This chapter will further explain hope and the beliefs about knowledge and intelligence and the impact they have on learning.

## Beliefs
### Implicit and Explicit Beliefs

Beliefs are personal opinions about the environment and the self. Each person holds both implicit beliefs and explicit beliefs. Implicit beliefs are subliminal beliefs that influence an individual’s behaviour. For example, an international student who attended schools that only taught in Chinese, might develop an implicit belief that he or she has poor English pronunciation. Subsequently, this belief causes him or her to avoid reading or speaking aloud in an English-speaking school. In addition, implicit beliefs help the construction of an implicit theory, which involves an individual making unspoken speculations about the causes of an event. As an example, the aforementioned international student might state that he cannot pronounce English words properly because English is not the student’s mother tongue and the student’s family does not speak English at home. Consequently, the student has implicitly attributed his failure of pronouncing English words to both innate ability and practice. Explicit beliefs are conscious beliefs that impact a person’s behaviour. For example, a student who is consciously aware of his or her excellent speaking and writing in English class might develop an explicit belief that he or she has proficiency in English.
It is important to transform implicit beliefs to explicit beliefs because many attributions that people place upon their learning performance are implicit. The unconsciousness of certain beliefs will likely prevent people from discovering the reasons behind behaviors which might not be effective and/or healthy. In order to reflect on and to modify one’s beliefs, an individual should spend time trying to express their implicit beliefs to themselves and to the people around them. For example, a person can write in a journal or participate in group discussions.

### Development and Effects of Beliefs

Before we can understand how to change beliefs, it is important to understand how beliefs come to exist. It has been found that for many teachers, beliefs are derived as a result of their own personal experiences in education growing up. As a result, elementary teacher who are pre-service, enter programs with preconceived beliefs and attitudes towards education and how it should be approached. Beliefs about knowledge and intelligence is very important in classroom environments, as it provides the structure and base for organizing these environments. They impact how a teacher designs his or her classroom in terms of curriculum, methods, techniques and skills. Even the teaching of specific subjects such as math is impacted by the way teachers view knowledge and intelligence, as discovered by Stohlmann et. al (2014), which will be discussed later in this section. One area of beliefs teachers may hold is in regards to the roles of students and how information is attained. One theory, described by Bas (2015) is that teachers maintain either a traditional view, or a constructivist view about education. On the one hand, the traditional view is where teachers act as the authority figure towards students who are passive recipients of knowledge. On the other hand, a constructivist view sees the teacher as a guide who helps students in obtaining knowledge, in this view students are active participants in their own learning. A similar but more detailed view is the epistemological belief which consists of four categories, in which students progress through in their educational development. These categories include dualism, multiplism, relativism, and commitment. Dualism acts similarly to a traditional view, while multiplism shares views with a constructivist perspective.

### Changing Beliefs of Students and Teachers

It can be very difficult for people to change their beliefs and attitudes, Brownlee et al. (2001) found this to be especially true the more a belief is connected with other beliefs within an attitude structure. Whether information has been acquired as affective knowledge, which is subjective and based on emotional reactions or as cognitive knowledge, which is knowledge obtained objectively and rationally, will also impact the difficulty of changing ones beliefs.
While beliefs may be difficult to change, it is still possible to achieve with the proper understanding of how to implement beneficial change. When it comes to changing beliefs which have been attained through affective knowledge or cognitive knowledge, how the information was originally obtained plays a significant role in how the belief should be challenged. It has been found that information which is obtained through cognitive knowledge, is resistant to change through affective means and vice versa. This means that information which has initially been obtained through cognitive means, is more prone to change through cognitive means, and information initially obtained through affective means, is more susceptible to changing beliefs through affective means.
Another way in which beliefs can be changed was found in a study which compared techniques teachers in the US used, with techniques used by teachers in China. It was found that Chinese teachers had a greater coherent understanding of the concepts and were therefore better able to provide flexibility in their explanations, these teachers were also better able to provide meanings to their students. In contrast, teachers from the US were procedure based, and were not able to provide the same rich explanations to their students. US teachers beliefs about the best approach to teaching math changed once they were able to see the difficulties students had when they were taught only procedurally and not conceptually, and when a change in student learning was evident. While it may be difficult to change student or teachers views about knowledge and intelligence may be difficult, by providing environments where students and pre-service teachers are able to reflect on their own beliefs and shift into new modes of thought, a change in belief can be possible.

## Beliefs about Intelligence
### Intelligence

Intelligence can be defined in multiple ways. According to Sternberg, intelligence is based on three components: adjusting to, shaping and choosing an environment. It is also related to discovery and invention. Throughout history researchers studied intelligence to determine its nature and outcomes. In addition, social and cultural factors influence the ways people interpret intelligence. Moreover, intelligence is viewed as a general ability or as multiple abilities. For instance, Gardner’s theory of multiple intelligences involves seven intelligence aspects: logical-mathematical, spatial, bodily-kinesthetic, verbal, musical, interpersonal and intrapersonal intelligence (refer Figure 2). Similarly, Sternberg discovered three types of intelligence: emotional, creative and practical intelligence. Lastly, Carroll’s hierarchy of intelligence represents intelligence as a general ability that is made up of broader abilities, which can be further broken down into more specific abilities (refer to Figure 3).

### Entity and Incremental Theory

Two implicit theories of intelligence pioneered by Dweck are known as the entity theory and incremental theory. The entity theory presents the belief that intelligence cannot be changed; whereas, the incremental theory demonstrates that gradual modifications of intelligence are possible. Entity and incremental theorists differ from each other based on their understanding of an individual’s behaviour. For instance, entity theorists explain a person’s behaviour due to his or her genetically determined characteristics. Incremental theorists however, focus on identifying certain factors such as, intentions, necessity, previous behaviour and emotions, which give rise to an individual’s behaviour. Consequently, entity and theorists have different responses toward negative consequences. Individuals who believe in the entity theory will have a higher chance of demonstrating helplessness when they are facing challenges in terms of their performance . Furthermore, they will attribute their poor performance to their unchangeable traits; therefore, they feel that they have no control over their intelligence. On the other hand, those who believe in the incremental theory of intelligence will likely use controllable factors to counter negative effects to improve their performance.
| | Entity Theory | Incremental Theory |
| Intelligence is Changeable | No | Yes |
| Explanation of Behaviour | Genetics | Intentions, necessity, previous behaviour, emotions |
| Reaction to Negative Consequences | Helplessness, giving up | Persistence, problem-solving by regaining control |
As mentioned earlier, intelligence can be viewed as multiple abilities. Furnham conducted a study recently on entity and incremental beliefs about the multiple intelligences. The goals of the study was to see whether students believe that each of the fourteen intelligences is changeable or fixed and whether personality (e.g. Big Five and CORE self-beliefs) has a role in these entity and incremental beliefs. The fourteen intelligences were divided into three categories: abstract, skillful and classical. Abstract intelligences, such as naturalistic, sexual and intra-personal intelligences are easier to change. In addition, skillful intelligences, such as musical and creative intelligences are less easy change because they are believed to be based on innate ability as well as practice. Moreover, classical intelligences which include verbal and logical intelligences are easy to change. The CORE self-beliefs in the study were measured based on self-esteem, self-efficacy, internal locus of control and emotional stability. Regardless of holding incremental beliefs, high CORE self-beliefs help people see that intelligence can be increased because these beliefs cause a person to see that change and improvement are possible. The study also demonstrated that people who are introverts are more likely to hold entity beliefs; whereas, people who are extroverts are more likely to hold incremental beliefs. Furthermore, the openness personality trait appeared to promote incremental beliefs. Overall, Furnham's study raises awareness for the need to understand the diversity of students in the classroom when observing their entity and incremental beliefs about intelligence. The multiple intelligences model along with the entity and incremental theories help educators to pinpoint students' beliefs about a specific intellectual ability, which can be useful since different disciplines request different skills. Also, educators can gain knowledge on what types of intelligences are harder to change from a student's perspective. Although the study only found correlations between personality traits, such as openness, extraversion and CORE self-beliefs and incremental beliefs about intelligence, it might still be useful to try to promote these traits and see if they are of any help to students' incremental beliefs.

### Goal Orientation and Learning Performance

Initially, Dweck and Leggett stated that the implicit theories of intelligence give rise to two separate goal orientations, which are known as the performance orientation and the mastery orientation. The performance orientation involves the belief in the entity theory and the display of proficiency; whereas, the mastery orientation includes the incremental theory and the desire to improve one’s proficiency. This goal orientation model suggests that people are either performance-oriented or mastery-oriented. Over time researchers discovered that people could be performance-oriented and learning-oriented at different degrees depending on the task. Additional features were added to this model, such as approach and avoidance. Both of these components are applicable to performance and learning orientations. As a result, research on the beliefs about intelligence throughout history has led to the creation of four goal orientations that influence learning performance:
* Performance-approach goals
Concentrate on the desire to show proficiency by making other people targets for competition. For example, a student decides to work and pay attention in class because he or she wants to get the top grade, subsequently the grade gives him or her the motivation to study. However, once this grade is no longer achieved, the student will likely lose interest in learning.
* Performance-avoidance goals
Focus on the finding ways to avoid tasks that will likely reflect failure when compared to others. For instance, a student wants to do well in a class because they do not want to lose and be embarrassed. However, if the student cannot perform well, then he or she will choose to avoid any task that they cannot succeed in. Subsequently, they will likely miss many learning opportunities.
* Mastery-approach goals
Bring about a commitment to improve competence and to engage in meaningful learning, in which understanding is highly valued. For example, a student chooses to learn by obtaining a strong understanding of the knowledge that is imparted in the classroom. The student will take the time to self-regulate his or her learning by posing questions to teachers when confused or when they want to learn something new and improving their understanding and knowledge through consistent discussion with teachers and classmates.
* Mastery-avoidance goals
Give rise to hiding from inadequacy in relation to the self and to an undertaking. For instance, a student does not believe that he or she has the ability to learn and understand something. As a result, the student often thinks negatively of him or herself by saying "I am not smart" or "This question is too hard". Overall, they believe that they cannot improve their ability which means they cannot deal with the difficult task at hand.

### Western and Chinese Beliefs about Intelligence

Beliefs about intelligence are mostly tied to the Western culture. However, these western beliefs are often not applied to other cultures, such as the Chinese culture, which is a significant problem because schools contain students from various cultures. In addition, Sternberg stated different cultures will have some dissimilar interpretations of intelligence, which in turn leads to varying behaviours. We dedicated a section for differences between Western and Chinese beliefs about intelligence because the recent success of Chinese students in international, academic assessments has produced a desire to discover whether the Western beliefs about intelligence affect these students’ learning performances.
Chen and Wong’s study compare Western and Chinese students’ beliefs about intelligence and their academic performance. In the Chinese cultural context, performance-approach goals are very common because the schools promote competition, which in turn encourages a social hierarchy that forces students to obtain high academic achievement. Moreover, the academic achievement in Chinese culture is viewed as a child's obligation to his or her family. Consequently, Chinese students are constantly competing to honour their families. Furthermore, mastery goals are prevalent as well because the Chinese culture values Confucian philosophy, which promotes self-development and self-fulfillment. The results from the study demonstrated that like Western students, Chinese students who hold incremental beliefs are more likely to utilize mastery goals, which help them build effective learning strategies. Subsequently, these students' academic performance are likely to be more successful. However, the study showed that Chinese students' academic achievement might be due to their use of performance-approach goals. Also, even though performance-avoidance goals are often negatively associated with learning, it is positively correlated with mastery goals in Chinese students. Overall, the desire for self-development, competition and avoidance of failure in the Chinese culture give rise to the positive correlations between mastery, performance-approach and performance-avoidance goals. Unlike Western students, Chinese students might be able to obtain academic success with both performance and mastery goals. More research will need to be conducted to prove this phenomenon because the current study has a limitation of the participants being university students with high academic success. Therefore, future research should involve middle and high school students with varying academic achievement.
Wang and Ng's study focused on grade seven and ten Chinese students' implicit beliefs about intelligence and school performance. The results of the study showed that Chinese students viewed the changeability of intelligence and school performance separately and that the two have a role in developing helplessness. The Chinese culture emphasize the importance of effort over ability in terms of academic achievement, but this does not necessarily mean that they automatically believe that intelligence is changeable. In fact effort can be associated with improving performance or counteracting substandard intelligence in Chinese students. Also, Wang and Ng found that Chinese students believed that school performance was more changeable than intelligence. Therefore, Chinese students might be more likely to avoid helplessness and might even have higher academic achievement than Western students. This is because Western students view intelligence and school performance as related. Western students that hold entity beliefs about intelligence focus mainly on innate ability, which in turn hampers their academic achievement. For example, if they believe that intelligence is fixed, then their school performance cannot be changed. Lastly, the study found that like Western students, Chinese students that strongly believe that intelligence or school performance are not changeable, will more likely develop helplessness.

## Hope

For a student to reach a high level of hope, two components are necessary. These are agencies which is goal-directed determination, and pathways which is the planning of ways to meet goals. Agencies are also referred to as willpower or ‘will’ and pathways are also commonly referred to as ‘ways’ for one to reach their goals. Mellard, Krieshok, Fall and Woods (2013) provide an example for understanding how pathways and agencies work by considering a highschool dropout working in the food industry who wants to earn more money. He may consider pathways such as working hard at his current job and try to get promoted, look for a better paying job for his current skill level. He may also consider a larger goal, but break it up into smaller achievable goals such as obtaining his GED, then getting a certification in trades. He would then move onto the agency stage, where he would choose one of his options and put it into action with thoughts such as “I’m capable of getting my GED”. If he were to encounter obstacles such as requiring transportation to get to school, he would use the same patterns and consider possible pathways such as asking a classmate for a ride or taking public transit. In order for high hope to develop both components must be present as neither alone is sufficient. External agents can influence hope as well, as external resources can help people increase the perceived pathways and agencies rather than thinking goal setting and hope are only individual pursuits.

### Benefits of Hope

There has been lots of research to show that high hope has several benefits for students mental well-being. It has been shown to increase optimism and happiness in students, and students with high hope are less likely to have anxiety or depression as students who have low hope. Higher hope has also shown to increase academic achievement, especially in students around the 7th grade. Research has also shown that these students are more likely to prepare to achieve academically by studying more and getting involved in extracurricular activities. When students have a higher level of hope they are also more likely to set more challenging goals for themselves at school, and focus on success over failure. This alternative focus leaves these students to perceive they will be successful at attaining the challenging goals they set for themselves.If students however, fail to obtain this perception they are likely to experience learned-helplessness. This maladaptive strategy commonly develops in performance-oriented students who have experienced failure and come to believe that anything they try will result in failure. As a result, these students refuse to engage in tasks because they assume they will not succeed. By failing to participate in anything, these students prevent themselves from being successful and therefore have a difficult time increasing their levels of hope for future accomplishments. The overwhelming research shows the importance of increasing levels of hope in students, not only for the benefits of mental well-being but also for the effects it has on students academic performance.

### Importance of Hope in the Education Process

It is important for parents and educators to create resilient learners by encouraging students to not only succeed but also stumble and fail. By doing so, students are able to recognize failure as something which they can overcome and learn from. It is also important to encourage a realistic understanding of a student's potential. Students who create goals which are too far out of their capacities are likely to fail more frequently and decrease their level of hope. Goal related experiences in general can be beneficial in increasing a student's level of hope, especially By creating goals which are realistic but still maintain some level of challenge, students are able to achieve goals and increase their level of hope for future challenges. Another recommendation to increase hope is to promote mastery goals in teaching. It is also beneficial for students to have role models to encourage students to stay mentally energized to continue to pursue their goals and assist in finding pathways to achieve them.

## Beliefs about Knowledge
### Models of Knowledge

Epistemological beliefs are the beliefs about what knowledge is and how one acquires that knowledge (Otting)Epistemological beliefs are the individually based systems of beliefs that are more or less independent from one another. They differ according to the age and the nature of education Younger learners are said to be more naïve, for instance, they quickly accept the knowledge without questioning it. Older learners, however, approach the knowledge in a more critical manner. In addition, one's type of the education affects one's epistemological beliefs. For example, the people who are in the soft sciences (e.g. psychology) approach the type of knowledge with uncertainty, which means that there are several answers or ways to solve a problem. On the other hand people in the hard sciences (e.g. chemistry) approach knowledge with the belief that it is fixed, thus there is one answer and not the several answers. Epistemological beliefs predict numerous aspects of academic performance, including comprehension, cognition in different academics domains, motivation, learning approaches and self-regulation. Therefore, it is important for the teachers to understand epistemological beliefs. This subsequent sections will discuss the three different models of knowledge that were suggested by Perry, Schommer and Kitchner&King.
Perry's dualist and relativist model of knowledge
Perry states that students pass through two stages of knowledge which are the dualistic and the relativistic. The dualist knowledge is when the knowledge is either right or wrong. There is no ambiguity. As the students’ progress, they tend to now think in a relativist manner. This approach states that knowledge can be evaluated based on personal experience. There is no one answer but rather the knowledge is uncertain. Knowledge approaches are very important because they affect how the students approach learning. Students who are in the dualistic stage are most likely to be looking for the fact-oriented information when they are studying. They study like they are memorizing the information and they do not take time to break down the information so that they could deeply understand it. This is different from the student who use the relativistic approach. When they are studying they tend to search for context-oriented information. This means that they break down the information through paraphrasing, constructing what they have understood and they also summarize their information. This leads to the students who use the relativist approach to learning, to do better in their classes when they are getting graded.
Schommer's four dimensions of knowledge
Schommer came up with four separate dimensions about knowledge The first one was simple knowledge this is when knowledge is organized in bits and pieces, meaning that for one to understand it, it has to be broken down into smaller simple parts. The second one was certain knowledge which is the belief that knowledge is absolute, for example the student believes that there is one answer. The third one is fixed ability is the belief that one’s ability to learn is innate and cannot be changed for example the student will believe that it is either they are born to grasp materials. The fourth one is the quick learning which is the belief that learning is fast process or it completely does not occur. The earlier research that was done by Schommer, showing the effects that these beliefs had on the individuals learning were as follows: those who believed that knowledge was certain & simple tend to not use critical thinking skills, self-regulating skills and meta cognitive skills which resulted in them not acquiring the deeper knowledge since they were not questioning what they were learningThose who believed that knowledge was fixed resulted in students engaging in superficial learning because they was no deep and thoughtful thinking when they were tuckling materials that were presented to them. This resulted in them giving up when they were faced with challenges Those who believed in quick knowledge, were presented with a text and told to write a conclusion, most of them tent oversimplify the conclusion. Meaning that they just scrapped on the surface without asking themselves why they would think that would be the conclusion
Kitchener and King's Reflective model
This is framework of work was coined by Kitchener and King, in which explains the different stages that the students go through in seven stages of reflective knowledge. These seven stages are dived into three stages which are pre-reflective judgment (stages 1 to 3 knowledge is certain), quasi- reflective judgment (stages 4 and 5 knowledge is not certain)and reflective judgment (stages 6 and 7 knowledge is context based). This model is important in that it focuses on the reasoning behind the answers of the open-ended questions and also the individual’s problem solving skills. Also, the model is affected by the age, education level and major that one is in. Consequently, this is significant in the learning process because those who believe that knowledge is simply something that is handed done from authority learn differently from those that believe that knowledge is constructed. The studies that were done about the different stages show that those who value the teacher’s expertise and think that knowledge is certain tend to follow a more traditional manner of learning. This means that they wait to be handed over materials by the teacher. However, students that are in stages 6 and 7 recognize that knowledge is something that is personally constructed and not handed down by an expertise. These students are able to challenge their learning environments and are more open to the collaboration of information with the other students, because they also believe that peers like teachers can be a source of knowledge.

### Western Culture vs Eastern Culture

There are cultural differences in the beliefs in epistemology. The two views that are going to be discussed are the Western culture and Eastern culture. The Western culture emphasizes more on the Socratic view, in which the students are taught to question and challenge the information that they are given. Therefore they are more active in their learning because they are expected to reflect on the given information. The Eastern view of learning is mainly based on the Confucius. This is the belief in the student’s effort and willingness to learn. The students were expected to respect the authority that is imparting information to them because they are seen as the ones that are always correct and needs to be constantly followed and be obeyed if one wants to learn. Learning is not something that the students just do, but they do it for a purpose. Most of the time the purpose of learning was for the students to go work as civil servants. These differences in cultural beliefs does not mean that these students are in the different stages of knowledge but rather that they have different ways to acquire knowledge. It is important that the teacher does not become bias about this views, by thinking that those students who value the what the authority is says without questioning it or those who come from the Eastern culture are in the early stages of knowledge..

## Application to Instruction
### Awareness and Discussion of Beliefs

It is important for educators to be aware of the various beliefs relating to knowledge and intelligence. By making students aware of what their beliefs are, through group discussions and reflection journals, teachers are better able to help students identify and change their beliefs. Moreover, teachers should also explicitly teach students how beliefs about intelligence and knowledge affect learning. For example, if someone believes intelligence is something which is fixed, they will be less likely to pursue in learning when faced with a challenge. In addition, if someone believes knowledge is fixed, then they are less likely to reflect or question their thoughts because they think what they know is always true. Similarly, these beliefs can change the opportunities in which we expose ourselves to. If an individual does not believe they have the knowledge or intelligence required for a certain career opportunity, they are not likely to attempt to pursue that career. With appropriate belief strategies, nearly all students can attain a high academic achievement as these strategies can encourage students to use previous knowledge and develop advanced critical thinking skills. In this respect, classroom environments play a significant role in shaping students beliefs as they can enhance beliefs already held by students, challenge them, or introduce new ideas.
Not only is it important to be aware of the different beliefs about intelligence and knowledge held by students, but teachers should be aware that these beliefs change as the students age. For example, elementary school students tend to believe intelligence entails capacities based on cognition. This is determined by how much knowledge an individual possesses and how well they read and comprehends visuospatial relationships. These students believe intelligence involves non-cognitive factors, such as communication and interaction skills, work habits, and athleticism. High school students however pay more attention not only to a person’s cognitive abilities when judging an individual’s intelligence but also their performance. Jones’ study presents five themes of how high school students define intelligence: knowledge, skills and abilities; academic effort; achievement; decision making and personal characteristics. Taking the age of the student into consideration is important in understanding how they perceive intelligence. If teachers are aware of these beliefs, they can better recognize how it impacts students learning and organize their classroom environments and curriculum accordingly.
Epistomological knowledge is also believed to depend on the age and experiences of the child. According to studies done by Perry, as children progress through levels of education, so did their level of knowledge. As individuals mature, their beliefs about the complexity of knowledge, the justifications of knowledge and the effort required to obtain knowledge began to change. This finding is important for teachers to understand that acquiring critical thinking and justification of knowledge that is seen in the higher stages of reflective thinking or relativistic stage comes with age and experience. Therefore teachers should not rush to impose critical thinking, instead they should offer patience and support and take small steps when introducing critical thinking. The belief of intelligence being fixed or incremental also affects the academic achievement of the student and their motivation. Students who believe intelligence to be incremental see intelligence as something that requires effort. These students see failing a test a result of not putting in enough effort in studying, improving would require the motivation to applying more effort. This is different from students who believe intelligence is something that is fixed, which led to learned helplessness and lack of motivation to succeed in the next test. With these students teachers need to teach students that school is about effort and intelligence is not something which is fixed.

### Development of Reasoning Skills and Reflective Thinking

Teachers need to ensure that they give information that challenge their student’s epistemological views. Epistomologial beliefs influences the learning of the individuals. Those who believe that learning is something that is complex, uncertain, effortful and requiring justification tend to do well with their academics.This is because they know that their motivation changes their learning. They are also open to exploring the new ideas, and go out there to find deeper contextual information.These are the learners who are in the higher stages of the reflective thinking and those who are believed to be in the relativistic stage. The beliefs in the epistemological knowledge is something that should be taught to the teachers as well. This is because the teachers beliefs about knowledge and how it is acquired affects the student’s learning process. The teacher's beliefs about teaching are deemed important because they may be used to filter and interpret information, frame tasks, and guide action. The teachers who believed that they were the only source of information that their students had, structured the class in a non-discussion one. This led to their students believing that knowledge was certain, and the only sources of knowledge was from the authority. This differs from the teachers that believed that knowledge is constructive, this led to them designing the classroom in a more collaboration manner. The teachers would encourage students, to think critically about the information that they were given. The teacher also encouraged the student’s engagement with others because they knew that this will help in making them more open to the new ideas. This also encouraged the students in their reflective thinking. Therefore it is important that the teachers are trained not to have the traditional view of thinking because this in turn influences the students.

### Cultural Diversity

British Columbia's new curriculum has developed three competencies that students should strive for during their education. One of the competencies that relates to the cultural diversity of the beliefs about intelligence and knowledge is the positive personal and cultural identity competency:
"[T]he awareness, understanding, and appreciation of all the facets that contribute to a healthy sense of oneself. It includes awareness and understanding of one’s family background, heritage(s), language(s), beliefs, and perspectives in a pluralistic society. Students who have a positive personal and cultural identity value their personal and cultural narratives, and understand how these shape their identity. Supported by a sense of self-worth, self-awareness, and positive identity, students become confident individuals who take satisfaction in who they are, and what they can do to contribute to their own well-being and to the well-being of their family, community, and society."
The multicultural classroom in Canadian schools require educators to be open-minded and flexible when helping students develop their cultural identity and their beliefs. Figure 7 demonstrates the multicultural society that exists in Canada today. As mentioned earlier in this chapter, the Western and Eastern cultures have a different view on intelligence and knowledge. As a result, children need to be taught explicitly about how cultural identity affects their beliefs about intelligence and knowledge
In terms of beliefs about intelligence, cultural differences give rise to different goal orientations which in turn causes academic performances to vary. Therefore, teachers should evaluate the beliefs and goal orientations of each individual student in a private session to ensure that they are positive and useful. Unfortunately, there might be occasions, in which students have negative beliefs and ineffective goal orientations because of the cultural context they live in. For example, in Chen and Wong's aforementioned study, there appears to be a positive correlation between performance-approach, performance-avoidance and mastery goals. In addition, these goals each seem to help Chinese students' academic achievement. However, an important point to keep in mind is that this correlation is most likely based on the Chinese students' desire of self-development, competition and avoidance of failure. Educators should strive to encourage self-development to enable students to taken on mastery goals, but competition and avoidance of failure are not features of a good learning environment. There is a lot of stress that comes with competing and avoiding failure. Even if academic achievement is obtained, educators need to be cautious. It might be more effective to promote an incremental view of intelligence in the classroom because students holding this view are more likely to focus on their own improvement and to learn for the sake of mastery and enjoyment. Subsequently, students are more likely to feel confident and satisfied with their learning.
As for beliefs about knowledge. cultural differences lead to different ways of developing and utilizing knowledge. As mentioned earlier, the Western and Eastern cultures have differing views of knowledge. Therefore the teacher should be willing to have a multicultural classroom. For instance, one that has both the Socratic view and Confucian view and be able to teach the students to implement one or the other depending with the situation and the class that they are taking. The Socratic view is important for the social sciences classes in which the students are supposed to question what they are learning since there is no right or wrong answer. The Confucian view is helpful in learning the hard sciences. such as physics, which adhere to the laws, meaning that the student has to grasp the fundamental facts. The teacher should create a classroom that is group based so that the students can be able to share their different beliefs and critically think about them. Overall, teaching children the Socratic and Confucian approaches explicitly can help students have a better understanding of how cultural affects beliefs and thinking, which in turn prepares them to collaborate with people in a multicultural society. Additionally, other cultures' beliefs can also be researched and it is highly encouraged that teachers keep themselves updated to ensure that they are considering the effects of culture in their classrooms.

## Suggested Readings

Bernardo, A. B. I. (2010). Extending hope theory: Internal and external locus of trait hope. Personality and Individual Differences, 49, 944–949. doi:10.1016/j.paid.2010.07.036.
Haimovitz, K., Wormington, S. V., & Corpus, J. H. (2011). Dangerous mindsets: How beliefs about intelligence predict motivational change. Learning And Individual Differences, 21(6), 747-752. doi:10.1016/j.lindif.2011.09.002
OECD (2009), "Teaching Practices, Teachers' Beliefs and Attitudes", in OECD. , Creating Effective Teaching and Learning Environments: First Results from TALIS, OECD Publishing, Paris. DOI: 10.1787/9789264068780-6

## Glossary

Affective knowledge: Information acquired subjectively, based on emotional reaction.
Agency: Goal-directed determination, willpower.
Beliefs: personal opinions about the environment and the self
Certain knowledge: belief that knowledge is absolute
Cognitive knowledge: information acquired objectively and rationally.
Constructivist view: teachers are guides in helping students obtain knowledge, students are active in their own learning
Dualist knowledge: belief that knowledge is either right or wrong
Entity theory: the belief that intelligence cannot be changed
Epistemological beliefs: beliefs about what knowledge is and how one acquires that knowledge
Explicit beliefs: conscious beliefs that impact a person’s behaviour
Fixed ability: belief that one’s ability to learn is innate and cannot be changed for example the student will believe that it is either they are born to grasp materials
High hope: occurs when both agencies and pathways are present, students believe they have ability of attaining their goals.
Implicit beliefs: subliminal beliefs that influence an individual’s behaviour
Implicit theory: involves an individual making unspoken speculations about the causes of an event
Incremental theory: demonstrates that gradual modifications of intelligence are possible
Intelligence: a person's capacity to adjust to, shape and choose an environment
Mastery-approach goals: bring about a commitment to improve competence and to engage in meaningful learning, in which understanding is highly valued
Mastery-avoidance goals: give rise to hiding from inadequacy in relation to the self and to an undertaking
Mastery orientation: includes the incremental theory and the desire to improve one’s proficiency
Pathways: planning of ways to reach one's goals
Performance-approach goals: concentrate on the desire to show proficiency by making other people targets for competition
Performance-avoidance goals: focus on the finding ways to escape tasks that will likely reflect failure when compared to others
Performance orientation: involves the belief in the entity theory and the display of proficiency
Pre-reflective judgment: the stages in which knowledge is certain
Quasi-reflective judgment: the stages in which knowledge is uncertain
Quick learning: the belief that learning is fast process or it completely does not occur.
Relativist knowledge: belief that knowledge can be evaluated based on personal experience
Reflective Judgement: the stages in which knowledge is content based
Simple knowledge: knowledge is organized in bits and pieces, meaning that for one to understand it, it has to be broken down into smaller simple parts
Traditional views: teachers act as an authority figure while students are passive recipients of knowledge.

## References

* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Baş, G. G. (2015). Correlation Between Teachers' Philosophy of Education Beliefs and Their Teaching-Learning Conceptions. Education & Science / Egitim Ve Bilim, 40(182), 111-126. doi:10.15390/EB.2015.4811
* ↑ Stohlmann, M. M., Cramer, K. C., Moore, T. T., & Maiorca, C.C. (2014). Changing Pre-service Elementary Teachers' Beliefs about Mathematical Knowledge. Mathematics Teacher Education & Development, 16(2), 4-24
* ↑ Baş, G. G. (2015). Correlation Between Teachers' Philosophy of Education Beliefs and Their Teaching-Learning Conceptions. Education & Science / Egitim Ve Bilim, 40(182), 111-126. doi:10.15390/EB.2015.4811
* ↑ Baş, G. G. (2015). Correlation Between Teachers' Philosophy of Education Beliefs and Their Teaching-Learning Conceptions. Education & Science / Egitim Ve Bilim, 40(182), 111-126. doi:10.15390/EB.2015.4811
* ↑ Stohlmann, M. M., Cramer, K. C., Moore, T. T., & Maiorca, C.C. (2014). Changing Pre-service Elementary Teachers' Beliefs about Mathematical Knowledge. Mathematics Teacher Education & Development, 16(2), 4-24
* ↑ Baş, G. G. (2015). Correlation Between Teachers' Philosophy of Education Beliefs and Their Teaching-Learning Conceptions. Education & Science / Egitim Ve Bilim, 40(182), 111-126. doi:10.15390/EB.2015.4811
* ↑ Baş, G. G. (2015). Correlation Between Teachers' Philosophy of Education Beliefs and Their Teaching-Learning Conceptions. Education & Science / Egitim Ve Bilim, 40(182), 111-126. doi:10.15390/EB.2015.4811
* ↑ Brownlee, J., Purdie, N., & Boulton-Lewis, G. (2001). Changing Epistemological Beliefs in Pre-service Teacher Education Students. Teaching In Higher Education, 6(2), 247-268. doi:10.1080/13562510120045221
* ↑ Brownlee, J., Purdie, N., & Boulton-Lewis, G. (2001). Changing Epistemological Beliefs in Pre-service Teacher Education Students. Teaching In Higher Education, 6(2), 247-268. doi:10.1080/13562510120045221
* ↑ Brownlee, J., Purdie, N., & Boulton-Lewis, G. (2001). Changing Epistemological Beliefs in Pre-service Teacher Education Students. Teaching In Higher Education, 6(2), 247-268. doi:10.1080/13562510120045221
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Edwards, K. (1990). The interplay of affect and cognition in attitude formation and change. Journal Of Personality And Social Psychology, 59(2), 202-216. doi:10.1037/0022-3514.59.2.202
* ↑ Stohlmann, M. M., Cramer, K. C., Moore, T. T., & Maiorca, C.C. (2014). Changing Pre-service Elementary Teachers' Beliefs about Mathematical Knowledge. Mathematics Teacher Education & Development, 16(2), 4-24
* ↑ Stohlmann, M. M., Cramer, K. C., Moore, T. T., & Maiorca, C.C. (2014). Changing Pre-service Elementary Teachers' Beliefs about Mathematical Knowledge. Mathematics Teacher Education & Development, 16(2), 4-24
* ↑ Stohlmann, M. M., Cramer, K. C., Moore, T. T., & Maiorca, C.C. (2014). Changing Pre-service Elementary Teachers' Beliefs about Mathematical Knowledge. Mathematics Teacher Education & Development, 16(2), 4-24
* ↑ Stohlmann, M. M., Cramer, K. C., Moore, T. T., & Maiorca, C.C. (2014). Changing Pre-service Elementary Teachers' Beliefs about Mathematical Knowledge. Mathematics Teacher Education & Development, 16(2), 4-24
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Sternberg, R. J. (2014). Teaching about the nature of intelligence. Intelligence, 42176-179. doi:10.1016/j.intell.2013.08.010
* ↑ Sternberg, R. J. (2014). Teaching about the nature of intelligence. Intelligence, 42176-179. doi:10.1016/j.intell.2013.08.010
* ↑ Sternberg, R. J. (2014). Teaching about the nature of intelligence. Intelligence, 42176-179. doi:10.1016/j.intell.2013.08.010
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lindif.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Sternberg, R. J. (2014). Teaching about the nature of intelligence. Intelligence, 42176-179. doi:10.1016/j.intell.2013.08.010
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Dweck, C. S., Chiu, C., & Hong, Y. (1995). Implicit theories and their role in judgments and reactions: A world from two perspectives. Psychological Inquiry,6(4), 267-285. doi:10.1207/s15327965pli0604_1
* ↑ Dweck, C. S., Chiu, C., & Hong, Y. (1995). Implicit theories and their role in judgments and reactions: A world from two perspectives. Psychological Inquiry,6(4), 267-285. doi:10.1207/s15327965pli0604_1
* ↑ Dweck, C. S., Chiu, C., & Hong, Y. (1995). Implicit theories and their role in judgments and reactions: A world from two perspectives. Psychological Inquiry,6(4), 267-285. doi:10.1207/s15327965pli0604_1
* ↑ Dweck, C. S., Chiu, C., & Hong, Y. (1995). Implicit theories and their role in judgments and reactions: A world from two perspectives. Psychological Inquiry,6(4), 267-285. doi:10.1207/s15327965pli0604_1
* ↑ Dweck, C. S., Chiu, C., & Hong, Y. (1995). Implicit theories and their role in judgments and reactions: A world from two perspectives. Psychological Inquiry,6(4), 267-285. doi:10.1207/s15327965pli0604_1
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Furnham, A. (2014). Increasing your intelligence: Entity and incremental beliefs about the multiple 'intelligences'. Learning And Individual Differences,32163-167. doi:10.1016/j.lind'if.2014.03.001
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Chen, W., & Wong, Y. (2015). Chinese mindset: Theories of intelligence, goal orientation and academic achievement in Hong Kong students. Educational Psychology, 35(6), 714-725. doi:10.1080/01443410.2014.893559
* ↑ Wang, Q., & Ng, F. F. (2012). Chinese students' implicit theories of intelligence and school performance: Implications for their approach to schoolwork. Personality And Individual Differences, 52(8), 930-935. doi:10.1016/j.paid.2012.01.024
* ↑ Wang, Q., & Ng, F. F. (2012). Chinese students' implicit theories of intelligence and school performance: Implications for their approach to schoolwork. Personality And Individual Differences, 52(8), 930-935. doi:10.1016/j.paid.2012.01.024
* ↑ Wang, Q., & Ng, F. F. (2012). Chinese students' implicit theories of intelligence and school performance: Implications for their approach to schoolwork. Personality And Individual Differences, 52(8), 930-935. doi:10.1016/j.paid.2012.01.024
* ↑ Wang, Q., & Ng, F. F. (2012). Chinese students' implicit theories of intelligence and school performance: Implications for their approach to schoolwork. Personality And Individual Differences, 52(8), 930-935. doi:10.1016/j.paid.2012.01.024
* ↑ Wang, Q., & Ng, F. F. (2012). Chinese students' implicit theories of intelligence and school performance: Implications for their approach to schoolwork. Personality And Individual Differences, 52(8), 930-935. doi:10.1016/j.paid.2012.01.024
* ↑ Wang, Q., & Ng, F. F. (2012). Chinese students' implicit theories of intelligence and school performance: Implications for their approach to schoolwork. Personality And Individual Differences, 52(8), 930-935. doi:10.1016/j.paid.2012.01.024
* ↑ Snyder, C. R., Hoza, B., Pelham, W. E., Rapoff, M., Ware, L., Danovsky, M., et al. (1997). The development and validation of the Children’s Hope Scale. Journal of Pediatric Psychology, 22, 399–421.
* ↑ Adelabu, D. H. (2008). Future time perspective, hope, and ethnic identity among African American adolescents. Urban Education, 43, 347–360.
* ↑ Mellard, D. d., Krieshok, T., Fall, E., & Woods, K. (2013). Dispositional factors affecting motivation during learning in adult basic and secondary education programs. Reading & Writing, 26(4), 515-538. doi:10.1007/s11145-012-9413-4.
* ↑ Snyder, C R. Journal of Counseling and Development : JCD73.3 (Jan 1995): 355
* ↑ Bernardo, A. B. I. (2010). Extending hope theory: Internal and external locus of trait hope. Personality and Individual Differences, 49, 944–949. doi:10.1016/j.paid.2010.07.036.
* ↑ Snyder, C. R., Harris, C., Anderson, J. R., Holleran, S. A., Irving, L. M., Sigmon, S. T., et al. (1991). The will and the ways: Development and validation of an individual-differences measure of hope. Journal of Personality and Social Psychology, 60(4), 570–585
* ↑ Ryzin, M. M. (2011). Protective Factors at School: Reciprocal Effects Among Adolescents' Perceptions of the School Environment, Engagement in Learning, and Hope. Journal Of Youth & Adolescence, 40(12), 1568-1580. doi:10.1007/s10964-011-9637-7
* ↑ Adelabu, D. H. (2008). Future time perspective, hope, and ethnic identity among African American adolescents. Urban Education, 43, 347–360
* ↑ Adelabu, D. H. (2008). Future time perspective, hope, and ethnic identity among African American adolescents. Urban Education, 43, 347–360
* ↑ Ryzin, M. M. (2011). Protective Factors at School: Reciprocal Effects Among Adolescents' Perceptions of the School Environment, Engagement in Learning, and Hope. Journal Of Youth & Adolescence, 40(12), 1568-1580. doi:10.1007/s10964-011-9637-7
* ↑ Snyder, C R. Journal of Counseling and Development : JCD73.3 (Jan 1995): 355
* ↑ Ryzin, M. M. (2011). Protective Factors at School: Reciprocal Effects Among Adolescents' Perceptions of the School Environment, Engagement in Learning, and Hope. Journal Of Youth & Adolescence, 40(12), 1568-1580. doi:10.1007/s10964-011-9637-7
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson
* ↑ Addison, B. B. (2012). Academic care, classroom pedagogy and the house group teacher: ‘making hope practical’ in uncertain times. Pastoral Care In Education, 30(4), 303-315. doi:10.1080/02643944.2012.688064
* ↑ Addison, B. B. (2012). Academic care, classroom pedagogy and the house group teacher: ‘making hope practical’ in uncertain times. Pastoral Care In Education, 30(4), 303-315. doi:10.1080/02643944.2012.688064
* ↑ Ryzin, M. M. (2011). Protective Factors at School: Reciprocal Effects Among Adolescents' Perceptions of the School Environment, Engagement in Learning, and Hope. Journal Of Youth & Adolescence, 40(12), 1568-1580. doi:10.1007/s10964-011-9637-7
* ↑ Phan, H. H. (2013). Examination of Self-Efficacy and Hope: A Developmental Approach Using Latent Growth Modeling. Journal Of Educational Research, 106(2), 93-104. doi:10.1080/00220671.2012.667008
* ↑ Snyder, C. R., Hoza, B., Pelham, W. E., Rapoff, M., Ware, L., Danovsky, M., et al. (1997). The development and validation of the Children’s Hope Scale. Journal of Pediatric Psychology, 22, 399–421.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Dahl, T., Bal., M.,& Turi, A.L. (2005) Are Student’s beliefs about knowledge and learning associated with their reported use of strategies? British Journal of Educational Psychology 75, 257–273
* ↑ Schommer, M. 1993. Epistemological development and academic performance among secondary students. Journal of Educational Psychology, 85: 406 – 411
* ↑ Dahl, T., Bal., M.,& Turi, A.L. (2005) Are Student’s beliefs about knowledge and learning associated with their reported use of strategies? British Journal of Educational Psychology 75, 257–273
* ↑ Dweck, C. S., & Leggett, E. L. (1988). A socio-cognitive approach to motivation and personality Psychological Review, 95, 256–273
* ↑ Dahl, T., Bal., M.,& Turi, A.L. (2005) Are Student’s beliefs about knowledge and learning associated with their reported use of strategies? British Journal of Educational Psychology 75, 257–273
* ↑ Hofer, B. K., & Pintrich, P. R. (1997). The development of epistemological theories: Beliefs about knowledge and knowing and their relation to learning. Review of Educational Research, 67,88–140
* ↑ Bruning, R., Schraw, G. & Norby, M. (2011). Cognitive psychology and instruction. Boston, MA: Allyn & Bacon/Pearson.
* ↑ Hardy,C.,&Tolhurst,D.,(2014). Epistemological Beliefs and Cultural Diversity Matters in Management Education and Learning: A critical review and Future Direction. Academic of Management Learning & Education, 13(2) 265-289
* ↑ Hardy,C.,&Tolhurst,D.,(2014). Epistemological Beliefs and Cultural Diversity Matters in Management Education and Learning: A critical review and Future Direction. Academic of Management Learning & Education, 13(2) 265-289
* ↑ Tweed, R. G., & Lehman, D. R. 2002. Learning considered within a cultural context. Confucian and Socratic approaches.American Psychologist, 57, 89 –99.
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Bruning, R., Schraw, G., & Norby, M. (2010). Cognitive psychology and instruction (5th ed). Pearson Merrill Prentice Hall, Upper Saddle River, NJ. ISBN: 978-0132368971
* ↑ Jones, B. D., Byrd, C. N., & Lusk, D. (2009). High school students’ beliefs about intelligence. Research In The Schools, 16(2), 1-14.
* ↑ Jones, B. D., Byrd, C. N., & Lusk, D. (2009). High school students’ beliefs about intelligence. Research In The Schools, 16(2), 1-14.
* ↑ Jones, B. D., Byrd, C. N., & Lusk, D. (2009). High school students’ beliefs about intelligence. Research In The Schools, 16(2), 1-14.
* ↑ Jones, B. D., Byrd, C. N., & Lusk, D. (2009). High school students’ beliefs about intelligence. Research In The Schools, 16(2), 1-14.
* ↑ Zhao, Q., Zhang, J., & Vance, K. (2013). Motivated or paralyzed? Individuals' beliefs about intelligence influence performance outcome of expecting rapid feedback
* ↑ Zhao, Q., Zhang, J., & Vance, K. (2013). Motivated or paralyzed? Individuals' beliefs about intelligence influence performance outcome of expecting rapid feedback
* ↑ Bendixen, L.D.(2002).A process model of Epistemic belief change. In Hofer, B.K., and Pintrich PR.(eds). Personal Epistemology: The
* ↑ Erdamar, G., & Alpan, G., (2013) Examining the epistemological beliefs and problem solving skills of preservice teachers during teaching practice. Teaching in Higher Education, 18 (2), 129-143
* ↑ Zhao, Q., Zhang, J., & Vance, K. (2013). Motivated or paralyzed? Individuals' beliefs about intelligence influence performance outcome of expecting rapid feedback
* ↑ Braten.L.,& Ferguson. L.,(2015). Beliefs about sources of knowledge predict motivation for learning in teacher education. Teaching and Teacher Education, 13-23
* ↑ Erdamar, G., & Alpan, G., (2013) Examining the epistemological beliefs and problem solving skills of preservice teachers during teaching practice. Teaching in Higher Education, 18 (2), 129-143
* ↑ Positive personal and cultural identity competency profiles. (n.d.). Retrieved March 02, 2016, from https://curriculum.gov.bc.ca/sites/curriculum.gov.bc.ca/files/pdf/PPCICompetencyProfiles.pdf.
* ↑ Dahl, T., Bal., M.,& Turi, A.L. (2005) Are Student’s beliefs about knowledge and learning associated with their reported use of strategies? British Journal of Educational Psychology 75, 257–273

# Technologies and Designs for Learning

In order to best use technology for teaching and learning, teachers and designers need to understand its potential benefits and pitfalls. This chapter examines theories about how cognitive processes are affected by multimedia learning environments and evidence-based principles for designing such environments. The first section introduces cognitive load theory and describes how the cognitive demands of a multimedia environment affect how students learn from it. The second section introduces the four component instructional design model which offers research-based guidance for designing materials and technologies to facilitate learning of complex skills. Finally, this chapter will look at how technology can be used to facilitate collaborative learning.

## Cognitive Load Theory

Cognitive load theory is an important aspect when looking at technology in the educational setting. Cognitive load theory is a theory proposed by John Sweller and focuses on working memory and instruction. Our working memory is only capable of processing a limited amount of information at one time When designing instructional tools working memory’s limitations is something that needs to be kept in mind, especially when factoring technology into instruction. The reason behind this is that if too much information is presented simultaneously working memory can become overloaded will either to fail to take in all of the information being presented or will shut down completely and take in none of the information. Sweller proposed that there are three types of cognitive load: intrinsic, extraneous, and germane. Through understanding the differences between these three types of cognitive load we should be able to analyze how multimedia presentations are helpful for learning or if they cause cognitive load issues

### Intrinsic Cognitive Load

Intrinsic cognitive load refers to mental processing that is essential to completing a task. Intrinsic cognitive load according to Sweller is something that cannot be changed by instructional design but needs to be taken into account by instructional designers Any material that is being learned places intrinsic cognitive load on working memory, the level of difficulty is what changes how much pressure is put on the working memory If a student’s level of expertise is high in the topic being learned then intrinsic cognitive load will still affect working memory just not as much as if the student had little to no knowledge on the topic in question In this case the level of previous knowledge and understanding of the topic in question needs to be taken into account when presenting a class with new information. For example if a person already had some knowledge about oranges, a lesson on the parts of oranges would cause less intrinsic cognitive load than if they didn't know anything about oranges.

### Extraneous Cognitive Load

Extraneous cognitive load is mental processing that does not promote learning and which can be eliminated by changing the design of a task. Extraneous cognitive load is entirely determined by instructional design For example in a multimedia presentation extraneous cognitive load is the sounds, pictures, text, and animations that could be used to present the material. The more that the working memory has to attend to the less likely it is going to retain the information presented Extraneous cognitive load is manageable, good instructional design lessens the load while poor design can increase the load. For example, a teacher is doing a lesson on the life cycle of a butterfly and decides to use a slide show on the smart board. In the slide show the teacher outlines all the relevant information about each part of the cycle but they add an animation of the butterfly evolving through the stages. In this case the extraneous cognitive load would increase because the students have to pay attention to the relevant information while being distracted by the animation.

### Germane Cognitive Load

Germane cognitive load the amount of working memory devoted to processing the amount of intrinsic cognitive load associated with the information presented and is associated only with a learner’s characteristics He notes that germane cognitive load does not cause an independent strain on working memory rather, it is directly associated with intrinsic and extraneous cognitive load levels. For example if we assume that a student’s level of motivation stays constant they have no control over their level of germane cognitive load So what does this have to do with instruction? According to Sweller, this means that if lessons are created to allow working memory to focus on intrinsic cognitive load, by reducing extraneous cognitive load, germane cognitive load is increased and the level of learning increases as well.

### Research and Implications

Intrinsic and extraneous cognitive load are relational, in other words if both are high then working memory can become overloaded The implications are that because only extraneous cognitive load can be controlled instructional designers need to work to keep it low so as not to overload working memory when the intrinsic cognitive load is high According to the theory, in order to reduce extraneous cognitive load we should take advantage of long term memory’s vast capacity by drawing on existing schemas and creating new ones, thereby reducing the strain on working memory These include: presenting goal free problems, useful redundancy, modality, completion problem effect, split attention effect and others
To start, goal free problems were designed by to change student activities to reduce extraneous cognitive load and to encourage schema production They do this by reducing the chance a student will use goal related strategies to try to solve the problem. This is done by changing how a problem is worded so that students don’t limit themselves to trial and error testing which, can take up a lot of working memory’s capacity For example, a math problem asks: a train is traveling at fifty kilometers per hour and travels a distance of 400 kilometers. How long did it take? If a student doesn’t know the correct formula for calculating time when having the above information they will start a trial and error approach to finding the answer, which will increase extraneous cognitive load. However, if the question asked the student to show as many ways as you can to calculate the answer instead it will reduce the extraneous cognitive load on working memory.
The worked example effect is when a person studies already worked examples to learn how to solve a problem, this also reduces the trial and error approach to problem solving because it provides the student a way to create a schema on how to solve these particular types of problems Unlike regular problems, worked problems focus a person’s attention on the steps needed to solve a problem rather than on the problem as a whole, theoretically reducing extraneous cognitive load because nothing else needs to be attended to In this case if a teacher gives students a new equation in math and then proceeds to provide them with a list some of examples where this equation can be used to solve problems the students have a resource to use when it comes to using the equation, which reduces cognitive load.
The theory behind useful redundancy is that if a student is presented with the same information but in different ways they will be more likely to remember it The idea is that because it is the same information just presented in different ways the extraneous cognitive load will lessen because learners choose which way they prefer to attend to the information However, research has since been conducted that brings this claim into question studies have shown that rather than promote deeper learning it lessens it
In a study conducted by Mayer, Heiser, and Lonn a series of experiments were conducted to investigate the redundancy effect in multimedia learning. They define the redundancy effect as a multimedia learning situation where words are presented as text and speech and the learning is hindered by the dual presentation of information In the first experiment 78 college students were tested on retention and transfer of information based on a multimedia presentation on the formation of lightening. The students were divided into four test groups. The no-text/no-seductive-details group received animation and concurrent narration, the text/no-seductive-details group received the presentation with added on screen text that summarized the narration. The no-text/seductive-details group received a presentation that contained text that had irrelevant but entertaining information. The last group received both an on screen text summary and entertaining irrelevant information The results of this first experiment found that the students who received the on screen text summary remembered less on the retention test than those who did not have the on screen text. As well, students who received the seductive details also retained less than those who did not receive seductive details This first experiment falls in line with the theory that over use of details on a multimedia presentation is detrimental to retention of information. They hypothesised that the redundancy effect caused by the on screen text could have been due to the increased cognitive load in the visual channel, or in the auditory channel. The second experiment set out to test this hypothesis by breaking the participants into three groups. The first group contained 36 students who received no added text to the presentation, the second contained 37 students who received a summary of the narration, and the third group contained 36 students who received a presentation with added word for word text of the narration The results showed that the students who received no added text to their presentation remembered more than those who had the added text. They also found that there was no significant difference in retention between the two groups that had added text. The third experiment set out to discover what happens when video clips are added to multimedia presentations. In this experiment the video clips that were added contained information about lightening but that they were not relevant to the specific information presented in the original presentation Thirty eight college students were divided into two groups, the no video clip group and the group that had video clips added to the presentation. They found that the students in the added video group did not remember any more than the no video group but the results failed to reach statistical significance The last experiment conducted looked at whether adding video clips before or after a multimedia presentation boosts interest in the presentation. The results showed that adding video clips to the beginning of a presentation results in students remembering more of the presentation although the results were not statistically significant Overall, this study concluded that adding extra modes of presenting the same information reduced the amount of information that students will retain after seeing a multimedia presentation. When a learner has to divide their working memory to make sense of the information presented the extraneous cognitive load is increased which reduces the amount of information that can be learned. This is especially important when text is added to a presentation. Mayer, Heiser, and Lonn recommend that instructional designers should refrain from adding text when the information is presented orally in multimedia presentations.
Some theorize that those who work on instructional design can go further than only considering ways to reduce extraneous cognitive load. They feel that instructional design can be improved by creating ways to increase germane cognitive load in learners By increasing a learner’s germane cognitive load they feel that the learner’s attention can be directed to the construction of schemas which in turn reduce the strain on working memory during the learning process.

### Summary

In summary Sweller proposes that there are three types of cognitive load and all effect how our working memory is utilized when learning new information. The implications of cognitive load theory on the use of technology in instructional design is that technology can be an effective learning tool as long as guidelines are followed in order to reduce extraneous cognitive load on working memory. In particular teachers need to pay attention to the research conducted on redundancy effect so that they do not overload working memory with redundant information. One way in which technology can be utilized is to present information in ways that help with schema production, which reduces cognitive load by moving information into long term memory.

## Four-Component Instructional Design

Four Component Instructional Design (4C/ID) is an instructional design model developed by van Merriënboer and his colleagues. It prescribes instruction for learning in a complex environment. The 4C/ID model is based on the idea that skills are learned most effectively by using them instead of just reading instructions from a text. It is important that the conditions of learning are similar to what the learner would encounter in real-world applications of the skill, and instruction emphasizes practice rather than information giving. The 4C/ID model consists four components: (1) Learning Task, (2) Supportive Information, (3) Just-in-Time (JIT) Information, and (4) Part-Task Practice (van Merriënboer, 1997;; van Merriënboer & Kirschner, 2007). These tasks are ordered from task difficulty, less complex to more complex. At the beginning of each four components, lots of scaffolding is required, and gradually reduce in amount of scaffolding as learners progress. In this section we will discuss researches and theories about how technology can support this theory of learning.

### (1) Learning Task

Learning Task is represented as circles in Figure 1. Complex learning involves achieving integrated sets of learning goals. The 4C/ID model promotes use of learning tasks that are whole, authentic, and concrete. Learners participating in the online courses, other wise known as technology-based instruction, based on this model it is important to begin learning as a cluster of relatively simple , but meaningful tasks called task classes. It is impossible to provide highly complex learning tasks from the beginning of the training program because this will slow down excessive cognitive overload for the learner. This will lead to learning and performance impairments. Once learners master the simple but necessary components, they progress towards more complex tasks. Complexity of a task is determined by the number of skills involved in task classes, how they relate each other, and amount of knowledge needed to perform them. While there is no increasing difficulty for the learning tasks within one task classes, they do differ with regard to the amount of support provided to learners. This support that a child receives is called scaffolding. Scaffolding is used when needed in situations such as learners moving from the lowest level task classes to the top-level task classes. Dotted lines around the circles in Figure 1 represents the process of selection and development of suitable learning tasks for a child. Eventually, supports and scaffolding fades as a result. Fading support is due to the expertise reversal effect. It is the phenomenon where supports (e.g. coaching) and instructional methods (systematic steps) that works well for novices can have negative effects for advanced learners due to redundancies. It also increases their cognitive load. Learning tasks stimulate learners to construct cognitive schemata by mindfully abstracting away from the concrete experiences that the learning tasks provide. In learning, generalization and discrimination consists schemata to make them more in line with new experiences. According to van Merriënboer, Clark, and Croock these to-be-constructed schemata comes in two forms. Mental Models: that allows reasoning in the domain because they reflect the way in which the learning domain is organized. Cognitive Strategies: guides problem solving in the domain because they reflect the way problems may be effectively approached. Product-oriented and Process-oriented supports are the two ways to applying learning tasks in a classroom setting. Product-oriented support can be divided into highest or lesser degree. Highest product-oriented support is a learning task that provides a case study or worked-out examples that confronts the learner with a given state , a desired goal state, and a solution, intermediate solutions, or both. It is desirable to use accidents, success stories, or stories with unexpected ending to motivate student learning. In these learning tasks, learners are required to answer questions that stimulates deeper processing and the indiction of mental models from the given example materials. By demonstrating a real-life example, learners can get a clear impression of how a particular domain is organized. It is necessary to allow students to come up with their own conclusion/solution. More information can be retrieved from Figure 2. Process-oriented support is also directed towards the problem-solving process itself. A modeling example confronts the learner with an expert who is performing the task while explaining why the task is performed as it is performed. This is a hands-on experience allows children to retrieve information a lot easier than information gathered by reading texts. This method also helps retain information easier than other learning methods. By studying by using the modeling example, learners can get a clear understanding of the systematic approaches and rules of thumb that even professionals use. Thinking aloud may be helpful to bring the hidden mental problem-solving processes as well. Moreover, computer-based learning tools may invite learners to approach the problem at hand as an expert would do.

### (2) Supportive Information

This type of information plays a role in developing complex skill using technology. Learners need information in order to work successfully on nonrecurrent skills (schemata-like controlled processes) aspects of learning tasks and to genuinely learn from those tasks. Procedure-like automatic processes are called recurrent skills in the 4C/ID framework. Complex cognition consists of both nonrecurrent and recurrent skills. Supportive information is provided to help learners master the nonrecurrent aspects of complex cognitive task. It provides a bridge between learners' prior knowledge and the learning tasks. It is the information that teachers typically refer it to the theory and often presented during lectures or in study books . The goal of supportive information is to help learners acquire the different kinds of flexible schemata needed to cope with real life problems. Supportive information plays as an additional to or an elaboration of the previous information and help students to establish factual relationships between newly presented information elements and their prior knowledge. It allows learners to do things that could not be done before. It has been shown that this type of elaboration process produces highly complex schemata that should allow for deeper understanding. Learners may study how databases are organized in order to develop useful mental models. Task performers further develop their mental models and cognitive strategies in order to improve their performance. For example, Tiger Woods makes extensive study of the layout of golf courses to develop mental models of how they are organized. Also by him watching videotapes of his competitors help him develop cognitive strategies of how to approach problems in this world (real-world). It is of utmost importance to stress non-arbitrary relationships. Methods that identify relevant relationships can be used in an expository fashion or in an inquiry fashion. Expository methods allows learners to explicitly present the non-arbitrary relationships. Inquiry methods ask the learners to discover the relationships. Within these two methods, experiential one is the most important relationship. It relates general and abstract knowledge to concrete cases. The 4C/ID model furthermore distinguishes inductive and deductive strategies for presenting supportive information. There are two types of inductive strategies. Inductive-Inquiry Strategy is a method that presents one or more case studies and then asks the learners to identify the relationships between pieces of information illustrated in the case(s). However, this method is very time consuming and requires deep level of understanding although learners have no experience with the skill. Therefore van Merriënboer, Clark, and Croock (2002) does not recommend using this method unless there is enough instructional time available. Inductive-Expository Strategy on the other hand, starts with one or more case studies and then explicitly presents the relationships between pieces of information that were illustrated in the cases. Merriënboer, Clark, and Croock (2002) suggests using this approach by default since this strategy is more reasonable and time effective by starting with concrete, and recognizable case studies that works well for learners with little prior knowledge. Cognitive Feedback is known as a final part of supportive information. This refers to the nonrecurrent aspects of performance since nonrecurrent performances are never correct or incorrect, it is rather more or less effective. Cognitive feedback can only be presented once learners have finished one or more, or all, learning tasks. When feedbacks are well-designed, it should stimulate learners to reflect on the quality of their personal problem-solving processes and founded solutions.

### (3) Just-in-Time (JIT) Information

In contrast to supportive information, JIT information is aimed at the recurrent aspects of complex skills. It is the prerequisite to the learning and performance of recurrent aspects of learning tasks or practice items. Automaticity depends heavily on consistency, and repetitive practice. JIT information gives learners the step-by-step guidance when needed then fades quickly. The goal of JIT information is to make basic, but critical skills as automatic as possible, as soon as possible. Freeing cognitive resources, leading to more automaticity becomes crucial to advanced learners. It also provides a step-by-step knowledge, such as teachers or tutors directing learners almost acting as an assistant looking over their shoulder. JIT information is identical for many learning tasks, therefore it is typically provided during the first learning task for which the skill is relevant. Similarly to scaffolding, JIT information goes through a principle called fading, that is a quick fade as learners gain more expertise in the learning material. Instructional method of JIT information mainly promote complication through restricted encoding of situation-specific knowledge into cognitive rules. These rules are formed through multiple practice and this process is when information is necessary for forming the rules is directly available from our working memory. Applying this into a real-life situation, for instance, when one is learning golf, your coach will preferably explain how to hold a club, taking stances, and making swings out on the driving range while making first drives, and not during a lecture in a classroom. This goes the same for learners in a classroom setting. Information Displays is organized in small units, this is considered to be essential because controlling the number of new information to bear minimum prevent processing overload during practice. In a real-life situation, for instance, a manual for complex machine may explain the steps one by one rather than assuming user's prior knowledge and only stating some of the steps. This approach should directly present information displays when the learners need the information to work on the recurrent aspects of a particular learning task. However, in some situations this approach is not always helpful. Training for a job, for instance, learning aids such as on-line help system, checklists, and manuals are available and readily accessible. This is due to lack of direct presentation of JIT information when necessary. Demonstrations and Instances are the name for elements of the recurrent skill, also known as generalities. Just like rules can be applied in various situations, these are called demonstrations; for concepts, plans, and principle, on the other hand are called instances. Cognitive Feedback is considered as a final part of JIT information which relates to feedback that is provided on the recurrent aspects of performance. This feedback should promote compilation, meaning that if rules are not correctly applied to the situation, learners are said to make an "error". These feedbacks are recommended to be presented as early as possible. This is for learners to correctly input the right information into their working memory. The 4C/ID model genuinely believe that errors are inevitable in learning and it also plays an important role in a sense that learners learn to recognize their own mistakes and errors, and learn how to recover from them.Well-designed feedbacks should inform the learner why there was an error and provide suggestions or hints of how to achieve their goal. It becomes crucial not to give out answers to encourage their learning process.

### (4) Part-Task Practice

Learning tasks are designed to promote schema construction, and also facilitate compilation for recurrent aspects of the complex skills. The last component of 4C/ID model, part-task practice provides additional practice for selected recurrent skills in order to reach required level of automaticity. It is a way of automatizing procedural knowledge more rapidly while circumventing cognitive load problems resulting when learners try to develop skills while simultaneously trying to solve a problem. Expertise is ordinarily a slow-developing process that depends on extending practice to automatize the productions that directly control behavior. JIT information presentation aims at restricted encoding of newly presented information in rules. Learners practice would be supported through appropriate JIT information until they achieve automaticity. van Merriënboer and his associates believed that some part-task practice can help reduce task complexity due to relatively short and spaced periods of it intermixed with work on complex, authentic tasks. This pattern allows the learner to practice sub skills and relate them to the overall task. It is important that practiced items are divergent for all situation/environment that underlying rules can deal with. However, when a high level of automaticity of recurrent aspects are required, learning tasks may provide insufficient repetition to provide necessary amount of strengthening. This is when we need to include additional part-task practice. Other situations such as learning in a general environment, part-task practice is not helpful to complex learning. Part-task practice promotes the compilation of procedures or rules and specially their subsequent strengthening. These are a very slow process that requires numerous practice items. Examples for part-task practice are multiplication tables or playing scales on musical instruments. It becomes critical to start part-task practice within an appropriate cognitive context since it has been found effectively only after learners were exposed to an easier version of the complex skill. Task hierarchy indicates that either, they enable the performance of many other skills higher in the hierarchy, or it has to be performed simultaneously with many other coordinate skills. Therefore, one should identify the first task class then initiate part-task practice.Practice Items for par-task practice encourages learners to practice number of times just like the saying, "Practice makes perfect". However, learners have to keep in mind that the whole set of practice items should be divergent, and be applicable in all situations. This will help develop a broad set of situation-specific rules. In cases such as highly complex algorithms, it may be necessary to work from simple to complex practice items to decompose it into parts then gradually combine towards the whole task. This approach is called a Part-Whole Approach. Right use of part-task practice will lead to accurate performance of a recurrent skill. Furthermore, extensive amount of overtraining may be necessary to make the skill fully automatic. For tasks that highly relies of automaticity, sometimes the ultimate goal is not be accurate. It is common, in such cases, that acceptable accuracy combined with high speed and performance skills as a whole is the goal. In order to reach this, the recurrent skills are first practiced under speed stress, then once the speed criteria is reached, the skill is practiced under time-sharing condition. Only then, the skll is practiced in the context as a whole task. In other words, performance criteria gradually change from accuracy, to accuracy combined with speed, to accuracy combined with speed under time-sharing conditions or high overall workload. It is suggested that short, spaced periods of part-task practice or overtraining has better results than long, concentrated periods of part-task practice. Part-task practice is best intertwined with the learning tasks because this provides distrubuted practice and also enables the learners to relate the recurrent constituent skill to the whole complex skill.

### Research and Implementation

Recent study on the effectiveness of learning environments using one-by-one-by-two pretest-posttest quasi-experimental design from Frederick K. Sarfo and Jan Elen (2007) concluded that 4C/ID method combined with Information and Communication Technology (ICT) showed the best result in learning gains. The dependent variable was the learning gain which was calculated by subtracting pretest score from posttest scores. The independent variable was the tree treatment conditions. Three groups compared were; regular method of teaching vs 4C/ID learning environment with ICT vs 4C/ID learning environment without ICT. The sample consisted of 129 students selected from six Secondary Technical School in Ghana with the age mean of 18 and Standard Deviation of 1.3 years. Assessment tasks consisted of 26 pretest and posttest items; 13 retention and 13 transfer test. Result revealed a statistically significant difference between student's pretest and posttest in all three groups. With average pretest across all groups being 6.28, the average posttest across all groups were 14.39. Taking a closer look into the data presented by Frederick K. Sarfo and Jan Elen (2007), study claim that 4C/ID learning environment with ICT scored higher in both pretest and posttest. Researchers conclude that these results indicates that the experimental group was better able to solve problems that required reasoning, reflection and recall of procedures, facts and concepts.
Using this Four Component Instructional Design in a classroom setting will help students learn better specially in a complex environment. In order to apply this model, teachers who are teaching the material should be an expert in the field. This will help answer all questions that students may have and helps children understand the course material deeper. Additional support from media or technology specialist may be required. Most importantly, in this model, it becomes essential for teachers-students, and student-student to work as a collaborative team.

### Summary

Four Component Instructional Design model is based on research on cognitive learning and expertise. It provides a framework for designing technology systems for developing complex skills. According to the model, experiences should be realistic and increasingly more authentic tasks; such as projects, cases, and scenarios. Instructions given to the learner should focus on practice and not information giving. These components will be practiced until one achieves the required level of automaticity, without any scaffolding. Once children accomplished all four components, it can be said the one mastered the knowledge or activities. Most importantly, the 4C/ID model does not propagate the idea of errorless learning. The 4C/ID model should be used to develop training programs for complex skills and when transfer is the overarching learning outcome. This model is not developed for teaching conceptual knowledge or procedural skills, and not useful for designing very short programs. Despite all these studies, further research continues on Four Component Instructional Design model.

## Collaborative Learning

Learning collaboratively through pieces of technology systems
As technology is becoming more advanced so are its uses in which individuals can gain and share information. Collaborative learning which is sharing and learning knowledge through peers/groups has become a focal point for different interactions through technology systems. Social interactions are an important factor in cognitive growth. Student interactions with their peers and teacher are among the most important of these exchanges. However, a question that comes up is how can technology help or incorporate these types of interactions. Ways in which good technology design can help students is to note how our cognitive system works, these are things such as attention, working memory, and long term-memory as well as how complex cognitive skills develop. An example of this is the need for supportive and JIT (just-in-time) information, coaching, and scaffolding for effective learning strategies. A key thing to remember is that a good design system works with our cognitive systems. This section will be broken down into different models of technology designs and how collaborative learning may or may not be effective within these systems, The different models in which it will be broken down are learning through/from experts, learning with peers, learning through inquiry, learning through creation, and learning through games. Collaborative learning is seen as a great tool for teachers and students when it comes to education and information being taught or shared. It allows students to experience what it is like working with other peers. However, although it can be seen as a great system to have and incorporate in classrooms it does have some flaws and are still being fully developed to be used in the most effective ways for teachers and students. Teachers should not heavily rely on these types of technology systems but they can be useful and informative.
One of the first things to consider when discussing various systems of technology and their possible implications is how do people learn? It is often seen that many students have trouble with learning information because they are more focused on memorizing rather than understanding. However, Nobel Laureate Herbert Simon said a great piece that “the meaning of ‘knowing’ has shifted from being able to remember and repeat information to being able to find and use it.” In order for students to develop a better understanding in subject matter they must have a deep foundation for factual knowledge, understanding facts and ideas in the context of a conceptual framework and organize knowledge in ways that facilitate retrieval and application. What does this mean exactly in terms of knowledge? Having a deep foundation of factual knowledge, students are aware of the information that is true and relevant to what they are learning. Understanding facts and ideas in the context of a conceptual framework, meaning students understand the material in the context that it is placed in, and how it relates to that topic. Organize knowledge in ways that facilitate retrieval and application, is helping students take that knowledge that they have or are learning and being able to apply it to other areas or topics. These requirements do however have some difficulties when it comes to implementing them in class or within the curriculum. It becomes difficult for teachers because students come into the classroom with these preconceived notions about what they already know. As well as, teachers have a set amount of information they need to teach, it becomes difficult when they have to go into depth in every topic or re-teach certain areas multiple times. As students progress through their school careers many teachers believe they are taught certain material from the previous years, this is not always the case. Some students may feel like they are behind, or are too afraid to ask questions and ask for help. This is where the incorporation of technology systems may be able to help or at least ease the pressure off teachers and allow students to use them within the classroom or on their on time. Now these technology systems are not to take the place of the teacher but rather complement the teacher's lesson. It is not their job to be the foundation for learning but instead they can act as a review to help students with exams or projects. Teachers who rely too heavily on these technology systems may lose a lot of material and interaction that the students can only receive from a physical being. Believing that technology can take the place of teachers is not the proper way of looking at the systems that are being created, they should be viewed as more of a tool to help aid those who take advantage of using them.
To give a brief descriptions about what these systems are-
Learning from Experts: Cognitive Tutors & Telementoring
The first type of technology system is learning from experts, two examples of this are known as cognitive tutors and telementoring. Cognitive tutors, is “a type of intelligent tutor that supports ‘guided learning by doing’”. Cognitive tutors are based around John Anderson’s ACT theory. This theory contains three main principles, the first one is procedural-declarative distinction, the second one is knowledge compilation, and the third one is strengthening through practice. The main focus of cognitive tutors is to monitor students learning as well as provide them with context specific feedback when a student needs it. The primary focus for cognitive tutors are in the areas of mathematics and computer programming. This is able to help students get a better understanding of material while working at their own pace they can also work with others to solve and work through the problems together. One study that was done with cognitive tutors, was a study done by Kenneth R. Koedinger, called Intelligent Tutoring Goes To School in the Big City. In this study “The Pittsburgh Urban Mathematics Project (PUMP) [had] produced an algebra curriculum that is centrally focused on mathematical analysis of real world situations and the use of computational tools. We have built an intelligent tutor, called PAT, that supports this curriculum and has been made a regular part of 9th grade Algebra in 3 Pittsburgh schools. PAT was useful because it was able to help students who had difficulty learning in classrooms. In the 1994-95 school year, the PAT curriculum expanded to include 10 lessons and 214 problem situations. Students are in the computer lab two days a week, working with PAT at a self-paced rate. Student time on the tutor will more than double (roughly from 25 to 70 days) compared to the 93-94 school year.” Telementoring or better known as ‘e-mentoring’ or ‘online-mentoring’ provides students with the opportunity to work with another individual with problems they may be having with course material. Mentoring interactions occur with problems that students are having and questions that they think of. A downfall to telementoring is that students do not get to work with the same adult over and over again. Although they are collaborating they do not get to build a connection with the mentor as some students do with teachers. They do not get the physical one on one interaction with a teacher and the connection is different in comparison to virtually speaking/learning from an individual. This can also be a pitfall with cognitive tutors and learning through any software is not building a relationship with a teacher or mentor and feeling as if there is a disconnect.
Learning with Peers: Knowledge Forum & Starburst
Knowledge forum is a collaboration platform for students to build upon ideas. It places emphasis on community rather than the individual. Knowledge forum is a place where students or individuals can create databases where knowledge is built, this is where collaboration is highly involved. The main components of knowledge forums are what are known as notes and views. A view is a way to organize the notes made by individuals, this can take the shape of a concept map, a diagram, or anything that visually adds structure. the notes appear within these structures. This is also great because it involves the concept of visually learning as well because through the diagrams and maps students are able to connect ideas and see how the connections are made. This is a way for students to all work together on a topic and provide information on a database that can continuously grow. However, knowledge forums are not the only place students and individuals should get their knowledge experience. Learning material through books, and lectures, as well as going on field-trips allows individuals to get a better understanding and perspective. Knowledge forum is just a database where the topic is shaped and evolves. Similar to knowledge forum starburst also provides a place for students to collaborate with others in sharing ideas through a database. However starburst the ideas spread out like a web getting larger and larger. These two systems mainly focus on peer interactions and collaboration among individuals in order for knowledge to build and grow. A study that was shown using knowledge forums was done by Carol and Yuen Yan Chan. In their study, which is taken directly from their article written: “The sample includes 521 secondary school students in Forms One to Six (ages 12–17) from eight secondary schools in Hong Kong. These participants were involved in a research project on computer-supported knowledge building. The sample includes 322 male and 199 female students, with 216 from junior high (Grades 7–9, ages 12–14) and 305 from senior high schools (Grades 10–12, ages 15–17). Students in Hong Kong are streamed into different bands according to their academic achievements; there were 267 students from high-band schools and 254 students from low-band schools.This study took place in the context of a University-School Partnership project on developing knowledge-building pedagogy for elementary and secondary teachers in Hong Kong. The context of the project included university researchers/mentors providing professional development to teachers. There were regular workshops throughout the year to help teachers better understand knowledge-building epistemology and pedagogy; groups of project teachers meeting to plan their curricula collectively; and classroom visits with university researchers and teachers. Regarding knowledge building pedagogy, in a typical knowledge-building classroom, students usually start by identifying areas of inquiry and putting forth their ideas and questions, ‘making ideas public’ for collective improvement is emphasized. In Asian classrooms, it is particularly important for students to experience working together as a community. In this project, classroom and online discourse were integrated, with students contributing notes to Knowledge Forum as they engaged in collaborative inquiry – posing questions, putting forth ideas and theories, building on others’ ideas, and co-constructing explanations to advance their collective knowledge. Data were collected from two questionnaires examining students’ views of collaboration and online learning, and their preferred approaches to learning. After examining the questionnaire data, we excluded items on online learning that showed variable responses, and focused on the questionnaire items on knowledge-building and approaches to learning. We also employed students’ usage statistics on Knowledge Forum derived from Analytic Toolkit to examine their online forum participation.The questionnaire, comprising 12 items, written in Chinese, examined students’ views of collaboration aligned with the notion of knowledge building. Students were asked to use a 5-point Likert scale to rate the questionnaire items that reflected their experience of collaboration while working on knowledge building. In assessing these items, the students could refer to both face-to-face and online collaboration To measure students’ online forum participation, Analytic Toolkit was used to retrieve and analyze summary statistics on individual students’ activity in Knowledge Forum. Analytic Toolkit Version 4.6 provides up to 27 analyses to show how students interact with each other in the Knowledge Forum database. We selected several of the most frequently employed indices from previous studies, including those that have been grouped into overall indices with good construct validity with quality of forum writing (e.g., van Aalst & Chan, 2007; Lee et al., 2006; Niu & van Aalst, 2009). The indices are as follows: (i) Number of notes written: This is included because it is the most commonly used index for measuring online participation. (ii) Scaffolds: This index refers to the number of scaffolds (thinking prompts) used. Knowledge Forum includes scaffolds such as “I need to understand”, “a better theory”, and “putting our knowledge together”. Scaffolds help students to frame ideas and to signpost their ideas to others for interaction and dialogue. (iii) Revision: Students’ attempts to revise their notes are recorded. From a knowledge-building perspective, revision shows a deeper approach to working with ideas. Instead of employing a linear approach, ideas are revisited and revised based on the contributions of the community. (iv) Number of notes read: The number of notes read has been considered important for assessing community awareness; one cannot engage in dialogue without knowing what others have written (Zhang et al., 2009). (v) Number of build-on notes: This index is different from the number of posted notes, and refers to responses to previous notes. This index provides more information about interaction among participants. (vi) Keywords: Students can include “keywords” when they write notes on Knowledge Forum. Other participants can use these keywords to search for related notes on similar topics. The use of keywords reflects domain knowledge and community awareness as students try to make their work more accessible to other members.”
Learning through Inquiry: Anchored Instruction & WISE
The best example of anchored instruction is known as The Adventures of Jasper Woodbury Series. These series are complex video-based problems and it was created so that each of the Jasper adventures are focused on a complex math-oriented problem that needs to be solved. Because such math problems are very complex they are often too difficult to solve alone. While working together, students are able to come up with more than one right solution, and are needed to provide evidence as to why they think theirs is correct. This involves collaboration among the students to come up with various solutions to the problems given because there is not only one right answer. Another way students can work together to solve problems is through what is called WISE (Web-based Inquiry Science Environment). Students work together in a web-based environment and discuss problems to do with global warming or recycling. With WISE teachers are able to play a supportive role and monitor what the students are providing as solutions. “WISE provides evidence and hints about the topic; notes, visualization, discussion, and assessment tools; and prompts for collaboration, reflection, and design of solutions” The big ideas with anchored instruction are that students are learning by constructing understanding as well as learning in context. There is generative learning that occurs as well and this is where sub-goals are created. The big ideas with a program such as wise are that learning is intentional and students are integrating prior knowledge when answering questions.
Learning through Creation: Scratch
An example of learning through creation is the program Scratch. It is a website that media and visuals is the main component. In scratch students are able to work individually or as a group to make visuals in a program for an online community. They are able to share these visuals with one another. With Scratch the students are in control and are able to think with the use of objects, as well as create something from their own imagination. They can encompass audio alongside their visual creations. Students work together in creating these pieces and can share them in the classroom as part of a project, or teachers can base it off a theme or topic they are learning. This helps students think creatively and work collaboratively. An example of how Scratch can be used by teachers in their lesson plan is imagine you are in your 8th grade history class and for your final project you have to choose a topic that you have learned about within the semester and create a visual representation of it. Whether the project be completed in groups or done individually. You work with other members and decide to use the program Scratch, you begin to create different characters such as wounded men, and soldiers etc. You and your group members discuss ideas and begin to create each idea piece by piece. Slowly the image your group had in their mind is creatively coming to life. You are now able to see the piece of history you learned in a visual picture and you can share it with your other classmates.
Learning through Games: Quest Atlantis
Another way students can work collaboratively is through games, one game in particular is Quest Atlantis. This provides different scenarios and realms for students to venture through as they come across problems and tasks they have to choose from and solve. It is an engaging game however it may not fit in classrooms but rather in the spare time of students. The context is best for providing situated learning; this is learning that takes place from social relationships and connecting prior knowledge to new contexts.
Collaborative learning through the use of computer programs is another great way to get students engaged with materials. It does have setbacks in the ways in which they can be used and incorporated into classrooms. Teachers may not have enough devices for students to use as well as students can become unfocused and begin to just play around with the programs. For the programs that provide hints when there is a problem occurring, students can just continuously be getting hints without even trying. Although there a positive to these technology systems, one must take into considerations the negative implications as well. As mentioned none of these systems should be the primary bases of students learning, instead they should be a supplementary add on for students and teachers to use.

## Glossary

Cognitive Load Theory: a theory proposed by John Sweller and focusses on working memory and instruction.
Cognitive tutors: A type of intelligent tutor that supports ‘guided learning by doing’
Collaborative learning: sharing and learning knowledge through peers/groups
Expertise reversal effect: phase where supports and instructional methods have negative effects on individuals due to increase in cognitive load
Extraneous Cognitive Load is the way working memory is affected by the material is presented
Germane Cognitive Load: the amount of working memory devoted to processing the amount of intrinsic cognitive load associated with the information presented and is associated only with a learner’s characteristics.
Intrinsic Cognitive Load: refers to the way in which information is presented.
Nonrecurrent skills: tasks that are effortful, error-prone, easily overloaded, and require focused attention; =schemata
Recurrent skills: correspond to procedures; they occur with little or no effort, are data-driven, and require little or no conscious attention
Situated learning: learning that takes place from social relationships and connecting prior knowledge to new contexts.
Task classes: principle of working from a simple to complex or meaningful task

## Suggested Readings

* Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn. Washington DC: National Academy Press. (pp. 1-50)
* Chan, C. Chan, Y. (2010). Students’ views of collaboration and online participation in Knowledge Forum. Computers & Education, Vol 57(1), Aug, 2011. pp. 1445-1457
* Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2

## References

* Anderson, J., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned: Journal of the Learning Sciences, 4(2), 167-207.
* Anderson, J. R., Hadley, W. H., Koedinger, K. R., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8, 30-43.
* Barab, S. A., Dodge, T., & Ingram-Goble, A. (2008). Reflexive play spaces: A 21st century pedagogy. Games, Learning, and Society, Cambridge University Press, Cambridge, MA.
* Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* Bollen, L., Harrer, A., Mclaren, B. M., Seawall, J., & Walker, E. (1995) Collaboration and Cognitive Tutoring: Integration, Empirical Results, and Future Direction
* Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn. Washington DC: National Academy Press. (pp. 1-50)
* Craig, S., Gholson, B., & Driscoll, D. (2002). Animated pedagogical agents in multimedia educational environments: Effects of agent properties, picture features and redundancy. Journal of Educational Psychology, 94(2), 428-434. doi:10.1037//0022-0663.94.2.428
* Chan, C. Chan, Y. (2010). Students’ views of collaboration and online participation in Knowledge Forum. Computers & Education, Vol 57(1), Aug, 2011. pp. 1445-1457
* Kevin O'neil, D., & Harris, J. B. (2004). Bridging the perspectives and developmental needs of all participants in curriculum-based telementoring programs. Journal of Research on Technology in Education, 37(2), 111-128
* Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* Scardamalia, M., & Bereiter, C. (2006). Knowledge building: Theory, pedagogy, and technology. In R. K. Sawyer (Ed).The Cambridge handbook of the learning sciences (pp97-118). New York: Cambridge University Press.
* Salisbury, D.F., Richards, B.F., & Klein, D. (1985). Designing practice: A review of prescriptions and recommendations from instructional design theories. Journal of InstructionalDevelopment, 8(4), 9- 19.
* Schnotz, W., & Rasch, T. (2005). Enabling, facilitating, and inhibiting effects of animations in multimedia learning: Why reduction of cognitive load can have negative results on learning. ETR&D Educational Technology Research and Development, 53(3), 47-58.
* Sweller, J. (2010). Element Interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review 22(2), 123-138. doi: 10.1007//s10648-010-9128-5.
* Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* van Merrienboer, J., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development 53(3), 5-13.

## Citations

* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Sweller, J. (2010). Element Interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review 22(2), 123-138. doi: 10.1007//s10648-010-9128-5
* ↑ Sweller, J. (2010). Element Interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review 22(2), 123-138. doi: 10.1007//s10648-010-9128-5
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ van Merrienboer, J., & Ayres, P. (2005). Research on cognitive load theory and its design implications for e-learning. Educational Technology Research and Development 53(3), 5-13
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Mayer, R.E., Heiser, J., & Lonn, S. (2001). Cognitive constraints on multimedia learning: When presenting more material results in less understanding. Journal of Educational Psychology 93(1), 187-198.
* ↑ Sweller, J., van Merrienboer, J., & Paas, F. (1998). Cognitive Architecture and Instructional Design. Educational Psychology Review, 10(3), 251-296. doi:1040-726X/98/0900-0251S15.00/0
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Salisbury, D.F., Richards, B.F., & Klein, D. (1985). Designing practice: A review of prescriptions and recommendations from instructional design theories. Journal of InstructionalDevelopment, 8(4), 9- 19.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* ↑ Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* ↑ Sarfo, F., & Elen, J. (2007). Developing technical expertise in secondary technical schools: The effect of 4C/ID learning environments. Learning Environ Res Learning Environments Research, 207-221. doi:10.1007/s10984-007-9031-2
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Merriënboer, J., Clark, R., & Croock, M. (2002). Blueprints for complex learning: The 4C/ID-model. ETR&D Educational Technology Research and Development, 50(2), 39-64.
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Bransford, J. D., Brown, A. L., & Cocking, R. R. (2000). How people learn. Washington DC: National Academy Press. (pp. 1-50)
* ↑ Bollen, L., Harrer, A., Mclaren, B. M., Seawall, J., & Walker, E. (1995) Collaboration and Cognitive Tutoring: Integration, Empirical Results, and Future Direction
* ↑ Anderson, J., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive tutors: Lessons learned: Journal of the Learning Sciences, 4(2), 167-207.
* ↑ Anderson, J. R., Hadley, W. H., Koedinger, K. R., & Mark, M. A. (1997).Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8, 30-43.
* ↑ Anderson, J. R., Hadley, W. H., Koedinger, K. R., & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education (IJAIED), 8, 30-43.
* ↑ Scardamalia, M., & Bereiter, C. (2006). Knowledge building: theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–119). New York: Cambridge University Press
* ↑ Scardamalia, M., & Bereiter, C. (2006). Knowledge building: theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–119). New York: Cambridge University Press
* ↑ Scardamalia, M., & Bereiter, C. (2006). Knowledge building: theory, pedagogy, and technology. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 97–119). New York: Cambridge University Press
* ↑ Chan, C. Chan, Y. (2010). Students’ views of collaboration and online participation in Knowledge Forum. Computers & Education, Vol 57(1), Aug, 2011. pp. 1445-1457
* ↑ Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.

# Problem Solving, Critical Thinking and Argumentation

We are constantly surrounded by ambiguities, falsehoods, challenges or situations in our daily lives that require our Critical Thinking, Problem Solving Skills, and Argumentation skills. While these three terms are often used interchangeably, they are notably different. Critical thinking enables us to actively engage with information that we are presented with through all of our senses, and to think deeply about such information. This empowers us to analyse, critique, and apply knowledge, as well as create new ideas. Critical thinking can be considered the overarching cognitive skill of problem solving and argumentation. With critical thinking, although there are logical conclusions we can arrive at, there is not necessarily a 'right' idea. What may seem 'right' is often very subjective. Problem solving is a form of critical thinking that confronts learners with decisions to be made about best possible solutions, with no specific right answer for well-defined and ill-defined problems. One method of engaging with Problem Solving is with tutor systems such as Cognitive Tutor which can modify problems for individual students as well as track their progress in learning. Particular to Problem Solving is Project Based Learning which focuses the learner on solving a driving question, placing the student in the centre of learning experience by conducting an extensive investigation. Problem Based Learning focuses on real-life problems that motivate the student with experiential learning. Further, Design Thinking uses a specific scaffold system to encourage learners to develop a prototype to solve a real-world problem through a series of steps. Empathy, practical design principles, and refinement of prototyping demonstrate critical thought throughout this process. Likewise, argumentation is a critical thinking process that does not necessarily involve singular answers, hence the requirement for negotiation in argumentative thought. More specifically, argumentation involves using reasoning to support or refute a claim or idea. In comparison problem solving may lead to one solution that could be considered to be empirical.
This chapter provides a theoretical overview of these three key topics: the qualities of each, their relationship to each other, as well as practical classroom applications.
Learning Outcomes:
* Defining Critical Thought and its interaction with knowledge
* Defining Problem Solving and how it uses Critical Thought to develop solutions to problems
* Introduce a Cognitive Tutor as a cognitive learning tool that employs problem solving to enhance learning
* Explore Project Based Learning as a specific method of Problem Solving
* Examine Design Thinking as a sub-set of Project Based Learning and its scaffold process for learning
* Define Argumentation and how it employs a Critical Though process
* Examine specific methodologies and instruments of application for argumentation

# Critical thinking

Critical thinking is an extremely valuable aspect of education. The ability to think critically often increases over the lifespan as knowledge and experience is acquired, but it is crucial to begin the process of this development as early on as possible. Research has indicated that critical thinking skills are correlated with better transfer of knowledge, while a lack of critical thinking skills has been associated with biased reasoning. Before children even begin formal schooling, they develop critical thinking skills at home because of interactions with parents and caregivers. As well, critical thinking appears to improve with explicit instruction. Being able to engage in critical thought is what allows us to make informed decisions in situations like elections, in which candidates present skewed views of themselves and other candidates. Without critical thinking, people would fall prey to fallacious information and biased reasoning. It is therefore important that students are introduced to critical thought and are encouraged to utilize critical thinking skills as they face problems.

### Defining critical thinking

In general, critical thinking can be defined as the process of evaluating arguments and evidence to reach a conclusion that is the most appropriate and valid among other possible conclusions. Critical thinking is a dynamic and reflective process, and it is primarily evidence-based. Thinking critically involves being able to criticize information objectively and explore opposing views, eventually leading to a conclusion based on evidence and careful thought. Critical thinkers are skeptical of information given to them, actively seek out evidence, and are not hesitant to take on decision-making and complex problem solving tasks. Asking questions, debating topics, and critiquing the credibility of sources are all activities that involve thinking critically. As outlined by Glaser (1941), critical thinking involves three main components: a disposition for critical thought, knowledge of critical thinking strategies, and some ability to apply the strategies. Having a disposition for critical thought is necessary for applying known strategies.
Critical thinking, which includes cognitive processes such as weighing and evaluating information, leads to more thorough understanding of an issue or problem. As a type of reflection, critical thinking also promotes an awareness of one's own perceptions, intentions, feelings and actions.
| Components of Critical Thinking |
| Knowledge | Developing a knowledge base and specific tactics to aid the acquisition of knowledge are more easily controlled through instruction. |
| Inference | Forming connections between an existing knowledge base through the use of Deduction and/or Induction. |
| Evaluation | Analyzing, judging, weighing, making forming moral judgments, criticizing and questioning external information presented as well as one's own knowledge base. |
| Metacognition | The process of "thinking about thinking". This involves the assessment of whether one's own decisions, opinions or beliefs are informed and well supported. |

### Critical thinking as a western construct

In modern education, critical thinking is taken for granted as something that people universally need and should acquire, especially at a higher educational level. However, critical thinking is a human construct - not a scientific fact - that is tied to Ancient Greek philosophy and beliefs.
The link to Ancient Greece relates both to Ancient Greek priorities of logic over emotion, as well as its democratic principles. Various authors, including Elder & Paul, Moon, and Stanlick & Strawser share the view that critical thinking questioning back to the time of Socrates. Likewise, Morgan & Saxton (2006) associate critical thinking with a fundamental requirement of all democratic citizens.
An additional connection with Ancient Greece involves the Socratic Method. The Socratic Method involves a conversation between two or more people in which they ask and answer questions to challenge each other’s theses using logic and reason. Such debates are subject to the issue of objective/subjective dualism in that the purpose of debate is the belief that there is a ‘right answer’, yet the ability to conduct such a debate demonstrates the subjectivity of any thesis.
Because of this strong connection to Ancient Greece, critical thinking is generally considered to be a western construct. This is further amplified another western construct called Bloom’s Taxonomy, which is considered to be the essence of critical thinking in modern education.
Since critical thinking is a human construct, notions of what constitutes critical thinking vary considerably from person to person. Moon (2007) lists 21 common notions of critical thinking provided by people from her workshops, and then provides her own 2-page definition of the term. One view of critical thinking is that it involves a set of skills that enables one to reach defensible conclusions and make decisions in a domain or context in which one has some prior knowledge. Another view is that critical thinking involves the use of systematic logic and reasoning, which while not necessarily producing empirical answers nevertheless uses a rational and scientific approach. Ultimately, Moon concludes that there is no right or wrong definition.

### Critical thinking in other parts of the world

Scholars argue that while the critical thinking construct is linked to western, democratic nations, that does not mean that other non-western cultures do not possess or use similar constructs that involve critical thinking. Instead, “there are different ways or forms of reasoning”; for example, Asian approaches to debates involve finding connections between conflictive arguments in order for such ideas to coexist. This is due to eastern values regarding face-saving. In contrast, western approaches are often viewed as being competitive: attacking the views of others while defending one's own position. Despite this dichotomous generalisation, eastern and western approaches have more similarities than they would first seem. With regards to the diplomatic Asian approach to debating, western approaches also involve compromise and negotiation for the very reason that ideas are often complex and that there can be many ‘right’ answers. Similarly, the extent to which other cultures adopt western notions of critical thinking is determined by cultural values. In Muslim cultures, for example, the value of critical thinking is link to views on the appropriateness of voicing one’s views.

### Disposition and critical thinking

It has been suggested that critical thinking skills alone are not sufficient for the application of critical thinking – a disposition for critical thinking is also necessary. A disposition for critical thought differs from cognitive skills. A disposition is better explained as the ability to consciously choose a skill, rather than just the ability to execute the skill. Having a disposition for critical thinking can include such things as genuine interest and ability in intellectual activities. Perkins et al. (2000) expand on the idea of the necessity for a critical thinking disposition, and indicate three aspects involved in critical thinking disposition: an inclination for engaging in intellectual behaviours; a sensitivity to opportunities, in which such behaviours may be engaged; and a general ability for engaging in critical thought. Halpern (1998) suggests that this critical thinking disposition must include a willingness to continue with tasks that seem difficult, openmindedness, and a habit of planning. In fact, in a cognitive skills study conducted by Clifford et al. (2004), they discovered that a disposition for critical thinking was associated with better overall critical thinking skills.
These are characteristics of one's attitude or personality that facilitate the process of developing CT skills:
* Inquisitive
* Systematic
* Judicious
* Truthseeking
* Analytical
* Open-minded
* Confidence in reasoning
There are many factors that can influence one's disposition towards CT; the first of these is culture. There are many aspects of culture that can impact the ability for people to think critically. For instance, religion can negatively impact the development of CT. Many religions are founded upon faith, which often requires wholehearted belief without evidence or support. The nature of organized religion counters the very premise of CT, which is to evaluate the validity and credibility of any claim. Growing up in an environment such as this can be detrimental to the development of CT skills. This kind of environment can dampen dispositions that question religious views or examine the validity of religion. Another cultural factor that can be detrimental to a CT disposition is that of authority. When a child is raised under the conditions of an authoritarian parenting style, it can be detrimental to many aspects of their lives, but especially to their CT skills, as they are taught not to question the credibility of authority and often receive punishment if they do. This is also applicable in the classroom. Classroom environments that foster a disposition for critical thinking in which teachers who do not foster an atmosphere of openness or allow students to question what they are taught can impact CT development as well. Classrooms where questions are rejected or home environments in which there is a high level of parental power and control can all affect the ability of students to think critically. What is more, students will have been conditioned not to think this way for their entire lives. However, despite these cultural limitations, there are ways in which a disposition for CT can be fostered in both the home and the classroom.
Classroom structure is a primary way in which CT dispositions can be highlighted. Fostering a classroom structure in which students are a part of the decision making process of what they are studying can be very helpful in creating CT dispositions. Such structures help students become invested in what they are learning as well as promote a classroom atmosphere in which students may feel free to question the teacher, as well as other students' opinions and beliefs about different subjects. Allowing the freedom to scrutinize and evaluate information that has been given to students is an effective way of creating a classroom environment that can encourage students to develop CT dispositions. This freedom allows for the students to remain individuals within the larger classroom context, and gives them the power to evaluate and make decisions on their own. Allowing the students to share power in the classroom can be extremely beneficial in helping the students stay motivated and analytical of classroom teachings. Teachers can also employ a variety of techniques that can help students become autonomous in the classroom. Giving students the opportunity to take on different roles can be effective in creating CT dispositions, such as making predictions and contemplating problems. Allowing students to engage with problems that are presented, instead of just teaching them what the teacher or textbook believes to be true, is essential for students to develop their own opinions and individual, though. In addition to this, gathering data and information on the subject is an important part of developing CT dispositions. Doing so allows for students to go out and find resources that they themselves can analyze and come to conclusions on their own. Using these aspects of CT students can most effectively relate to the predictions that were first made and critique the validity of the findings.

### Self-regulation and critical thinking

In conjunction with instructing CT, teachers also need to keep in mind the self-regulation of their students. Students need to be able to maintain motivation and have a proactive attitude towards their own learning when learning a new skill. In an article by Phan (2010), he argues that self-regulated students that have better goal setting have more personal responsibility for their learning, can maintain their motivation, are more cognitively flexible, and hence are more inclined to utilize CT. Since CT skills are highly reflective, they help in self-regulated learning (SRL), and in turn, self-regulatory strategies aid in developing CT skills. These two cognitive practices are assets to students’ growth and development.
Self-Regulation provides students with the basic meta-cognitive awareness required for proactive learning. This pro-activity allows students to engage in the cognitive processes of CT, such as evaluation, reflection and inference. Through one’s meta-cognitive ability to assess one’s own thoughts, one develops the capability to become autonomous in one’s learning. Instead of having a supervisor overlook every task, the learner can progress at their own pace while monitoring their performance, thereby engaging in SRL. Part of this process would include periodic reflection upon the strategies that one uses when completing a task. This reflection can facilitate the student’s learning by using CT to evaluate which strategies best suit their own learning based on their cognitive needs.
The complex nature of CT suggests that it requires a long developmental process requiring guidance, practice and reinforcement. To facilitate this process, self-monitoring as a first step to self-regulation can jump-start reflective thought through assessing one’s own educational performance. This assessment promotes self-efficacy through generating motivational beliefs about one’s academic capabilities. From there, through practice, students can extend their CT skills beyond themselves and into their educational contexts. With practice, students use their meta-cognitive strategies as a basis for developing CT in the long run.

## Critical thinking strategies

Psychologists and educators have discovered many different strategies for the development of critical thinking. Among these strategies are some that may be very familiar, such as concept maps or Venn diagrams, as well as some that may be less familiar, such as appeal-question stimuli strategies. Concept mapping is particularly useful for illustrating the relationships between ideas and concepts, while Venn diagrams are often used to represent contrasting ideas.

### Venn Diagrams

Venn diagrams are used frequently in elementary grade levels and continue to be used as a contrast/compare tool throughout secondary school. An example of a situation in which a Venn diagram activity may be appropriate is during a science class. Instructors may direct students to develop a Venn diagram comparing and contrasting different plants or animals. Concept maps may be introduced in elementary grades, although they are most often used in the secondary and post-secondary levels. Concept maps are an interactive and versatile way to encourage students to engage with the course material. A key aspect of concept mapping is how it requires students to reflect on previously learned information and make connections. In elementary grades, concept maps can be introduced as a project, while later, possibly in college or university, students may use them as a study strategy. At the elementary level, students can use concept maps to make connections about the characters, settings, or plot in a story they have read. When introducing concept maps, teachers may provide students with a list of words or phrases and instruct the students to illustrate the connections between them in the form of a concept map. Asking questions can also be a simple and engaging way to develop critical thought. Teachers may begin by asking the students questions about the material, and then encouraging students to come up with their own questions. In secondary and post-secondary education, students may use questions as a way to assess the credibility of a source. At the elementary school level, questions can be used to assess students' understanding of the material, while also encouraging them to engage in critical thought by questioning the actions of characters in a story or the validity of an experiment. Appeal-question stimuli, founded by Svobodová, involves a process of students asking questions regarding their reading comprehension.

### Discussions

Using discussions as a way to develop students’ critical thinking skills can be a particularly valuable strategy for teachers. Peer interactions provide a basis for developing particular critical thinking skills, such as perspective taking and cooperation, which may not be as easily taught through instruction. A large part of discussions, of course, is language. Klooster (2002) suggested that critical thinking begins with asking questions. Similarly, Vygotsky has claimed that language skills can be a crucial precursor for higher level thought processes. As children develop larger vocabularies, they are better able to understand reading material and can then begin to think abstractly about the material and engage in thoughtful discussions with peers about what they understood.
Studies have indicated that cross-age peer discussions may be particularly helpful in facilitating the development of critical thinking. Cross-age peer groups can be effective because of the motivation children tend to have when working with peers of different ages. Younger children often look up to the older children as mentors and valuable sources of knowledge and experience, while older children feel a sense of maturity and a responsibility to share their knowledge and experience with younger students. These cross-age peer discussions also provide students with the challenge of tailoring their use of language to the other group members in order to make their points understandable. An example of cross-age peer groups that is relatively common in Canadian schools is the big buddy programs, where intermediate grade students are assigned a primary grade buddy to help over the course of the school year. Big buddies may help their little buddies with projects, advice, or school events. The big buddy/little buddy programs can be effective as younger students look up to their big buddies, and the big buddies feel a responsibility to help their little buddy. One important factor to be considered with cross-age peer discussions, as noted by Hattie (2006), is that these discussions should be highly structured activities facilitated by a teacher in order to ensure that students understand their group responsibilities.

#### The classroom environment

Having an environment that is a safe place for students to ask questions and share ideas is extremely valuable for creating a classroom that encourages critical thinking. It has been suggested that students are more likely to develop a disposition for critical thinking when they are able to participate in the organization and planning of their classroom and class activities. In these classrooms, students are legitimately encouraged by their teacher to engage in the decision making process regarding the functioning of the classroom. It is also important for teachers to model the desired types of critical thought, by questioning themselves and other authorities in a respectful and appropriate manner. Studies have indicated higher levels of cognitive engagement among students in classrooms with teachers who are enthusiastic and responsive. Therefore, teachers should be encouraging and inclusive, and allow student engagement in classroom planning processes when possible.

### Critical questions

Research is increasingly supporting the idea that critical thinking can be explicitly taught. The use of critical questioning in education is of particular importance, because by teaching critical questioning, educators are actively modelling critical thinking processes. One of the key issues with teaching critical thinking in education is that students merely witness the product of critical thinking on the part of the teacher, i.e. they hear the conclusions that the teacher has reached through critical thinking. Whereas an experienced critical thinker uses critical questions, these questions are implicit and not normally verbalised. However, for students to understand critical questioning and critical thinking strategies, the students must see the process of critical thinking. Modelling the formation and sequencing of critical questions explicitly demonstrates the thought process of how one can reach a logical conclusion.
There various methods of teaching critical questioning. The frameworks discussed below are among the most famous of these. All have their own strengths and weaknesses in terms of ease-of-use, complexity, and universality. Each of these methods approaches critical thinking with a specific definition of this human concept. As such, one’s own definition of critical thinking will likely affect one’s receptiveness to a specific critical questioning framework.

#### Socratic Method

One of the key features of western approaches to critical thinking involves the importance of critical questioning, which is linked to the Socratic Method from Ancient Greece traditions. Whether answering existing questions posed or creating new questions to be considered, critical thinking involves questions, whether explicitly / implicitly, consciously / unconsciously. Browne & Keeley (2006) base their definition of critical thinking specifically on the involvement of critical questions.
Answers to critical questions are not necessarily empirical. They may involve reasoning and be logical, but are nevertheless subject to alternative views from others, thus making all views both subjective and objective at the same time. Elder & Paul (2009) separate such critical questions into three categories:
* Questions that have a correct answer, which can be determined using knowledge
* Questions that are open to subjective answers that cannot be judged
* Questions that produce objective answers that are judged based the quality of evidence and reasoning used
Books on critical questioning tend to be influenced heavily by the Socratic Method, and they make a distinction between ‘good’ and ‘bad’ questions. Good questions are those that are relevant to the topic at hand and that take a logical, systematic approach, while bad questions are those that are not relevant to the topic, are superficial, and are sequenced haphazardly. Elder & Paul (2009) argue that “[i]t is not possible to be a good thinker and a poor questioner.” In other words, if a person cannot thinking of relevant and logical questions, they will be unable to reach any rational conclusions.
Additionally, as indicated above, critical thinking requires more than just asking the right questions. There is a direct relationship between critical thinking and knowledge. One can possess knowledge, but not know how to apply it. Conversely, one can have good critical questioning skills, but lack the knowledge to judge the merits of an answer.
In terms of teaching critical questioning using the Socratic Method, it is essential to appreciate that there is no set of questions that one can follow, since the type of critical questions needed is based on the actual context. Consequently, the examples presented by different authors vary quite considerably. Nevertheless, there are specific guidelines one can follow:
* Use critical questions to identify and understand the situation, issues, viewpoints and conclusions
* Use critical questions to search for assumptions, ambiguity, conflicts, or fallacies
* Use critical questions to evaluate the effects of the ideas
Part 1 of the Socratic Method is more of an information gathering stage, using questions to find out essential details, to clarify ideas or opinions, and to determine objectives. Part 2 uses the information from Part 1 and then uses questions to probe for underlying details that could provide reasons for critiquing the accuracy of the idea. Part 3 uses questions to reflect upon the consequences of such ideas.
Conklin (2012) separates the above three parts into six parts:
* Using questions to understand
* Using questions to determine assumptions
* Using questions to discover reasons / evidence
* Using questions to determine perspectives
* Using questions to determine consequences
* Using questions to evaluate a given question
Here are some sample questions for each part:
Questions for understanding:
* Why do you think that?
* What have you studied about this topic so far?
* How does this relate to what you are studying now?
Questions that determine assumptions
* How could you check that assumption?
* What else could be assumed?
* What are your views on that? Do you agree or disagree?
Questions that discover reasons / evidence
* How can you be sure?
* Why is this happening?
* What evidence do you have to back up your opinion?
Questions that determine perspectives
* How could you look at this argument another way?
* Which perspective is better?
Questions that determine consequences
* How does it affect you?
* What impact does that have?
Questions that evaluate a given question
* Why was I asked this question?
* Which questions led to the most interesting answers?
* What other questions should be asked?
Depending on the text, the Socratic Method can be extraordinarily elaborate, making it challenging for educators to apply. Conklin (2012) states that a teacher would need to spend time planning such questions in advance, rather than expect to produce them during a lesson.

#### Bloom’s Taxonomy

Bloom’s Taxonomy was originally designed in 1956 to determine cognitive educational objectives and assess students’ higher-order thinking skills. Since then, though, it has become adapted and used as a useful tool for promoting critical thinking skills, particularly through critical questioning. These critical questions involve Bloom’s categories of understanding, applying, analysing, synthesising and evaluating. Such categories can be seen to relate to the Socratic Method promoted by other authors, i.e. the importance of questioning to understanding, analyse and evaluate. Moon (2007) believes that “‘evaluation’, ‘reflection’ and ‘understanding’” are key aspects of critical thinking, which should therefore appear in any notion of critical thinking. At the same time, Bloom’s Taxonomy generates a natural set of questions that can be adapted to various contexts.
In one example, a teacher uses a picture of a New York speakeasy bar. Using Bloom’s Taxonomy, the teacher could ask and model the following critical questions:
* KNOWLEDGE: What do you see in the picture?
* COMPREHENSION: What do people do in places like that?
* ANALYSIS: Why are there so many policemen in the picture?
* APPLICATION: What similar situations do we see nowadays?
* SYNTHESIS: What if there were no laws prohibiting such behaviour?
* EVALUATION: How would you feel if you were one of these people? Why?

#### Norman Webb’s Depth of Knowledge

Webb’s Depth of Knowledge (DOK) taxonomy was produced in 2002 in response to Bloom’s Taxonomy. In contrast with Bloom’s Taxonomy, Webb’s DOK focuses on considering thinking in terms of complexity of thinking rather than difficulty.
Webb’s DOK has four levels:
* Recall & reproduction
* Working with skills & concepts
* Short-term strategic thinking
* Extended strategic thinking
Level 1 aligns with Bloom’s level of remembering and recalling information. Example critical questions in this level would include:
* What is the name of the protagonist?
* What did Oliver Twist ask Fagin?
Level 2 involves various skills, such as classifying, comparing, predicting, gathering, and displaying. Critical questions can be derived from these skill sets, including the following:
* How do these two ideas compare?
* How would you categorise these objects?
* How would you summarize the text?
Level 3 involves analysis and evaluation, once again aligning with Bloom’s Taxonomy.
* What conclusions can you reach?
* What theory can you generate to explain this?
* What is the best answer? Why?
At the same time, Level 3 of DOK shares similarities with the Socratic Method in that the individual must defend their views.
Level 4 is the most elaborate and challenging level. It involves making interdisciplinary connections and the creation of new ideas / solutions.
Since DOK becomes increasingly elaborate with levels and leads to the requirement to defend one’s position using logic and evidence, there are parallels with the Socratic Method. At the same time, because is used to develop standards in assessing critical thinking, it shares similarities with Bloom’s Taxonomy.

#### Williams Model

The Williams Model was designed by Frank Williams in the 1970s. Unlike other methods, the Williams Model was designed specifically to promote creative thinking using critical questioning. This model involves the following aspects:
* Fluency
* Flexibility
* Elaboration
* Originality
* Curiosity
* Risk taking
* Complexity
* Imagination
Critical questions regarding fluency follow a sort of brainstorming approach in that the questions are designed to generates ideas and options. For ‘flexibility’, the questions are designed to produce variations on existing ideas. ‘Elaboration’ questions are about building upon existing ideas and developing the level of detail. As the name suggests, critical questions for ‘originality’ are for promoting the development of new ideas. The ‘curiosity’ aspect of the Williams Model bears a similarity with that of the ‘Wonder’ stage of the Know Wonder Learn (KWL) system. ‘Risk taking’ questions are designed to provoke experimentation. Although the name ‘complexity’ may sound similar to ‘elaboration’, it is instead about finding order among chaos, making connections, and filling in gaps of information. The final aspect is ‘Imagination’, which involves using questions to visualise.

#### Wiggins & McTighe’s Six Facets of Understanding

Wiggins & McTighe’s ‘Six Facets of Understanding’ are all based on deep understanding aspects of critical thinking. The method is used for teachers to design questions for students to promote critical thinking. The six facets are Explanation, Interpretation, Application, Perspective, Empathy, and Self-Knowledge.
‘Why’ and ‘How’ questions dominate the ‘Explanation’ facet in developing theory and reasoning:
* How did this happen? Why do you think this?
* How does this connect to the other theory?
Interpretation questions encourage reading between the lines, creating analogies or metaphors, and creating written or visual scenarios to illustrate the idea. Questions include:
* How would you explain this idea in other words?
* Why do you think that there is conflict between the two sides?
* Why is it important to know this?
Application questions are about getting students to use knowledge. Part of this comes from predicting what will happen based on prior experience. Another aspect involves learning from the past. Critical questions in this facet include:
* How might we prevent this happening again?
* What do you think will happen?
* How does this work?
Perspective questions involves not only looking at ideas from other people’s perspectives, but also determining what people’s points of views are. In comparison with Empathy questions, though, Perspective questions involve more of an analytical and critical examination. Here are some example questions:
* What are the different points of view concerning this topic?
* Whose is speaking in the poem?
* Whose point of view is being expressed?
* How might this look from the other person’s perspective?
Empathy questions involve perspective-taking, including empathy, in order to show an open mind to considering what it would feel like to walk in another person’s shoes.
* How would you feel in the same situation?
* What would it be like to live in those conditions?
* How would you react if someone did that your family?
Self-knowledge questions are primarily designed to encourage self reflection and to develop greater self awareness. In particular, Self-Knowledge questions reveal one’s biases, values, and prejudices and how they influence our judgment of others. Critical questions in this facet include:
* How has my life shaped my view on this topic?
* What do I really know about the lives of people in that community?
* What knowledge or experience do I lack?
* How do I know what I know? Where did that information / idea come from?
Questions within the Six Facets of Understanding all incorporate the following attributes:
* They are open ended
* They require deep thought
* They require critical thinking
* They promote transfer of knowledge
* They are designed to lead to follow-up questions
* They require answers that are substantiated
For examples of critical questioning in action in a classroom environment, view the External Link section at the bottom of this page.

# Problem Solving

In everyday life we are surrounded by a plethora of problems that require solutions and our attention to resolve them to reach our goals. We may be confronted with problems such as: needing to determine the best route to get to work, what to wear for an interview, how to do well on an argumentative essay or needing to find the solution to a quadratic equation. A problem is present in situations where there is a desire to solve the problem, however the solution is not obvious to the solver. Problem solving is the process of finding the solutions to these problems.. Although they are related, critical thinking differs fundamentally from problem solving. Critical thought is actually a process that can be applied to problem solving. For example, students may find themselves engaging in critical thought when they encounter ill-defined problems that require them to consider many options or possible answers. In essence, those who are able to think critically are able to solve problems effectively.
This chapter on problem solving will first differentiate between Well-defined Problems and Ill-defined Problems, then explain uses of conceptualizing and visually representing problems within the context of problem solving and finally we will discuss how mental set may impede successful problem solving.

### Well-defined and Ill-defined Problems

Problems can be categorized into two types: ill-defined or well-defined Cognitive Psychology and Instruction (5th Ed). New York: Pearson.</ref> to the problem at hand. An example of a well-defined problem is an algebraic problem (ex: 2x - 29 = 7) where one must find the value of x. Another example may be converting the weight of the turkey from kilograms to pounds. In both instances these represent well-defined problems as there is one correct solution and a clearly defined way of finding that solution.
In contrast, ill-defined problems represent those we may face in our daily lives, the goals are unclear and they have information that is conflicting, incomplete or inconclusive. An example of an ill-defined problem may be “how do we solve climate change?” or “how should we resolve poverty” as there is no one right answer to these problems. These problems yield the possibility to many different solutions as there isn’t a universally agreed upon strategy for solving them. People approach these problems differently depending on their assumptions, application of theory or values that they use to inform their approach. Furthermore, each solution to a problem has its own unique strengths and weaknesses..
Ill-Defined versus Well-Defined Problems :
| Ill-Defined | Well-Defined |
| Given state is not clearly specified , unclear goal state, unclear set of allowable procedures and multiple solutions. | Given state is clearly specified, there are clearly specified goals, clearly specified set of allowable procedures and one clear solution. |
| For example: How should we resolve global warming? | For example: 5x=10 |
| Argumentation, attitudes and "metacognition highly predicted problem-solving score | Domain knowledge and justification skills highly predicted problem-solving scores. |
Table 1. Summarizes the difference between well-defined and ill-defined problems.

#### Differences in Solving Ill-defined and Well-defined Problems

In earlier times, researchers assumed both types of problems were solved in similar ways, more contemporary research highlights some distinct differences between processes behind finding a solution.
Kitchener (1983) proposed that well-defined problems did not involve assumptions regarding Epistemological Beliefs because they have a clear and definite solution, while ill-defined problems require these beliefs due to not having a clear and particular solution. In support of this idea, Schraw, Dunkle and Bendixen conducted an experiment with 200 participants, where they found that performance in well-defined problems is not predictive of one's performance on ill-defined problems, as ill-defined problems activated different beliefs about knowledge.
Furthermore Shin, Jonassen and McGee (2003), found that solving ill-defined problems brought forth different skills than those found in well-structured problems. In well-structured problems domain knowledge and justification skills highly predicted problem-solving scores, whereas scores on ill-structured tasks were predictive of argumentation, attitudes and metacognition in an astronomy simulation.
Aligned with these findings, Cho and Jonassen (2002) found that groups solving ill-structured problems produced more argumentation and problem solving strategies due to the importance of considering a wide variety of solutions and perspectives. In contrast, the same argumentation technique distracted the participant's activities when they dealt with well-defined problems. This research highlights the potential differences in the processes behind solving ill-defined and well-defined problems.

##### Implications Of The Classroom Environment

The fundamental differences between well-structured and ill-structured problems implicate that solving ill-structured problems calls for different skills, strategies, and approaches than well-structured problems. Meanwhile, most tasks in the educational setting are designed around engaging learners in solving well-structured problems that are found at the end of textbook chapters or on standardized tests.. Unfortunately the strategies used for well-defined problems have little application to ill-defined problems that are likely to be encountered day to day as simplified problem solving strategies used for the well-structured designs have been found to have almost no similarities to real-life problems
This demonstrates the need to restructure classrooms in a way that facilitates the student problem solving of ill-structured problems. One way we may facilitate this is through asking students questions that exemplify the problems found in everyday life. This type of approach is called problem based learning and this type of classroom structure students are given the opportunity to address questions by collecting and compiling evidence, data and information from a plethora of sources. In doing so students learn to analyze the information,data and information, while taking into consideration the vast interpretations and perspectives in order to present and explain their findings.

###### Structure Of The Classroom

In problem-based learning, students work in small groups to where they explore meaningful problems, identify the information needed to solve the given problem, and devise effective approaches for the solution. Students utilize these strategies, analyze and consider their results to devise new strategies until they have come up with an effective solution. The teacher’s role in this classroom structure is to guide the process, facilitate participation and pose questions to elicit reflections and critical thinking about their findings. In addition teachers may also provide traditional lectures and explanations that are intended to support student inquiry.
In support of the argument to implement a problem-based approach to problem solving, a meta-analysis conducted by Dochy, Segers, Van den Bossche, & Gijbels (2003), found problem-based learning to be superior to traditional styles of learning though in supporting flexible problem solving, application of knowledge, and hypothesis generation. Furthermore, Williams, Hemstreet, Liu, and Smith (1998) found that this approach fostered greater gains in conceptual understanding in science. Lastly Gallagher, Stepien, & Rosenthal (1992), found that in comparing traditional vs. project-based approaches students in problem-based learning demonstrate an ability to define problems. These findings highlight the benefits of problem-based learning on understanding and defining problems in science. Given the positive effects of defining problems this education approach may also be applied to our next sub-topic of conceptualizing problems.

### Steps to Problem Solving

There have been five stages consistently found within the literature of problem solving: (1) identifying the problem, (2) representing the problem, (3) choosing the appropriate strategy, (4) implementing the strategy, and (5) assessing the solutions. This overview will focus on the first two stages of problem solving and examine how they influence problem solving.

#### Conceptualizing Problems

One of the most tedious and taxing aspects of problem solving is identifying the problem as it requires one to consider the problem through multiple lenses and perspectives without being attached to one particular solution to early on in the task. In addition it is also important to spend time clearly identifying the problem due to the association between time spent "conceptualizing a particular problem and the quality of one's solutions". For example consider the following problem:
Becka baked a chocolate cake in her oven for twenty five minutes. How long would it take her to bake three chocolate cakes?
Most people would jump to the conclusion to multiply twenty five by three, however if we place all three cakes in the oven at a time we find it would take the same time to bake three cakes as it would take to bake one. This example highlights the need to properly conceptualize the problem and look at it from different viewpoints, before rushing to solutions.
Taking this one step further, break down the five steps as the would be used to conceptualize the problem:
Stage 1 - Define the Problem
| Goal ( I want too.... | Barrier (but...) |
| Buy a new car. | I am not sure what the most economical model is. |
| Exercise more. | I do not know when I will have time. |
| Get a better job. | I am not sure what type of retraining I will need. |
Stage 2 - Brainstorm Solutions
| Problem | Checking Facts |
| I need to get a new car. | How much money will it take? Do I really need a car or can I take transit? Is it better to buy a new car or used? |
Stage 3 - Pick a Solution
Stage 4 - Implement the Solution
Stage 5 - Review the Result
* Result -
* Was the decision to buy a new car the best solution?
* Decided to buy a new car, saved money to purchase it, and bought a new economical car.
* The new car cost more, but it is reliable and has worked as transportation for a long time.
* Therefore, this was the best solution.
Research also supports the importance of taking one's time to clearly identifying the problem before proceeding to other stages. In support of this argument, Getzel and Csikszentmihalyi found that artist students that spend more time identifying the problem when producing their art were rated as having more creative and original pieces than artists who spent less time at this stage . These researchers postulated that in considering a wider scope of options during this initial stage they were able to come up with more original and dynamic solutions.
Furthermore, when comparing the approaches of experienced teachers and novice post-secondary students studying to be teachers, it was found that experienced teachers spent a greater amount of time lesson planning in comparison to post-secondary students when in a placed in a hypothetical classroom. In addition these teachers offered significantly more solutions to problems posed in both ill-defined and well-defined problems. Therefore it is implicated that successful problem solving is associated with the time spent finding the correct problem and the consideration of multiple solutions.

##### Instructional Implications

One instructional implication we may draw from the literature that supports that the direct relationship between time spent on conceptualizing a problem and the quality of the solution, is that teachers should encourage students to spend as much time as possible at this stage . In providing this knowledge and by monitoring student’s problem solving processes to ensure that they “linger” when conceptualizing problems, we may facilitate effective problem solving .

#### Representing the Problem

Problem Representation refers to how the known information about a particular problem is organized . In abstract representation of a problem, we merely think or speak about the problem without externally visually representing . In representing a problem tangibly this is done by creating a visual representation on paper, computer, etc. of the data though graphs, stories, symbols, pictures or equations. These visual representations may be helpful they can help us keep track of solutions and steps to a problem, which can particularly be useful when encountering complex problems.
For example if we look at Dunker's Buddhist Monk example :
In the morning a Buddhist monk walks outside at sunrise to climb up the mountain to get to the temple at the peak. He reaches the temple just prior to sunset. A couple days later, he departs from the temple at sunrise to climb back down the mountain, travelling quicker than he did during his ascent as he is going down the mountain. Can you show a location along the path that the monk would have passed on both at the exact time of the day?
In solely using abstraction, this problem is seemingly impossible to solve due to the vast amount of information, how it is verbally presented and the amount of irrelevant information present in the question. In using a visual representation we are able to create a mental image of where the two points would intersect and are better able to come up with a solution.
Research supports the benefits of visual representation when confronted with difficult problems. Martin and Schwartz found greater usage of external representations when confronted with a difficult task and they had intermittent access to resources, which suggests that these representations are used as a tool when problems are too complex without external aids. Results found that while creating the initial visual representation itself took up time, those who created these visual representations solved tasks with greater efficiency and accuracy.
Another benefit is that these visual representations may foster problem solving abilities by enabling us to overcome our cognitive biases. In a study conducted by Chambers and Reisberg, participants were asked to look at the image below then close their eyes and form a mental image. When asked to recall their mental image of the photo and see if there were any alternate possibilities of what the photo could be, none of the participants were able to do so. However when participants were given the visual representation of the photo they were quickly able to manipulate the position of the photo to come up with an alternate explanation of what the photo could be. This shows how visual representations may be used in education by learners to counteract mental sets, which will be discussed in the next section.

##### Instructional Implications

As shown above, relying on abstraction can often overload one’s cognitive resources due to short- term memory being limited to seven items of information at a time. Many problems surpass these limits disabling us being able to hold all the relevant information needed to solve a problem in our working memory. Therefore it is implicated that in posing problems teachers should represent them written or visually in order to reduce the cognitive load. Lastly another implication is that as teachers we may increase problem-solving skills through demonstrating to students different types of external representations that can be used to show the relevant information pertaining to the problem. These representations may include different types of graphs, charts and imagery, which all can serve as tools for students in coming up with an effective solution, representing relevant information and reducing cognitive load

### Challenges of Problem Solving

As discussed above there are many techniques to facilitate the problem solving process, however there are factors that can also hinder this process. For example: one’s past experiences can often impede problem solving as they can provide a barrier in looking at novel solutions, approaches or ideas.

#### Mind set

A mind set refers to one's tendency to be influenced by one's past experiences in approaching tasks. Mental set refers to confining ourselves to using solutions that have worked in the past rather than seeking out alternative approaches. Mental sets can be functional in certain situation as in using strategies that have worked before we are quickly able to come up with solutions. However, they can also eliminate other potential and more effective solutions.

#### Functional Fixedness

Functional Fixedness is a type of mental set that refers to our tendency to focus on a specific function of an object (ie. what we traditionally use it for) while overlooking other potential novel functions of that object.
A classic example of functional fixedness is the candle problem. Consider you are at a table with a box full of tacks, one candle, and matches, you are then asked to mount the lit candle on the wall corkscrew board wall as quickly as possible, and make sure that this doesn't cause any wax to melt on the table. Due to functional fixedness you might first be inclined to pin the candle to the wall as that is what tacks are typically used for, similar to participants in this experiment. However, this is the incorrect solution as it would cause the wax to melt on the table.
The most effective solution requires you to view the box containing the tacks as a platform for the candle rather than it's traditional use as a receptacle. In emptying the box, we may use it as a platform for the candle and then use the tacks inside to attach the box to the wall. It is difficult to initially arrive at this solution as we tend to fixate on the function of the box of holding the tacks and have difficulty designating an alternate function to the box (ie. as a platform as opposed to a receptacle). This experiment demonstrates how prior knowledge can lead to fixation and can hinder problem solving.

##### Techniques to Overcome Functional Fixedness

As proposed by McCaffrey (2012), one way to overcome functional fixedness is to break the object into parts. In doing so we may ask two fundamental questions “can it be broken down further” and “does my description of the part imply a use”. To explain this we can use McCaffrey’s steel ring figure-8 example. In this scenario the subject is given two steel rings, a candle and a match, they are asked to make the two steel rings into a figure 8. Looking at the tools provided to the subject they might decide that the wax from the candle could potentially hold the two pieces of steel together when heated up. However the wax would not be strong enough. It leaves them with a problem, how do they attach the two steel rings to make them a figure eight.
In being left with the wick as a tool, and labelling it as such we become fixated on seeing the primary function of the wick as giving off light, which hinders our ability to come up with a solution for creating a figure-8. In order to effectively solve problem we must break down our concept of the wick down further. In seeing a wick as just a waxed piece of string, we are able to get past functional fixedness and see the alternate functions of the string. In doing so we may come to the conclusion and see the waxed string as being able to be used to tie the two rings together. In showing the effectiveness of this approach McCaffrey (2012) found that people trained to use this technique solved 67% more problems than the control group.

###### Instructional Implications

Given the effectiveness of this approach, it is implicated that one way we may promote Divergent Thinking is through teaching students to consider: "whether the object may be broken down further" and "whether the description of the part imply a use" in doing so we may teach students to break down objects to their purest form and make salient the obscure features of a problem. This connects to the previously discussed idea of conceptualization where problem solving effectiveness can be increased through focusing time on defining the problem rather than jumping to conclusions based on our own preconceptions. In the following section we will discuss what strategies experts use when solving problems.

## Novice Versus Expert In Problem Solving

Many researchers view effective problem solving as being dependent on two important variables: the amount of experience we have in trying to solve a particular category of problems, which we addressed earlier by demonstrating that in practicing problem solving through engaging in a problem-based approach we may increase problem solving skills. However, the second factor to consider is the amount of domain-specific knowledge that we have to draw upon. Experts possess a vast amount of domain knowledge, which allows them to efficiently apply their knowledge to relevant problems. Experts have a well-organized knowledge of their domain, which impacts they notice and how they arrange, represent and interpret information, this in turn enables them to better recall, reason and solve problems in comparison to novices.
In comparing experts to novices in their problem strategies, experts are able to organize their knowledge around the deep structure in important ideas or concepts in their domain, such as what kind of solution strategy is required to solve the problem. In contrast novices group problems based on surface structure of the problems, such as the objects that appear in the problem.
Experts also spend more time than novices analyzing and identifying problems at the beginning of the problem-solving process. Experts take more time in thinking and planning before implementing solutions and use a limited set of strategies that are optimal in allowing them to richer and more effective solutions to the given problem.
In addition experts will engage in deeper and more complete problem representation novices, in using external representations such as sketches and diagrams to represent information and solve problems. In doing so they are able to solve problems quicker and come up with better solutions.
Given the literature above it is evident that problem solving and expertise overlap as the key strategies that experts utilize are also provided as effective problem solving strategies. Therefore, we may conclude that experts not only have a vast knowledge of their domain, they also know and implement the most effective strategies in order to solve problem more efficiently and effectively in comparison to novices. In the next section we will discuss the connection between problem solving and critical thinking.

## Cognitive Tutor for Problem Solving

Cognitive Tutor is a kind of Intelligent Tutoring Systems. It can assign different problems to students according to their individual basis, trace users’ solution steps, provide just-in-time feedback and hint, and implement mastery learning criteria.
According to Anderson and colleague, the students who worked with LISP tutors completed the problems 30% faster and 43% outperformed than their peers with the help of teachers in mini-course. Also, college students who employed ACT Programming Tutor (APT) with the function of immediate feedback finished faster on a set of problems and 25% better on tests than the students who received the conventional instruction. In addition, in high school geometry school settings, students who used Geometry Proof Tutor (GPT) for in- class problem solving had a letter grade scores higher than their peers who participated in traditional classroom problem-solving activities on a subsequent test.

### An overview of Cognitive Tutor

In 1985, Anderson, Boyle, and Reigser added the discipline of cognitive psychology to the Intelligent Tutoring Systems. Since then, the intelligent tutoring system adopted this approach to construct cognitive models for students to gain knowledge was named Cognitive Tutors. The most widely used Cognitive Tutor is Cognitive Tutor® Algebra I. Carnegie Learning, Inc., the trademark owner, is developing full- scale Cognitive Tutor®, including Algebra I, II, Bridge to Algebra, Geometry, and Integrated Math I, II, III. Cognitive Tutor® now includes Spanish Modules, as well.
Cognitive Tutors support the idea of learning by doing, an important part of human tutoring, which to provide students the performance opportunities to apply the objective skills or concepts and content related feedback. To monitor students’ performance, Cognitive Tutors adopt two Algorithms, model tracing and knowledge tracing. Model tracing can provide immediate feedback, and give content-specific advice based on every step of the students’ performance trace. Knowledge tracing can select appropriate tasks for every user to achieve mastery learning according to the calculation of one’s prior knowledge.
Cognitive Tutors can be created and applied to different curriculum or domains to help students learn, as well as being integrated into classroom learning as adaptive software. The curriculum and domains include mathematics in middle school and high school, genetics in post-secondary institutions, and programming.
Cognitive Tutors yielded huge impacts on the classroom, student motivation, and student achievement. Regarding the effectiveness of Cognitive Tutors, research evidence supports more effectiveness of Cognitive Tutors than classroom instruction.

### The Theoretical Background of Cognitive Tutor
#### ACT-R theory

The theoretical background of Cognitive Tutors is ACT-R theory of learning and performance, which distinguishes between procedural knowledge and declarative knowledge. According to the ACT-R theory, procedural knowledge cannot be directly absorbed into people’s heads, and it can be presented in the notation of if-then Production rules. The only way to acquire procedural knowledge is learning by doing.

#### Production rules

Production rules characterize how students, whether they beginning learners or advanced learners, think in a domain or subject. Production rules can represent students' informal or intuitive thinking. The informal or intuitive forms of thinking are usually different from what textbook taught, and students might gain such patterns of thinking outside from school. Heuristic methods, such us providing a plan of actions for problem-solving instead of giving particular operation; and non-traditional strategies, such as working with graphics rather than symbols when solving equation, can be represented in production rules as well.

#### Cognitive model and model tracing

Cognitive model is constructed on both ACT-R theory and empirical studies of learners. All the solutions and typical misconceptions of learners are represented in the production system of the cognitive model.
* Three strategies of solving an algebra equation
* The equation: 2(3+X)=10
* Strategy 1: 2x3+2xX=10
* Strategy 2: 2(3+X)÷2=10÷2
* Strategy 3: 2x3+X=10
For example, there are three strategies of solving an algebra equation, 2(3+X)=10. Strategy 1 is multiplying 2 across the sum (3+X); Strategy 2 is dividing both sides of the equation by 2; Strategy 3 shows the misconception of failing to multiply 2 across the sum (3+X). Since there are various methods of each task, students can choose their way of solving problems.
Model tracing is an algorithm that can run forward along every student’s learning steps and provide instant context-specific feedback. If a student chooses the correct answer, for example, using strategy 1 or strategy 2 to solve the equation, the Cognitive Tutor® will accept the action and provide the student next task. If the student’s mistake match a common misconception, such as using strategy 3, the Cognitive Tutor will highlight this step as incorrect and provide a just-in- time feedback, such as you also need to multiply X by 2. If the student’s mistake does not match any of the production rule in the cognitive model, which means that the student does not use any of the strategies above, the Cognitive Tutor® will flag this step as an error in red and italicized. Students can ask for advice or hint any time when solving problems. According to Corbett, there are three levels of advice. The first level is to accomplish a particular goal; the second level is to offer general ideas of achieving the goal, and the third level is to give students detailed advice on how to solve the problem in the current context.

#### Knowledge tracing

Knowledge tracing can monitor the growing number of production rules during the problem solving process. Every student can choose one production rule every step of his or her way of solving problems, and Cognitive Tutors can calculate an updated estimate of the probability of the student has learned the particular rule. The probability estimates of the rules are integrated into the interface and displayed in the skill-meter. Using probability estimates, the Cognitive Tutors can select appropriate tasks or problems according to students’ individual needs.

### Effectiveness
#### Cognitive Tutor® Geometry

Aleven and Koedinger conducted two experiments to examine whether Cognitive Tutor® can scaffold self-explanation effectively in high school geometry class settings. The findings suggested that “problem-solving practice with a Cognitive Tutor® is even more effective when the students explain their steps by providing references to problem-solving principles.”
In geometry learning, it could happen when students have over-generalized production rules in their prior knowledge, and thus leading shallow encoding and learning. For instance, a student may choose the correct answer and go to next step base on the over-generalized production rule, if an angle looks equal to another, then it is, instead of real understanding. According to Aleven & Koedinger, self-explanation can promote more general encoding during problem-solving practice for it can push students to think more and reflect explicitly on the rules in the domain of geometry.
All the geometry class in the experiments includes classroom discussion, small-group activities, lectures, and solving problems with Cognitive Tutor®. In both of the experiments, students are required to solve problems with the help of the Cognitive Tutor®. However, the Cognitive Tutor® were provided with two different versions, the new version can support self-explanation which is also called guided learning by doing and explaining, and the other cannot. Theses additional features of the new version required students to justify each step by entering geometry principles or referring the principles to an online glossary of geometry knowledge, as well as providing explanations and solutions according to students’ individual choice. Also, the form of explanation in the new version is different from speech-based explanations mentioned in another experiment on self-explanation. The researchers found that students who use the new version of the Cognitive Tutor® were not only better able to give accurate explanation, but also able to deeper understand the domain rules. Thus, the students were able to transfer those learned rules to new situations better, avoiding shallow encoding and learning.

#### Genetics Cognitive Tutor

Corbett et al. (2010) conducted two evaluations of the Genetics Cognitive Tutor in seven different kinds of biology courses in 12 universities in America. The findings suggested the effectiveness of implementing Genetics Cognitive Tutor in post-secondary institution genetic problem-solving practice settings.
In the first evaluation, the participants used the Genetics Cognitive Tutor with their class activities or homework assignments. The software has 16 modules with about 125 problems in five general genetic topics. Genetics Cognitive Tutor utilized the cognitive model of genetics problem solving knowledge to provide step-by-step help, and both model tracing and knowledge tracing. With the average correctness of pretest (43%) and post-test (61%), the average improvements of using Genetic Cognitive Tutors was 18%. In the second empirical evaluations, the researchers examined whether the knowledge tracing can correctly predict students’ knowledge. The finding suggested that the algorithm of knowledge tracing is capable of accurately estimating every student performance on the paper- and-pencil post-test.

## Project Based Learning and Design Thinking
### Theorizing Solutions for Real World Problems

Project Based Learning is a concept that is meant to place the student at the center of learning. The learner is expected to take on an active role in their learning by responding to a complex challenge or question through an extended period of investigation. Project Based Learning is meant for students to acknowledge the curriculum of their class, but also access the knowledge that they already have to solve the problem challenge. At its roots, project-based learning is an activity in which students develop an understanding of a topic based on a real-life problem or issue and requires learners to have a degree of responsibility in designing their learning activity. Blummenfeld et al. (1991) states that Project Based Learning allows students to be responsible for both their initial question, activities, and nature of their artifacts.
Project based learning is based on five criteria
Characteristics of Project Based Learning :
* Projects can be either central or peripheral to the curriculum.
* Projects are focused on questions or problems that drive students to encounter (and struggle with) central concepts and principles of a discipline.
* Projects involve students in a constructive investigation.
* Projects are student-driven to some significant degree.
* Projects are realistic, not school-like.
Challenges are based on authentic, real-world problems that require learners to engage through an inquiry process and demonstrate understanding through active or experiential learning. An example would be elementary or secondary students being asked by their teacher to solve a school problem – such as how to deal with cafeteria compost. Students would be encouraged to work in groups to develop solutions for this problem within specific criteria for research, construction, and demonstration of their idea as learners are cognitively engaged with subject matter over an extended period of time keeping them motivated. The result is complex learning that defines its success is more than as more than the sum of the parts. Project Based Learning aims at learners coordinating skills of knowledge, collaboration, and a final project presentation. This type of schema construction allows learners to use concrete training to perform concrete results. The learner uses previous knowledge to connect with new information and elaborate on their revised perception of a topic. In Project Based Learning this would constitute the process of information gathering and discussing this information within a team to decide on a final solution for the group-instructed problem.
Unlike Problem-Based Learning, experiential learning within a constructivist pedagogy, is the basis of Project Based Learning, and learners show their knowledge, or lack there of, by working towards a real solution through trial and error on a specific driving question. The philosophy of Experiential experiential learning education comes from the theories developed by John Dewey in his work Education and Experience. Dewey argues that experience is shown to be a continuous process of learning by arousing curiosity, strengthen initiative, and is a force in moving the learner towards further knowledge. The experiential aspect of Project Based Learning through working towards solutions for real world problems ties learner’s solutions to practical constructs. Learners must make up the expected gap in their knowledge through research and working together in a collaborative group. The experiential learning through Project Based Learning is focused on a driving question usually presented by the teacher. It is this focus that students must respond to with a designed artifact to show acquired knowledge.
The constructivist methodology of Project Based Learning is invoked through the guided discovery process set forth by the instructor, unlike pure discovery which has been criticised for student having too much freedom, Project Based Learning involves a specific question driven by the instructor to focus the process of investigation. This form of constructivist pedagogy has shown to promote cognitive processing that is most effective in this type of learning environment. Project Based Learning provides a platform for learners to find their own solutions to the teacher driven question, but also have a system in which to discover, analyze, and present. Therefore, Project Based Learning delivers beneficial cognitive meaningful learning by selecting, organizing, and integrating knowledge.

### Experience is the Foundation of Learning

Project Based Learning is a branch of education theory that is based on the idea of learning through doing. John Dewey indicated that teachers and schools should help learners to achieve greater depth in correlation between theory and real-world through experiential and constructivist methods. Dewey stated that education should contain an experiential continuum and a democratization of education to promote a better quality of human experience. These two elements are consistent with Project Based Learning through the application of authentic, real world problems and production of artifacts as solutions, and the learner finding their own solutions through a collaborative effort with in a group. Blumenfeld et al. mentions that the value in Project Based Learning comes from questions that students can relate to including personal health and welfare, community concerns, or current events.
Project Based Learning has basis also in the work of Jean Piaget who surmised that the learner is best served to learn in a constructivist manner – using previous knowledge as a foundation for new learning and connections. The learner’s intelligence is progressed from the assimilation of things in the learner’s environment to alter their original schema by accommodating multiple new schema and assimilating all of this experienced knowledge. Piaget believed in the learner discovering new knowledge for themselves, but that without collaboration the individual would not be able to coherently organize their solution. Project Based Learning acknowledges Piaget’s beliefs on the need for collective communication and its use in assembling new knowledge for the learner.

### Self-Motivation Furthers Student Learning

Project Based Learning is perceived as beneficial to learners in various ways including gained knowledge, communication, and creativity. While engaging on a single challenge, learners obtain a greater depth of knowledge. Moreover, abilities in communication, leadership, and inter-social skills are strengthened due to the collaborative nature of Project Based Learning. Students retain content longer and have a better understanding of what they are learning. There are at least four strands of cognitive research to support Project Based Learning – motivation, expertise, contextual factors, and technology.
Motivation of students that is centred on the learning and mastery of subject matter are more inclined to have sustained engagement with their work. Therefore, Project Based Learning discourages public competition in favour of cooperative goals to reduce the threat to individual students and increase focus on learning and mastery. Project Based Learning is designed to allow students to reach goals together, without fear of reprisal or individual criticism. For instance, Helle, et al. completed a study of information system design students who were asked to work on a specific assignment over a seven-month timeline. Students were given questionnaires about their experience during this assignment to determine their motivation level. Helle, et al. examined the motivation of learners in project groups and found intrinsic motivation increased by 0.52 standard deviations, showing that Project Based learner groups used self-motivation more often to complete assignments. Further, the study implied intrinsic motivation increase substantially for those who were lowest in self-regulation.
Learner metacognitive and self-regulation skills are lacking in many students and these are important to master in student development in domains. In the Project Based Learning system the relationship between student and teacher allows the instructor to use scaffolding to introduce more advance forms of inquiry for students to model, thus middle school students and older are very capable of meaningful learning and sophisticated results. Learners would then become experts over time of additional skills sets that they developed on their own within this system.
Contextually, situated cognition is best realized when the material to be used resembles real-life as much as possible, therefore, Project Based Learning provides confidence in learners to succeed in similar tasks outside of school because they no longer associate subjects as artificial boundaries to knowledge transfer. Gorges and Goke (2015) investigated the relationship between student perception of their abilities in major high school subjects and their relating these skills to real-world problem application through an online survey. Learners showed confidence in problem-solving skills and how to apply their learning to real-life situations, as Gorges and Goke report, and that students who used Project Based Learning style learning have increased self-efficacy and self-concepts of ability in math (SD .77), history (SD .72), etc.. Therefore, students are more likely to use domain-specific knowledge outside of an academic setting through increased confidence. Further, a comparison between students immediately after finishing a course and 12 weeks to 2 years provided effect sizes that showed Project Based Learning helped retain much knowledge.
Technology use allows learners to have a more authentic experience by providing users with an environment that includes data, expanded interaction and collaboration, and emulates the use of artifacts. The learner, in accessing technology, can enhance the benefits of Project Based Learning by having more autonomy is finding knowledge and connecting with group members. Creativity is enhanced as students must find innovative solutions to their authentic problem challenges. For instance, using digital-story-telling techniques through Project Based Learning, as stated by Hung and Hwang, to collect data (photos) in elementary class to help answer a specific project question on global warming in science provided a significant increase in tests results (SD 0.64). As well, in order to find answers, learners must access a broad range of knowledge, usually crossing over various disciplines. The end result is that projects are resolved by student groups that use their knowledge and access to additional knowledge (usually through technology) to build a solution to the specific problem.

### Educators Find Challenges in Project Based Learning Implementation

One of the main arguments against this type of learning is that the project can become unfocused and not have the appropriate amount of classroom time to build solutions. Educators themselves marginalized Project Based Learning because they lack the training and background knowledge in its implementation. Further financial constraints to provide effective evaluation through technology dissuades teachers as well. The information gained by students could be provided in a lecture-style instruction and can be just as effective according to critics. Further, the danger is in learners becoming off-task in their time spent in the classroom, and if they are not continually focused on the task and the learning content, then the project will not be successful. Educators with traditional backgrounds in teaching find Project Based Learning requires instructors to maintain student connection to content and management of their time – this is not necessarily a style that all teachers can accomplish.Blumenfeld et al. (1998) state that real success from Project Based Learning begins and ends with a focused structure that allows teacher modelling, examples, suggested strategies, distributing guidelines, giving feedback during the activity, and allowing for revision of work.

### Learner Need for Authentic Results through Critical Thought

Project Based Learning is applicable to a number of different disciplines since it has various applications in learning, and is specifically relevant with the 21st century redefinition of education (differentiated, technologically-focused, collaboration, cross-curricular). STEM (Science, Technology, Engineering, Mathematics) is one form of 21st century education that benefits from instructors using Project Based Learning since it natural bridges between domains. The focus of STEM is to prepare secondary students for the rigors of post-secondary education and being able to solve complex problems in teams as would be expected when performing these jobs in the real world after graduation. Many potential occupational areas could benefit from Project Based Learning including medical, engineering, computer design, and education. Project Based Learning allows secondary students the opportunity to broaden their knowledge and become successful in high-stakes situation . Moreover, these same students then develop a depth in knowledge when it comes to reflecting upon their strengths and limitations. The result would be a learner who has developed critical thinking and has had a chance to apply it to real situations. Further the construction of a finished product is a realistic expectation in presenting an authentic result from learning. The product result demands accountability, and learner adherent to instructor expectations as well as constraints for the project.
The learner is disciplined to focus on specific outcomes, understand the parameters of the task, and demonstrate a viable artifact. The implication is that students will be ready to meet the challenges of a high-technology, fast-paced work world where innovation, collaboration, and results-driven product is essential for success. Technology is one area where Project Based Learning can be applied by developing skills in real-world application, thus cognitive tools aforded by new technology will be useful if perceived as essential for the project (as is the case in many real-world applications).. For example, designers of computer systems with prior knowledge may be able to know how to trouble-shoot an operating system, but they do not really understand how things fit or work together, and they have a false sense of security about their skills.

## Design-Thinking as a Sub-set of Project-Based Learning
### Using the Process of Practical Design for Real-World Solutions

Design Thinking is a pedagogical approach to teaching through a constructionist methodology of challenge-based problem solving branching off of Project Based learning. It should be understood as a combination of sub-disciplines having design as the subject of their cognitive interests.
An example of design-thinking would be learners engaged with finding a solution to a real-world problem. However, unlike Project Based Learning, design-thinking asks the learner to create a practical solution within a scaffolding process (Figure 3) such as finding a method to deliver clean drinking water to a village. Designers would consider social, economic, and political considerations, but would deliver a final presentation of a working prototype that could be marketable. Hence a water system could be produced to deliver water to villagers, but within the limits of the materials, finances, and local policies in mind. It designates cores principles of empathy, define, ideate, prototype, and test to fulfill the challenges of design. Starting with a goal (solution) in mind, empathise is placed upon creative and practical decision making through design to achieve an improved future result. It draws upon a thinking that requires investigation into the details of a problem to find hidden parameters for a solution-based result. The achieved goal then becomes the launching point for further goal-setting and problem solving.
This type of approach to education is based on the premise that the modern world is full of artificial constructs, and that our civilization historically has relied upon these artifacts to further our progress in technological advances. Herbert Simon, a founder of design-thinking, states that the world that students find themselves in today is much more man-made and artificial that it is a natural world. The challenge of design-thinking is to foster innovation by enhancing student creative thinking abilities. Design-thinking is a tool for scaffolding conventional educational projects into Project Based thinking. Van Merrienbroer (2004) views design-learning as a scaffolding for whole-task practice. It decreases intrinsic cognitive load while learners can practice on the simplest of worked-out examples. Therefore, Design-thinking is currently becoming popular due to its ability to bridge between the justification of what the learner knows and what the learner discovers within the context of 21st century skills and learning. A further example of this process is the design of a product that children will use to increase their physical activity (see video on Design Thinking) and can be explained using the scaffold of Design Thinking:
'Example of Design Thinking - Building a Motion Sensor Device to Motivate Exercise in Children' :
* Result
* Learn from People - "How to make using a motion sensor device compelling?" Asking kids from across the United States to share their likes, habits, frustrations. Interview children who are the mainstream, but also those who are very active or very sedentary. Those at the extremes are better at articulating the needs of the mainstream moderate group of children.
* Find patterns - Capturing the results from interviews on post-it notes to organize. Look for patterns that create opportunities. Some children expressed the need to socialize while playing games, while others stated they enjoyed talking with others while exercising.
* Design Principles - Research developed some specific design principles to be applied to the project - "Facilitate social interaction at all times", "Boost rewards early to increase adherence", "Motivate family activity, not just kid activity", "devote special attention to stay-at-home kids". These principles became the guide posts for designing a prototype.
* Make Tangible - How might these principles be made into a usable product? Create prototypes based on these principles.
* Iterate Relentlessly - Create mock-ups of electronic device user interfaces with paper and pencil and create devices from cardboard and tape. Create digital and physical models for children to test and provide feedback. The final result is a refined model based on this feedback.

### Critical Thought on Design in the Artificial World

Design-thinking is can be traced back to a specific scholars including Herbert Simon, Donald Schon, and Nigel Cross. Simon published his findings on the gap he found in education of professions in 1969. He observed that techniques in the natural sciences and that just as science strove to show simplicity in the natural world of underlying complex systems, and Simon determined the it was the same for the artificial world as well. Not only should this include the process behind the sciences, but the arts and humanities as well since music, for example involves formal patterns like mathematics (Simon, 136). Hence, the creative designs of everyone is based upon a common language and its application. While Schon builds upon the empathetic characteristics of design-thinking as a Ford Professor of Urban Planning and Education at MIT, referring to this process as an artistic and intuitive process for problem-solving. Schon realized that part of the design process was also the reflection-in-action that must be involved during critical thinking and ideating. Moreover, the solutions for problems do not lie in text-books, but in the designer’s ability to frame their own understanding of the situation. Cross fuses these earlier ideas into a pedagogy surrounding education stating that design-thinking should be part of the general education of both sciences and humanities. He implies that students encouraged to use this style of thinking will improve cognitive development of non-verbal thought and communication.

### Critical Thinking as Disruptive Achievement

Design-thinking follows a specific flow from theoretical to practical. It relies upon guided learning to promote effective learner solutions and goes beyond inquiry which has been argued does not work because it goes beyond the limits of long-term memory. Design-thinking requires the learner to have a meta-analysis of their process. Creativity (innovative thought) is evident in design thinking through studies in defocused and focused attention to stimuli in memory activation. Hu et al. (2010) developed a process of disrupted thinking in elementary students by having them use logical methods of critical thought towards specific design projects, over a four-year period, through specific lesson techniques. The results show that these students had increased thinking ability (SD .78) and that these effects have a long-term transfer increasing student academic achievement. This shows use of divergent and convergent thinking in the creative process, and both of these process of thought has been noted to be important in the process of creativity (Goldschmidt, 2016, p 2) and demonstrates the Higher Order Thinking that is associated with long-term memory. Design-thinking specifically demonstrates the capability of having learners develop

### Designers are Not Scientific?

Design-thinking critics comment that design is in itself not a science or cognitive method of learning, and is a non-scientific activity due to the use of intuitive processes. The learner is not truly involved within a cognitive practice (scientific process of reasoning). However, the belief of Cross is that design itself is a science to be studied, hence it can be investigated with systematic and reliable methods of investigation. Further, Schon states that there is connection between theory and practice that in design thinking means that there is a loyalty to developing a theoretical idea into a real world prototype. Design-thinking is a process of scientific cognitive practice that does constitute technical rationality and using this practice to understand the limits of their design that includes a reflective practice and meta. Further, this pedagogy is the application for the natural gap between theory and practice for most ideas, by allowing the learner to step beyond normal instruction and practice to try something new and innovative to come up with a solution. Design-thinking rejects heuristically-derived responses based on client or expert appreciation to take on an unforeseen form.

### 21st Century Learners and the Need for Divergent Thinking

Design-thinking is exceptionally positioned for use with 21st century skills based around technological literacy. Specifically, it is meant to assist the learner in developing creative and critical skills towards the application of technology. Designing is a distinct form of thinking that creates a qualitative relationship to satisfy a purpose. Moreover, in a world that is rapidly becoming technologized, design-thinking the ability to make decisions based upon feel, be able to pay attention to nuances, and appraise the consequences of one’s actions. The designer needs to be able to think outside the perceived acceptable solution and look to use current technology. Therefore, learners using design thinking are approaching all forms of technology as potential applications for a solution. Prototyping might include not just a hardware application, but also the use of software. Cutting-edge technologies such as Augmented Reality and Virtual Reality would be acceptable forms of solutions for design challenges. Specific application of design-thinking is, therefore applicable to areas of study that require technological adaptation and innovation. Specifically, the K-12 BC new curriculum (2016) has a specific focus on Applied Design, Skills, and Technologies that calls for all students to have knowledge of design-thinking throughout their entire education career and its application towards the advancement of technology. Therefore, Design Thinking is a relative and essential component to engaging student critical thought process.

# Argumentation

Argumentation is the process of assembling and communicating reasons for or against an idea, that is, the act of making and presenting arguments. CT in addition to clear communication makes a good argument. It is the process through which one rationally solves problems, issues and disputes as well as resolving questions.
The practice of argumentation consists of two dimensions: dialogue and structure. The dialogue in argumentative discussions focus on specific speech acts – actions done through language (i.e. accept, reject, refute, etc.) – that help advance the speaker’s position. The structure of an argument helps distinguish the different perspectives in discussion and highlight positions for which speakers are arguing.

## Educators Find Challenges in Project Based Learning Implementation

One of the main arguments against this type of learning is that the project can become unfocused and not have the appropriate amount of classroom time to build solutions. Educators themselves marginalize PBL* because they lack the training and background knowledge in its implementation. Further financial constraints to provide effective evaluation through technology dissuades teachers as well (Efstratia, 2014, p 1258). The information gained by students could be provided in a lecture-style instruction and can be just as effective according to critics. Further, the danger is in learners becoming off-task in their time spent in the classroom, and if they are not continually focused on the task and the learning content, then the project will not be successful. Educators with traditional backgrounds in teaching find Project Based Learning requires instructors to maintain student connection to content and management of their time – this is not necessarily a style that all teachers can accomplish (Efstratia, 2014, p 1258).

## Learner Need for Authentic Results through Critical Thought

Project Based Learning is applicable to a number of different disciplines since it has various applications in learning, and is specifically relevant with the 21st century redefinition of education (differentiated, technologically-focused, collaboration, cross-curricular). STEM (Science, Technology, Engineering, Mathematics) is one form of 21st century education that benefits from instructors using Project Based Learning since it natural bridges between domains. The focus of STEM is to prepare secondary students for the rigors of post-secondary education and being able to solve complex problems in teams as would be expected when performing these jobs in the real world after graduation. Many potential occupational areas could benefit from Project Based Learning including medical, engineering, computer design, and education.
Project Based Learning allows secondary students the opportunity to broaden their knowledge and become successful in high-stakes situation (Capraro, et al., 2013, p 2). Moreover, these same students then develop a depth in knowledge when it comes to reflecting upon their strengths and limitations (Capraro, et al., 2013, p 2). The result would be a learner who has developed critical thinking and has had a chance to apply it to real situations. Further the construction of a finished product is a realistic expectation in presenting an authentic result from learning. The product result demands accountability, and learner adherent to instructor expectations as well as constraints for the project (Capraro, et al., 2013, p 2). The learner is disciplined to focus on specific outcomes, understand the parameters of the task, and demonstrate a viable artifact. The implication is that students will be ready to meet the challenges of a high-technology, fast-paced work world where innovation, collaboration, and results-driven product is essential for success. Technology is one area where Project Based Learning can be applied by developing skills in real-world application. For example, designers of computer systems with prior knowledge may be able to know how to trouble-shoot an operating system, but they do not really understand how things fit or work together, and they have a false sense of security about their skills (Gary, 2013, p 1).

## Critical Thinking as Disruptive Achievement

Design-thinking follows a specific flow from theoretical to practical. It relies upon guided learning to promote effective learner solutions and goes beyond inquiry which has been argued does not work because it goes beyond the limits of long-term memory (Lazonder and Harmsen, 2016, p 2). Design-thinking requires the learner to have a meta-analysis of their process. Creativity (innovative thought) is evident in design thinking through studies in defocused and focused attention to stimuli in memory activation (Goldschmidt, 2016, p 1). Hu et al. (2010) developed a process of disrupted thinking in elementary students by having them use logical methods of critical thought towards specific design projects, over a four-year period, through specific lesson techniques. The results show that these students had increased thinking ability (SD .78) and that these effects have a long-term transfer increasing student academic achievement (Hu, et al. 2010, p 554). This shows use of divergent and convergent thinking in the creative process, and both of these process of thought has been noted to be important in the process of creativity (Goldschmidt, 2016, p 2) and demonstrates the Higher Order Thinking that is associated with long-term memory. Design-thinking specifically demonstrates the capability of having learners develop.

## The Process of Argumentation
### Argumentation Stages

The psychological process of argumentation that allows one the produce, analyze and evaluate arguments. These stages will be discussed in more detail later in this chapter.
| 1. Production | How one produces reasons for a standpoint, opinion or assertion. |
| 2. Analysis | Assessing the validity of proposed arguments. |
| 3. Evaluation | Exploring the different views of an argument. |

## The Impact of Argumentation on Learning

Argumentation does not only impact the development of CT and vice versa, it affects many other aspects of learning as well. For instance, a study conducted in a junior high school science class showed that when students engaged in argumentation, they drew heavily on their prior knowledge and experiences. Not only did argumentation enable the students to use their prior knowledge, it also helped them consolidate knowledge and elaborate on their understanding of the subject at a higher level. These are just a few of the ways in which argumentation can be seen to impact aspects of learning other than the development of CT.
Video: Argumentation in Education: https://www.youtube.com/watch?v=YHm5xUZmCDg

# The Relationship between Critical Thinking and Argumentation

Argumentation and CT appear to have a close relationship in instruction. Many studies have shown the impact that both of these elements can have on one another. Data suggests that when CT is infused into instruction it impacts the ability of students to argue tasks that involve both critical thinking and creative thinking must be of an argumentative nature, and that argument analysis and storytelling can improve CT. In other words it would appear that both CT and argumentation impact the development of each other in students and that both impact other aspects of learning and cognition.

## How Critical Thinking Improves Argumentation

CT facilitates the evaluation of the information necessary to make an argument. It aids in the judgement of the validity of each position. It is used to assess the credibility of sources and helps in approaching the issue from multiple points of view. The elements of CT and argumentation have many common features. For example, examining evidence and counter-evidence of a statement and the information that backs up these claims are both facets of creating a sound argument and thinking critically.
The impact of how CT explicitly impacts one’s ability to argue and reason with reference to the aforementioned four CT components will be examined in this section. First, there needs to be an examination of the aspects of CT and how they can be impacted by argumentation. The first component, knowledge, as stated by Bruning et. al (2011), actively shapes the way in which one resolves problems. Therefore, it is essential that students have a solid foundation of knowledge of whatever it is that they are arguing. The ability to use well founded information in order to effectively analyze the credibility of new information is imperative for students who wish to increase their argumentative abilities. The second component of CT that is important for argumentation is inference. As Chesñevar and Simari (2007) discuss in their examination of how we develop arguments, inference and deduction are essential aspects of reaching new conclusions from knowledge that is already known or proven.
In other words, the ability to reach conclusions from known information is pivotal in developing and elaborating an argument. As well, the use of induction, a part of the CT process, is important to argumentation. As Bruning et al. suggest, the ability to make a general conclusion from known information is an essential part of the CT process. Ontañón and Plaza (2015) make the argument that induction can be used in argumentation through communication with one another. Moreover, making inductions of general conclusions using the complete information that every member of the group can provide shows how interaction can be helpful through the use of induction in argumentation. Therefore, it can be seen how induction, an important part of CT, can have a significant impact on argumentation and collaboration. The final component of CT, that may be the most important in its relationship to argumentation, is evaluation. The components of Evaluation indicated by Bruning et al. are analyzing, judging and weighing. These are three essential aspects of creating a successful argument. Hornikx and Hahn (2012) provide a framework for three key elements of argumentation that are heavily attached in these Bruning et al.'s three aspects of CT.

### Production, Analysis, and Evaluation

The three aspects of argumentation that Hornikx and Hahn focus on in their research is the production, analysis and evaluation of arguments. Producing an argument uses the key aspects of CT; there must be evaluation, analysis, judgement and weighing of the argument that one wishes to make a stand on. Analysis of arguments and analysis in CT go hand in hand, there must be a critical analysis of information and viewpoints in order to create a successful and fully supported argument. As well, evaluation is used similarly in argumentation as it is derived from CT. Assessing the credibility of sources and information is an essential part in finding articles and papers that can assist someone in making an informed decision. The final aspect of evaluation in critical thinking is metacognition, thinking about thinking or monitoring one's own thoughts. Monitoring one's own thoughts and taking time to understand the rationality of the decisions that one makes is also a significant part of argumentation. According to Pinto et al.’s research, there is a strong correlation between one's argumentation ability and metacognition. In other words, the ability to think about one’s own thoughts and the validity of those thoughts correlates positively with the ability to formulate sound arguments. The transfer of thoughts into speech/argumentation shows that CT influences argumentation dramatically, however some research suggests that the two interact in different ways as well. It can clearly be seen through the research presented that argumentation is heavily influenced by CT skills, such as knowledge, inference, evaluation and metacognition. However there are also strong implications that instruction of CT in a curriculum can bolster argumentation. A study conducted by Bensley et. al (2010) suggests that when CT skills are directly infused into a course compared to groups that received no CT instruction, those who received CT instruction showed significant gains in their ability of argument analysis. There can be many arguments made for the implication of specific CT skills to impact argumentation, but this research shows that explicit teaching of CT in general can increase the ability of students to more effectively analyze arguments as well. This should be taken into account that Skills Programs mentioned later in this chapter should be instituted if teachers wish to foster argumentation as well as CT in the classroom.

## How Argumentation Improves Critical Thinking

Argumentation is a part of the CT process, it clarifies reasoning and the increases one's ability to assess viable information. It is a part of metacognition in the sense that one needs to evaluate their own ideas. CT skills such as induction and/or deduction are used to create a structured and clear argument.
Research by Glassner and Schwarz (2007) shows that argumentation lies at the intersection of critical and creative thinking. They argue that reasoning, which is both critical and creative, is done through argumentation in adolescents. They suggest that reasoning is constantly being influenced by other perspectives and information. The ability to think creatively as well as critically about new information is managed by argumentation. The back and forth process of accommodating, evaluating, and being open minded to new information can be argued as critical and creative thinking working together. However, the way in which one reaches conclusions from information is created from the ability to weigh this information, and then to successfully draw a conclusion regarding the validity of the solution that students come to. There is also a clear correlation of how argumentation helps students to nurture CT skills as well.
It is clear that CT can directly impact argumentation, but this relationship can also be seen as bidirectional, with argumentation instruction developing the CT skills. A study by Gold et al. shows that CT skills can be fostered through the use of argument analysis and storytelling in instruction. This research suggests that argumentation and argument analysis are not only be beneficial to students, but also to older adults. This study was conducted using mature adult managers as participants. The article outlines four skills of CT that can be impacted by the use of argument analysis and storytelling: critique of rhetoric, tradition, authority, and knowledge. These four skills of CT are somewhat deeper than many instructed in high schools and extremely important to develop. The ability of argumentation to impact CT in a way that enables a person to gain a better perspective on their view about these things is essential to developing personal values as well as being able to use argumentation and CT to critique those values when presented with new information. The ability of argumentation to influence the ability of individuals to analyze their own traditions and knowledge is important for all students as it can give them better insight into what they value.
Argumentation is beneficial to CT skills as well as creative thinking skills in high school students. Research done by Demir and İsleyen (2015) shows that argumentation based a science learning approach in 9th graders improves both of types of thinking. The ability of students to use argumentation to foster CT as well as creative thinking can be seen as being very beneficial, as mentioned earlier creative and CT skills use argumentation as a means of reasoning to draw conclusions, it is therefore not surprising that argumentation in instruction also fosters both of these abilities. In summation, it can clearly be seen that there is a link between both argumentation and CT along with many skills in the subset of CT skills. Explicit instruction of both of these concepts seems to foster the growth of the other and can be seen as complementary. In the next sections of this chapter how these aspects can be beneficial if taught within the curriculum and how they go hand in hand in fostering sound reasoning as well as skills that will help students throughout their lives will be examined.

# Instructional Application of Argumentation and Critical Thinking
## Teaching Tactics

An effective method for structuring the instruction of CT is to organize the thinking skills into a clear and sequential steps. The order in which these steps aid in guiding the student towards internalizing those steps in order to apply them in their daily lives. By taking a deductive approach, starting from broader skills and narrowing them down to task-specific skills helps the student begin from what they know and generate something that they hadn't known before through CT. In the spirit of CT, a student's awareness of their own skills also plays an important role in their learning. In the classroom, they should be encouraged to reflect upon the process through which they completed a goal rather than just the result. Through the encouragement of reflection, students can become more aware of the necessary thinking skills necessary for tasks, such as Argumentation.
Instructing CT and Argumentation predisposes the instruction to using CT skills first. In designing a plan to teach CT, one must be able to critically evaluate and assess different methods and make an informed decision on which would work best for one's class. There are a variety of approaches towards instructing CT. Descriptive Models consist of explanations of how "good" thinking occurs. Specifically, it focuses on thinking strategies such as heuristics to assess information and how to make decisions. Prescriptive Models consist of explanations of what good thinking should be. In a sense, these models give a prototype, a "prescription", of what good thinking is. This approach is comparatively less applicable and sets a high standard of what is expected of higher order thinking. In addition to evaluating which approach would work best for them, prior to teaching CT, instructors need to carefully select the specific types of CT skills that they want students to learn. This process involves assessing factors such as age range, performance level as well as cognitive ability of one's class in order to create a program that can benefit most of, if not all, the students. A final aspect of instruction to consider as an educator is whether direct or indirect instruction will be used to teach CT. Direct Instruction refers to the explicit teaching of CT skills that emphasizes rules and steps for thinking. This is most effective when solutions to problems are limited or when the cognitive task is easy. In contrast, Indirect Instruction refers to a learner-oriented type of teaching that focuses on the student building their own understanding of thinking. This is most effective when problems are ambiguous, unclear or open to interpretation such as moral or ethical decisions.
One example of indirect CT instruction is through the process of writing literature reviews. According to Chandler and Dedman, having the skills to collect, assess and write literature reviews as well as summarize results of studies requires CT. In a teaching note, they evaluated a BSW (Baccalaureate of Social Work) program that strived to improve CT in undergraduate students. Specifically, they assert that practical writing assignments, such as creating literature reviews, help students combine revision and reflection while expanding their thinking to evaluate multiple perspectives on a topic. They found that upon reframing the assignment as a tool to facilitate students in becoming critical reviewers, students viewed the literature review as a summation of course material in addition to an opportunity to improve critical reading and writing skills. Through questioning during discussions, students were guided to analyze the authority and credibility of their articles. The students actively sought for more evidence to support articles on their topics. They found that students successfully created well synthesized literature reviews at the end of the BSW program. This program used implicit instruction of CT skills through dialogue between instructor and students as well as peer engagement. Instead of explicitly stating specific skills or steps to learn CT, the instructors lead the students to practice CT through an assignment. As students worked on the assignment, they needed to use reasoning, analysis and inferential skills in order to synthesize and draw conclusions around the evidence they found on their topics. Practical application of CT skills through an assignment helped students develop CT through indirect instruction.
Argument mapping is a way to visualize argumentation. The following are links to argument mapping software:
https://www.rationaleonline.com/
http://www.argunet.org/editor/
http://debategraph.org/planet
https://www.truthmapping.com/map/1021/#s7164

## Skills Programs for CT

These programs aid in the formulation of critical thinking skills through alternative methods of instruction such as problem-solving. They are usually targeted towards special populations such as students with learning disabilities or cognitive deficits.

### The CoRT Thinking Materials

The CoRT (Cognitive Research Trust) program is based on de Bono’s idea that thinking skills should be taught in school as a subject. The Thinking Materials are geared towards the improvement of thinking skills. This skills program takes on a Gestalt approach and emphasizes the perceptual factor of problem solving. It usually spans over the course of 2 years and is suitable for a wide age range of children. The lessons strive to develop creative thinking, problem-solving as well as interpersonal skills. The materials are split into 6 units and cover topics such as planning, analyzing, comparing, selecting, evaluating and generating alternatives. A typical unit has leaflets covering a single topic, followed by examples using practice items. The leaflets are usually effective in group settings. The focus of these units are to practice thinking skills, therefore much of the instructional time is spent on practicing the topics brought up in the leaflets.
Much of the empirical research on this stand-alone program revolves around the development of creative thinking, however, it is relatively more extensive in comparison to the other programs mentioned in this chapter. The CoRT program has been shown to improve creativity in gifted students. Al-Faoury and Khwaileh (2014) assessed the effectiveness of the CoRT on gifted students’ creative writing abilities. The students were given a pretest that evaluated the fluency, flexibility and originality in writing creative short stories. Students in the experimental group were taught 20 CoRT lessons in total with 10 from CoRT 1 “Breadth” and 10 from CoRT 4 “Creativity” over the course of three months while the control group received traditional lessons on creative writing. The posttest followed the same parameters as the pretest and the results were analyzed by comparing pre and posttest scores. The researchers found a statistically significant effect of CoRT on the experimental group’s fluency, flexibility and originality scores. The mean scores of the experimental groups in all three elements were higher than the control group. These findings suggest that the CoRT program aids gifted students in creative writing skills as indicated through the use of rhetorical devices (metaphor, analogy, etc.), developing characters through dialogue and the control of complex structures. The flexibility and fluency of writing is also applicable to the practice of argumentation and CT. In developing the ability to articulate and modify ideas, students can transfer these skills from creative writing towards higher-order cognitive processes such as CT and argumentation.

### The Feuerstein Instrumental Enrichment Program (FIE)

The FIE is a specialized program focused on mediated learning experiences that strives to develop critical thinking and problem solving skills. Mediation is learning through interaction between the student and the mediator. Similar to Vygotsky's scaffolding, mediation is student-oriented and hinges upon 4 parameters: Intentionality, Reciprocity, Transcendence and Meaning. Intentionality emphasizes the differences between mediation and interaction where the student and mediator have a common goal in mind. Reciprocity involves the student-oriented mentality of mediation, the response of the student hold most importance over academic results. Transcendence focuses on the connectivity of the mediation, it encourages the formation of associations and applications that stretch beyond the scope of the immediate material. Lastly, Meaning in mediation is where the student and mediator explicitly identify "why" and "what for" which promotes dialogue between the two during mediation.
The "instruments" used to facilitate instruction are a series of paper and pencil exercises geared towards practicing internalizing higher order thinking strategies. The instruments cover domains such as analytic perception, spatial organization, categorization, comparison and many more. The implementation of this program varies across countries and is also dependent on the targeted population. A typical program contains 14 units with 3-4 sessions for a few hours every week administered by trained IE staff and teachers.

### The Productive Thinking Program

The Productive Thinking Program consists of the development of planning skills, generating and checking hypotheses as well as creating new ideas. This program is designed as a set of 15 lessons aimed at being completed over one semester. The target population of the program is upper-level elementary school students. The lessons are administered through the use of narrative booklets, often taking a detective-like approach to problem solving where the student is the detective solving a mystery. A structured sequence of steps guides the student to attain an objective specific to the lesson at hand. Following the booklet or story, supplementary problems are given in order for students to apply and practice learned skills.

### The IDEAL Problem Solver

The IDEAL Problem Solver structures problem-solving as 5 steps using the acronym IDEAL. First, (I)dentify the problem, the solver needs to find out what the problem is. Second, (D)efine the problem involves having a clear picture of the entire problem before trying to solve it. Third, (E)xplore the alternatives, meaning that the solver needs to assess the potential solutions available. Fourth, (A)cting on a plan, that is, applying the solution and doing the act of solving. Lastly, (L)ooking at the effects which encompasses the evaluation of the consequences of the chosen solution. IDEAL is flexible in that it can be adapted to suit a wide age range and different levels of ability in its application. It can also be applied to different domains such as composition or physics.

## Instructing Argumentation

Research on argumentation is a comparatively new field of study for education, but has been noted to be of significant importance to almost all educational settings. Grade schools, high schools, and colleges now emphasize the use of argumentation in the classroom as it is seen as the best way for communication and debate in a both vocational and educational settings around the world. A longitudinal study done by Crowell and Kuhn showed that an effective way to help students gain argumentative skills was through consistent and dense application of argumentation in the classroom and as homework. During this longitudinal study, students were exposed to a variety of different methods from which they gained argumentative abilities. The activities employed such as peer collaboration, using computers, reflection activities, individual essays, and small group work all have implications for being valuable in teaching argumentation although it is not clear which ones are the most effective. Data also showed that students all rose to a similar level of argumentative ability, no matter what they scored on argumentative tests before the study began. This shows that even students with seemingly no argumentative skills can be instructed to become as skilled or more skilled than their peers who tested higher than them at the beginning of the study.

### Dialogue and Argumentation

Research by Crowell and Kuhn (2011) highlights collaborative dialogical activities as practical interventions in the development of argumentative skills. The researchers implemented a longitudinal argumentative intervention that used topic cycles to structure a middle school philosophy class. The students had class twice a week for 50 minutes each class over the span of three years. The intervention is as follows: first, students were split into small groups on the same side of the argument to generate ideas around the topic (“for” and “against” teams). Then individuals from either side argue with an opponent through an electronic medium. Finally, the students engage in a whole class debate. These three stages were termed Pregame, Game and Endgame, respectively. After the intervention, students were required to write individual essays regarding the topic through which their argumentative skills would be assessed. The results showed an increased in the generation of dual perspective arguments in the intervention group. Such arguments require the arguer to assume the opposing stance to one’s own and reason its implications. This type of argument reflects a higher-order reasoning that requires critical assessment of multiple perspectives. These results did not begin to appear until year two and was only found statistically significant in year three suggesting that argumentative skills have a longer development trajectory than other lower-level cognitive skills. Through this stand-alone intervention, the collaborative aspect of dialogical activities facilitates the development of intellectual dispositions necessary for good argumentation.
Further research suggests that teaching through the use of collaborative discussions and argumentative dialogue is an effective teaching strategy. Through argumentation, students can acquire knowledge of concepts as well as the foundational ideas behind these concepts. In formulating arguments, students need to generate premises that provide structure to an argument through accepted definitions or claims. Argumentation helps students reveal and clarify misconceptions as well as elaborate on background knowledge. The two aforementioned dimensions of argumentation – dialogue and structure – are often used in assessing and measuring argumentative performance. Specifically, through student-expert dialogue, the students can be guided to give certain arguments and counterarguments depending on the expert’s dialectical decisions. This scaffolding helps the student engage in more critical evaluations that delve deeper into the topic in discussion.
In a study using content and functional coding schemes of argumentative behavior during peer-peer and peer-expert dialogue pairings, Macagno, Mayweg-Paus and Kuhn (2014) found that through student-expert dialogues, students were able to later formulate arguments that dealt with abstract concepts at the root of the issue at hand (i.e. ethical principles, conflict of values) in comparison to peer-peer dialogues. The expert used more specific and sophisticated ways of attacking the student’s argument, such as suggesting an alternative solution to the problem at hand, which in turn enhanced the performance of the student in later meta-dialogues. The results suggest that the practical application of argumentation through collaborate activities facilitates the development of argumentation skills. Similar to CT skills development, rather than teaching, implicit instruction through the practice of argumentation in interactive settings helps its development.

### Science and Argumentation

Much of the literature surrounding the application of argumentation in the classroom revolves around the scientific domain. Argumentation is often used as a tool in scientific learning to enhance CT skills, improve class engagement and activate prior knowledge and beliefs around the subject. In order to articulate and refine scientific theories and knowledge, scientists themselves utilize argumentation. Jonassen and Kim (2010) assert that science educators often emphasize the role of argumentation more than other disciplines. Argumentation supports the learning of how to solve well-structures problems as well as ill-structured ones in science, and from there by extension, in daily life. Specifically, the ill-structured ones reflect more practical everyday problems where goals and limitations are unclear and there are multiple solution pathways as well as multiple factors for evaluating possible solutions.
Through argumentation, students learn to use sound reasoning and CT in order to assess and justify their solution to a problem. For example, a well-structured problem would be one posed in a physics class where concrete laws and formulas dictate the solution pathway to a problem or review questions found at the end textbook chapters which require the application of a finite set of concepts and theories. An ill-structured problem would be finding the cause of heart disease in an individual. Multiple developmental and lifestyle factors contribute to this one problem in addition to the various different forms of heart disease that need to be evaluated. This sort of problem requires the application of knowledge from other domains such as nutrition, emotional well-being and genetics. Since ill-structured problems do not have a definite answer, students are provided with an opportunity to formulate arguments that justify their solutions. Through the practice of resolving problems in science, such as these, students can use CT to develop their argumentative ability.
One’s willingness to argue as well as one's ability to argue also play a significant role in learning science. For one science is at its core, extremely argumentative.
If students have to ability to engage in argumentation at an early age then there knowledge of specific content such as science can grow immensely. The main reason for this is argumentative discourse, being able to disagree with others is extremely important because for adolescents they are at an age which is fundamentally social (ie junior to senior high) using this social ability is pivotal as students at this point may have the confidence to disagree with one another. When a student disagrees with another in argument in a classroom setting it gives them an opportunity to explain the way in which they think about the material. This verbalization of one’s own thoughts and ideas on a subject can help with learning the subject immensely. It also allows for the student to reflect upon and expand their ideas as they have to present them to the class which helps with learning. This also provides the opportunity for the student to identify any misconceptions they have about the subject at hand as more than likely they will receive rebuttal arguments from others in their class. All these factors are aspects of CT and contribute to the learning of the concept and conceptual change in the student which is what learning is all about. The nature of adolescent social behaviour could provide a window through which argumentation could benefit their learning in dramatic ways in learning science.

### Argumentation, Problem Solving and Critical Thinking in History Education

History education offers learners an abundant opportunity to develop their problem solving and critical thinking skills while broadening their perspective on the human condition. The study of history addresses a knowledge gap; specifically, it is the difference between our knowledge of present day and the “infinite, unorganized and unknowable everything that ever happened”. It has long been understood that the study of history requires critical thought and analytical problem-solving skills. In order to become proficient at the study of history, learners must interpret and construct how we come to know about the past and navigate the connection between the past and the body of knowledge we call history. Unfortunately, history education has been demoted to simply recalling factual information - via the overuse of rote memorization and multiple-choice testing - all of which is placed outside the context of present day. This approach does little to inspire a love of history nor does it support the learner’s ability to construct an understanding of how the past and present are connected.
On the other hand, the study of science and mathematics has for many years been centred around developing skills through problem-solving activities. Students learn basic skills and build upon these skills through a progression of increasingly complex problems in order to further their understanding of scientific theory and mathematical relationships. Specific to science education, learners are taught to think like scientists and approach problems using the scientific method. If this approach works well for science and math education, why should it not be utilized for the teaching of history?. Therefore, to develop historical thinking skills it is necessary for instructors to teach the strategies and problem-solving approaches that are used by professional historians. However, unlike science and mathematics, the problems we solve in history are often ill-defined and may be unanswerable in a definitive sense making it more challenging for students to learn and transfer these skills. The following section will address these challenges and provide support for teaching historical thinking via The Big Six Historical Thinking Concepts (2013).

#### Historical Thinking - The Big Six

Based upon years of research and first-hand classroom experience, Seixas and Morton (2013) established a set of six competencies essential to the development of historical thinking skills. Much like science and mathematics education discussed above, the Big Six approach to history education allows the learner to progress from simplistic to advanced tasks. Moreover, the Big Six approach is intended to help the learner “move from depending on easily available, commonsense notions of the past to using the culture’s most powerful intellectual tools for understanding history”. (pg 1) Additionally, the Big Six concepts reveal to the learner the difficulties we encounter while attempting to construct a history of the past. The Big Six competencies include the following: historical significance, evidence, continuity and change, cause and consequence, historical perspectives, and the ethical dimension.
Historical Significance
To develop a critical view of history the learner must recognize and define the qualities that makes something (e.g., person, event, social change) historically significant and why they should spend their time learning about this thing. Behaviourist approaches to history education, focusing on the textbook as the main source of information, have caused learners to become passive in their approach to learning about the past. The textbook becomes the authority on what they need to know. Moreover, the sole use of textbooks to teach national history may contribute to the creation of a “master narrative” that limits a student’s access to what is controversial about their country’s past. By shifting the focus away from the textbook, learners may be able to further their critical thinking skills by following the steps historians take to study the past and constructing their own “reasoned decisions about historical significance”. However, even if a learner is provided primary source evidence to construct a narrative of the past but is not taught to recognize the subjective side to historical thinking - why these pieces of evidence were selected, why this topic was selected, and why they are both historically significant - they may not recognize the impacts of human motivation on the construction of historic understanding. Unlike scientific inquiry that relies on a “positivistic definition of rationality”, historical thinking requires learners to acknowledge human motivation - their own motivation in studying the past, their instructors motivation for selecting certain topics of study, and the motivation of those living in the past
Seixas & Morton (2013) cite two elements involved in constructing historical significance: “big, compelling concerns that exist in our lives today, such as environmental sustainability, justice, power, [and] welfare” and “particular events, objects, and people whose historical significance is in question” (pg 16) The intersection between these two elements is where historical significance is found. It is useful here to add Freedman’s (2015), definition of critical historical reasoning . Critical historical reasoning requires us to recognize that the study of history is not objective. Historians “frame their investigations through the questions they pose and the theories they advance” and therefore, learners of history must analyze the “integrity of historical narratives and their pattern of emphasis and omission” (pg 360). Critical historical reasoning aims towards “conscious awareness of the frame one has adopted and the affordances and constraints it imposes” (pg 360). Therefore, both historians and learners of history must recognize that historical significance is assigned and not an inherent feature of the past, and, importantly, is subject to change.
Evidence
The second set of competencies described by Seixas and Morton (2013) are based on using evidence to address an inquiry about the past. In a study of the cognitive processes involved in evaluating source documents, Wineburg (1991) lists three heuristics: corroboration, sourcing, and contextualization. Corroboration refers to comparing one piece of evidence to another, sourcing is identifying the author(s) of the evidence prior to reading or viewing the material, and contextualization refers to situating evidence in a specific time and place (pg 77).
This study utilized an expert/novice design to compare how historians and high school students make sense of historic documents. Wineburg (1991) argues that the historians were more successful in the task not because of the “schema-driven processing” common to science and mathematics, but by building a model of the [historic] event through the construction of “context-specific schema tailored to this specific event” (pg 83).Additionally, historians demonstrated greater appreciation for the source of the historic documents compared to the students. This suggests that the students did not make the connection between a document's author and the reliability of the source. As Wineburg states, the historian understands “that there are no free-floating details, only details tied to witnesses, and if witnesses are suspect, so are their details” (pg. 84). This study suggests the potential for historical understanding to be improved by teaching the cognitive strategies historians use to construct history.
Multiple narratives of the past exist as individuals bring their own values and experiences to their interpretations of historical evidence. Recognizing this may push learners beyond accepting historic accounts at face value and pull them towards a more critical approach to history. Inquiry-based guided discovery activities, such as Freedman’s (2015) Vietnam war narrative study, suggest that students may gain an awareness of the way they and others “frame” history through exploring primary source documents and comparing their accounts with standardized accounts (i.e. a textbook). By allowing learners to view history as an interpretation of evidence rather than a fixed body of knowledge, we can promote critical thought through the learners’ creation of inferences based on evidence and construction of arguments to support their inferences.
Continuity and Change
Developing an understanding of continuity and change requires the learner to recognize that these two elements overlap over the chronology of history; some things are changing at the same time that other things remain the same. If students are able to recognize continuity and the processes of change in their own lives they should be able to transfer this understanding to their study of the past. Students should be encouraged to describe and question the rate and depth of historic change as well as consider whether the change should be viewed as progress or decline. The evaluation of historic change as positive or negative is, of course, dependent on the perspective taken by the viewer. An example of continuity through history is the development of cultural identity. Carretero and van Alphen (2014), explored this concept in their study of master narratives in Argentinian high school students. They suggest that identity can be useful to facilitate history education, but could also create misconceptions by the learner confounding past with present (or, presentism), as demonstrated when using “we” to discuss people involved in victorious battles or revolutions of the past which gave shape to a nation (pg 308-309). It is useful, then to teach students to differentiate between periods of history. However, periodization of history, much like everything else in the knowledge domain, is based on interpretation and is dependent on the questions historians ask
Educational technology such as interactive timelines, narrative history games, and online discussion groups may help learners make connections between the past and present. For example, the Museum of Civilization offers a teaching tool on the history of Canadian medicare (http://www.museedelhistoire.ca/cmc/exhibitions/hist/medicare/medic01e.shtml). Interactive timelines allow students to see connections between continuity, change, cause, and consequences by visually representing where these elements can be found over historic time. Also, guiding the learners’ exploration of interactive timelines by selecting strong inquiry questions may improve students understanding and facilitate the development of historical thinking. For example, an investigation into the European Renaissance could be framed by the following question: “Did everyone in Europe experience the Renaissance the same way?” Questions such as this are open-ended so as to not restrict where the students takes their inquiry but also suggest a relationship between the changes of the Renaissance and the continuity of European society. Other examples of educational technology that support historical thinking include the “Wold History for us All” (http://worldhistoryforusall.sdsu.edu/) project. This website offers world history units separated into large-scale and local-scale topics and organized by historic period. The lesson plans and resources may allow the learner to making connections between local issues and the broader, global conditions affecting world history. Finally, a case study by Blackenship (2009) suggests that online discussion groups are a useful for developing critical thinking by allowing the teacher to view the students’ thought processes and thereby facilitating formative assessment and informing the type of instructional interventions required by the teacher. Blackenship (2009) cites additional research supporting the use of online discussion because it allows the learners to collect their thoughts before responding to a discussion prompt; they have more time to access prior knowledge and consider their own ideas.
Cause and Consequence
The historical thinking competencies of cause and consequence require learners to become proficient at identifying direct and indirect causes of historic events as well as their immediate and long-term consequences. Effective understanding of the causes of historic change requires the recognition of both the actions of individuals as well as the prevailing conditions of the time. Historical thinking requires students to go beyond simplistic immediate causes and think of history as web of “interrelated causes and consequences, each with various influences” (pg 110).
In addition to improving understanding of the past, these competencies may help learners to better understand present-day conflicts and issues. Shreiner (2014) used the novice/expert format to evaluate how people utilize their knowledge of history to make reasoned conclusions about events of the present. Similar to the Wineburg (1991) study discussed above, Shreiner (2014) found the experts were better at contextualizing and using sourcing to critically analyze documents for reliability and utility in establishing a reasoned judgement. Additionally, the study found that while students would use narrative to construct meaning, they typically created schematic narrative templates - general statements about the past which lack specific details & events. Seixas and Morton (2013) caution the use of overly-simplistic timelines of history because they could create a misconception that history is nothing more than a list of isolated events.The study indicates that historical narratives that follow periodization schemes and are characterized by cause-and-effect relationships, as well as change over time, are helpful for understanding contemporary issues. Therefore, it is important that educators work to develop these competencies in students. Much like historic change, the consequences of certain actions in history can be viewed as positive and negative, depending on perspective. This will be discussed in further detail below.
Historical Perspectives and Ethics
The final two historical thinking competencies proposed by Seixas and Morton are historical perspectives and ethics. Historical perspectives refers to analyzing the historical context for conditions that would influence a historic figure to view an event or act in a particular way. This could include religious beliefs, social status, geographic location, time period, prevailing economic and political conditions, and social/cultural conditions. This again requires some interpretation of evidence as oftentimes we do not have evidence that explicitly describes a historic figure’s attitudes and reasons for acting. Primary source documents, such as letters and journals can provide insight but still require the historian to use inference to make sense of the documents and connect the information to a wider historical narrative or biographical sketch of an individual. Additionally, “[h]ard statistics, such as birth and death rates, ages of marriage, literacy rates, and family size... can all help us make inferences about people's experiences, thoughts, and feelings” (pg 143). There are, of course, limitations to how much we can infer about the past; however, Seixas and Morton (2013) suggest that acknowledging the limitations of what we can know about the past is part of “healthy historical thinking” (pg 143). Learners can develop their understanding of historical perspective by observing the contrast between past and present ways of life and worldviews, identifying universal human traits that transcend time periods (e.g., love for a child), and avoiding presentism and anachronism. A greater understanding of historical perspective will be useful for students when encountering conflicting historical accounts as they will be able to see where the historical actors are “coming from” and therefore better understand their actions. Historical perspective and ethics are related. Seixas and Morton (2013) argue that “the ethical dimension of historical thinking helps to imbue the study of history with meaning” (pg 170). To understand the moral reasons for an individual's actions we need to understand the influence of historical, geographical, and cultural context. Additionally, to understand ethical consequences of the past we make moral judgments which require “empathetic understanding[;] an understanding of the differences between our moral universe and theirs” (Seixas and Peck, 2004, pg 113). People with little experience with historical thinking have difficulty separating the moral standards of today’s society with the societies of the past. Additionally, students tend to judge other cultures more critically than their own; oftentimes defending or justifying actions of their own nations. Therefore, Lopez, Carretero and Rodriguez-Moneo (2014) suggest using national narratives of nations different from the learner’s own nation to more effectively develop critical historical thinking. As the learner becomes proficient at analyzing the ethical decisions of the past, they can translate these skills to analyzing present-day ethical questions. Role playing is a useful instructional strategy for teaching historical perspective. Traditional, face-to-face classrooms allow for dramatic role play activities, debates, and mock trials where students can take on the role of an individual or social group from history. Additionally, educational games and websites allow for the integration of technology while using the role play strategy. Whitworth and Berson (2003) found that, in the 1990-2000s, technology in the social studies classroom was focused mostly on using the internet as a digital version of material that would have otherwise been presented in the classroom. They suggest that alternative uses of technology - such as inquiry-based webquests, simulations, and collaborative working environments - promote interaction and critical thinking skills. One example of a learning object that promotes critical thinking through role playing is the Musee-Mccord’s online game collection (http://www.mccord-museum.qc.ca/en/keys/games/). Specifically, the Victorian Period and the Roaring Twenties games allow the learner to progress through the time period and make decisions appropriate to the historic context of the period. These games are paired with relevant resources from the museum collections which can enhance the learner’s depth of understanding of the period. In terms of teaching strategies for the ethical component of history can be explored through historical narratives, debating ethical positions on historic events, and evaluating and critiquing secondary sources of information for ethical judgements.
To summarize, introducing professional historians’ strategies for studying history is widely regarded as a way to improve historical thinking in students. Professional historian’s cognitive processes of corroborating accounts, critically analyzing sources, and establishing historic context are reflected well by Seixas and Morton’s Big Six Historical Thinking Concepts (2013). Historical thinking gives students the skills to problem solve within the context of history and make sense of the past and connect it to the present in order to broaden the learner’s perspective, understand prevailing social conditions, and influence how they interact with the world. See the Historical Thinking Project’s webpage (http://historicalthinking.ca/lessons) for instructional ideas for all the historical competencies.

## Instructing through Academic Controversy

Using the technique of Academic Controversy could be an effective way of teaching both argumentation and CT skills to students. Academic controversy involves dividing a cooperative group of four in two pairs of students and assigning them opposing positions of an argument or issue, after which the two pairs each argue for their position. The groups then switch their positions and argue again, finally the group of four is asked to come up with an all-around solution to the problem. This activity can be effective in instructing both aspects of argumentation and CT, though it may be a bit dated. The activity is argumentative by nature, making students come up with reasons and claims for two sets of arguments. This equilibrium is important to the argumentative process because provides the students with an opportunity to evaluate the key points of their argument and the opposition's which could be beneficial in any debate. As well, this activity is geared to engage students in a few aspects of CT such as evaluation, since the students must assess each side of the argument. It also engages metacognitive processes as the students must come up with a synthesized conclusion with their peers of their own arguments, a process which requires them to be both analytical and open minded. This activity is a good way of increasing both CT skills and argumentation as it requires students to be open-minded, but also engage in analytical debate.

# Glossary
## Academic Controversy

    a two-round debate process through which a cooperative group of 4 are divided into opposing pairs that engage in a debate. Each pair argues for their own position and switch to the opposing position in the next round.

## Algorithms

    Procedures that can be applied to particular problems that if executed properly, guarantees the correct answer.

## Anachronism

    Attributing characteristics or events of one time period to another.

## Analysis

    The identification and selection of relevant information to allow for further inference and interpretation.

## Argumentation

    The process of using reasoning to support or refute a claim or idea.

## Critical Thinking

    A type a reflective thinking consisting of weighing, evaluating and understanding information

## Deduction

    A type of reasoning where specific conclusions are made from general, given information

## Descriptive Model

    An instructional approach that explains how good thinking occurs.

## Design Thinking

    Student centred learning engaged with finding a solution to a real-world problem.

## Direct Instruction

    A guided learning approach that directly teaches cognitive skills and involves knowledge being explicitly passed from teacher to student.

## Disposition [for critical thought]

    The ability to consciously choose a skill, including an inclination for engaging in intellectual behaviours, a sensitivity to opportunities where such behaviours may be engaged, and a general ability for engaging in critical thought.

## Divergent Thinking

    Thinking characterized by the generation and testing of multiple and diverse solutions .

## Domain Specific Knowledge

    Knowledge in a special area or field.

## Epistemological Beliefs

    Belief regarding the nature and acquiring of knowledge.

## Evaluation

    An umbrella term for the sub skills of analyzing, judging, and weighing

## Functional Fixedness

    A bias that restricts a person to using an object only in the way it is typically used in everyday life.

## Ill-defined Problem

    Problems that do not have a clear goal, solution path, or an expected answer.

## Indirect Instruction

    The learner-oriented instruction of material with emphasis on how the learner interprets the taught material

## Induction

    A type of reasoning where general conclusions are made from specific information

## Inference

    A type of connection or association between two units of knowledge

## Inquiry-based Instruction

    A form of minimally guided learning that allows students to construct their own understanding of the materials.

## Knowledge

    Information that one has, this can include connections and associations between known information

## Metacognition

    Knowledge people have about their own thoughts.

## Periodization

    Classifying the past into distinct blocks of time (periods).

## Prescription Model

    An instructional approach that explains the criteria and characteristics of good thinking

## Presentism

    A tendency to interpret past events using present day values and concepts.

## Problem-based Learning

    A student-centered approach in which students learn about a particular subject through the experience of solving an open-ended problem or question.

## Problem Presentation

    Allows problem solvers better visualize the problem at hand and thus aids them in arriving at a solution.

## Problem Solving

    Cognitive processing' used to accomplish a goal when no solution is apparent to the solver.

## Production

    The generation of arguments

## Project Based Learning

    A student-centred approach in which the learner responds to a complex challenge through a specific design process.

## Self-Regulation

    The process of being metacognitively, behaviourally, and motivationally active in one's own learning

## Skills Programs

    instructional curriculums designed to facilitate the development of CT skills through alternative teaching methods such as problem-solving

## Well-defined Problems

    Problems that do not have a clear goal, solution path, or an expected answer.

# Suggested Readings

* Abrami, P.C., Bernard, R.M., Borokhovski, E., Wade, A., Surkes, M.A., Tamim, R., & Zhang, D. (2008). Instructional Interventions Affecting Critical Thinking Skills and Dispositions: A Stage 1 Meta-Analysis. Review of Educational Research, 78(4). 1102-1134. DOI: 10.3102/0034654308326084.
* Phan, H.P. (2010). Critical thinking as a self-regulatory process component in teaching and learning. Psicothema, 22(2). 284-292.
* Kozulin, A. & Presseisen, B.Z. (1995). Mediated Learning Experience and Psychological Tools: Vygotsky’s and Feuerstein’s Perspective in a Study of Student Learning. Educational Psychologist, 30(2), 67-75.
* Crowell, A., & Kuhn, D. (2011). Dialogic Argumentation as a Vehicle for Developing Young Adolescents’ Thinking. Psychological Science, 22(4), 545-552. DOI: 10.1177/0956797611402512.

## External links

* Critical Thinking: How Children Can Start Thinking Deeply, Part 1
* Critical Thinking for Kids In Action, Part 2
* Critical Thinking for Kids In Action, Part 3
* Critical Thinking for Kids In Action, Part 4
* Critical Thinking Exercises for Kids

# References

* ↑ Heijltjes, A., Van Gog, T., & Paas, F. (2014). Improving Students' Critical Thinking: Empirical Support for Explicit Instructions Combined with Practice. Applied Cognitive Psychology, 28(4), 518-530.
* 1 2 3 4 5 6 7 Murphy, K. P., Rowe, M. L., Ramani, G., & Silverman, R. (2014). Promoting Critical-Analytic Thinking in Children and Adolescents at Home and in School. Educational Psychology Review, 26(4), 561-578.
* ↑ Gick, M. L. (1986). Problem-Solving Strategies. Educational Psychologist, 21(1/2), 99-121.
* 1 2 3 Ku, K. Y., Ho, I. T., Hau, K., & Lau, E. C. (2014). Integrating direct and Inquiry_Based Instruction in the teaching of critical thinking: An intervention study. Instructional Science, 42(2), 251-169.
* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Mathews, S. R., & Lowe, K. (2011). Classroom environments that foster a Disposition for Critical Thinking. Learning Environments Research, 14(1), 59-73.
* ↑ Glaser, E. M. (1941). An Experiment in the Development of Critical Thinking. Columbia University.
* 1 2 3 4 Phan, H.P. (2010). Critical thinking as a self-regulatory process component in teaching and learning. Psicothema, 22(2). 284-292.
* 1 2 3 4 5 6 Moon, J. (2007). Critical Thinking: An Exploration of Theory and Practice (1st ed.). London ; New York: Routledge.
* 1 2 Kurfiss, J. G. (1988). Critical Thinking: Theory, Research, Practice, and Possibilities: ASHE-ERIC/Higher Education Research Report, Volume 17, Number 2, 1988 (1st ed.). Washington, D.C: Jossey-Bass.
* 1 2 Board on Testing and Assessment, Division of Behavioral and Social Sciences and Education, & National Research Council. (2011). Assessing 21st Century Skills: Summary of a Workshop. National Academies Press.
* 1 2 Mason, M. (2009). Critical Thinking and Learning. John Wiley & Sons.
* 1 2 Elder, L., & Paul, R. (2009). The Art of Asking Essential Questions (5th Edition). Dillon Beach, CA: Foundation for Critical Thinking
* 1 2 3 Paul, R., & Elder, L. (2007). The Thinker’s Guide to The Art of Socratic Questioning. Dillon Beach, CA: The Foundation for Critical Thinking.
* 1 2 3 4 Morgan, N., & Saxton, J. (2006). Asking Better Questions (2nd ed.). Markham, ON: Pembroke Publishers.
* 1 2 Cain, R. B. (2007). The Socratic Method: Plato’s Use of Philosophical Drama. A&C Black.
* ↑ Harmon, D. A., & Jones, T. S. (2005). Elementary Education: A Reference Handbook. ABC-CLIO.
* ↑ Stanley, T., & Moore, B. (2013). Critical Thinking and Formative Assessments: Increasing the Rigor in Your Classroom. Routledge.
* 1 2 Jung, I., Nishimura, M., & Sasao, T. (2016). Liberal Arts Education and Colleges in East Asia: Possibilities and Challenges in the Global Age. Springer.
* ↑ Mason, M. (2009). Critical Thinking and Learning. John Wiley & Sons. p. 8.
* ↑ Davies, M., & Barnett, R. (2015). The Palgrave Handbook of Critical Thinking in Higher Education. Springer.
* 1 2 3 4 Cibáková, D. (2015). Methods of developing critical thinking when working with educative texts. E-Pedagogium, (2), 135-145.
* ↑ Garcia, T., & Pintrich, P. R. (1992). Critical Thinking and Its Relationship to Motivation, Learning Strategies, and Classroom Experience. 2-30.
* 1 2 Halpern, D. F. (2013). Thought and Knowledge: An Introduction to Critical Thinking. Psychology Press.
* ↑ Browne, M. N., & Keeley, S. M. (2006). Asking the Right Questions: A Guide to Critical Thinking (8th ed.). Upper Saddle River, N.J: Prentice Hall.
* ↑ Elder, L., & Paul, R. (2009). The Art of Asking Essential Questions (5th Edition). Dillon Beach, CA: Foundation for Critical Thinking. p. 3.
* ↑ Micarelli, A., Stamper, J., & Panourgia, K. (2016). Intelligent Tutoring Systems: 13th International Conference, ITS 2016, Zagreb, Croatia, June 7-10, 2016. Proceedings. Springer.
* 1 2 3 4 5 Conklin, W., & Teacher Created Materials. (2012). Strategies for Developing Higher-Order Thinking Skills, Grades 6 - 12. Shell Education.
* ↑ http://www.janinesmusicroom.com/socratic-questioning-part-i-the-framework.html
* ↑ Bloom, B. S., Krathwohl, D. R., & Masia, B. B. (1984). Taxonomy of educational objectives: the classification of educational goals. Longman.
* ↑ Blosser, P. E. (1991). How to Ask the Right Questions. NSTA Press.
* ↑ Wang, J.-F., & Lau, R. (2013). Advances in Web-Based Learning -- ICWL 2013: 12th International Conference, Kenting, Taiwan, October 6-9, 2013, Proceedings. Springer.
* 1 2 Gregory, G., & Kaufeldt, M. (2015). The Motivated Brain: Improving Student Attention, Engagement, and Perseverance. ASCD.
* ↑ Carol, K., & Sandi, Z. (2014). Q Tasks, 2nd Edition: How to empower students to ask questions and care about answers. Pembroke Publishers Limited.
* 1 2 Doubet, K. J., & Hockett, J. A. (2015). Differentiation in Middle and High School: Strategies to Engage All Learners. ASCD.
* 1 2 3 http://www.educ.kent.edu/fundedprojects/tspt/units/sixfacets.htm
* 1 2 McTighe, J., & Wiggins, G. (2013). Essential Questions: Opening Doors to Student Understanding (1st ed.). Alexandria, Va. USA: Association for Supervision & Curriculum Development.
* 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Bruning, G.J. Schraw & M.M. Norby (2011) Cognitive Psychology and Instruction (5th Ed). New York: Pearson.
* ↑ Anderson, J. R. Cognitive Psychology and Its Implications. New York: Freeman, 1980
* 1 2 Mayer, R. E., & Wittrock, R. C. (2006). Problem solving. In P. A. Alexander & P. H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp. 287–304). Mahwah, NJ: Erlbaum.
* ↑ Snyder, M. J., & Snyder, L. G. (2008). Teaching critical thinking and Problem solving skills. Delta Pi Epsilon Journal, L(2), 90-99.
* 1 2 3 Voss, J. F. (1988). Problem solving and reasoning in ill-structured domains. In C. Antaki (Ed.), Analyzing everyday explanation: A casebook of methods (pp. 74-93). London: SAGE Publications.
* 1 2 Pretz, J. E., Naples, A. J., & Sternberg, R. J. (2003). Recognizing, defining, and representing problems. In J. E. Davidson and R. J. Sternberg (Eds.), The psychology of problem solving (pp. 3–30). Cambridge, UK: Cam- bridge University Press.
* 1 2 3 4 Shin, N., Jonassen, D. H., & McGee, S. (2003). Predictors of Well-Structured and Ill-Structured Problem Solving in an Astronomy Simulation. Journal Of Research In Science Teaching, 40(1), 6-33.8
* ↑ Simon, D. P. (1978). Information processing theory of human problem solving. In W. K. Estes (Ed.), Handbook of learning and cognitive process. Hillsdale, NJ: Lawrence Erlbau
* ↑ Kitchener, K.S., Cognition, metacognition, and epistemic cognition. Human Development, 1983. 26: p. 222-232.
* ↑ Schraw G., Dunkle, M. E., & Bendixen L. D. (1995). Cognitive processes in well-structured and ill-structured problem solving. Applied Cognitive Psychology, 9, 523–538.
* ↑ Cho, K. L., & Jonassen, D. H. (2002) The effects of argumentation scaffolds on argumentation and problem solving. Educational Technology: Research & Development, 50(3), 5-22.
* 1 2 Jonassen, D. H. (2000). Revisiting activity theory as a framework for designing student-centered learning environments. In D. H. Jonassen, S. M. Land, D. H. Jonassen, S. M. Land (Eds.) , Theoretical foundations of learning environments (pp. 89-121). Mahwah, NJ, US: Lawrence Erlbaum Associates Publishers.
* ↑ Spiro, R. J., Feltovich, P. J., Jacobson, M. J., & Coulson, R. L. (1992). Cognitive flexibility, constructivism, and hypertext: Random access instruction for advanced knowledge acquisition in ill-structured domains. In T. M. Duffy, D. H. Jonassen, T. M. Duffy, D. H. Jonassen (Eds.) , Constructivism and the technology of instruction: A conversation (pp. 57-75). Hillsdale, NJ, England: Lawrence Erlbaum Associates, Inc.
* 1 2 3 4 5 Barrows, H. S. (1996). “Problem-based learning in medicine and beyond: A brief overview.” In L. Wilkerson & W. H. Gijselaers (Eds.), Bringing Problem-Based Learning to higher education: Theory and practice (pp. 3-12). San Francisco: Jossey Bass.
* 1 2 (Barron, B., & Darling-Hammond, L. (2008). Teaching for meaningful learning: A review of research on inquiry-based and cooperative learning. Powerful Learning: What We Know About Teaching for Understanding (pp. 11-70). San Francisco, CA: Jossey-Bass.)
* ↑ Dochy, F., Segers, M., Van den Bossche, P., & Gijbels, D. (2003). Effects of problembased learning: A meta-analysis. Learning and Instruction, 13, 533–568.
* ↑ Williams, D., Hemstreet, S., Liu, M.& Smith, V. (1998). Examining how middle school students use problem-based learning software. Unpublished paper presented at the ED-Media/ED Telecom ‘98 world Conference on Educational Multimedia and Hypermedia & World Conference on Educational Telecommunications, Freiberg, Germany.
* ↑ Gallagher, S. A., Stepien, W. J., & Rosenthal, H. (1992). The effects of problem based learning on problem solving. Gifted Child Quarterly, 36, 195–200. Gertzman, A., & Kolodner, J. L.
* ↑ Bruning, G.J. Schraw & M.M. Norby (2011) Cognitive Psychology and Instruction (5th Ed). New York: Pearson.
* ↑ Martin, L., and D. L. Schwartz. 2009. “Prospective Adaptation in the Use of External Representations.” Cognition and Instruction 27 (4): 370–400. doi:10.1080/
* ↑ Chambers, D., and D. Reisberg. 1985. “Can Mental Images Be Ambiguous?” Journal of Experimental Psychology: Human A pragmatic perspective on visual representation and creative thinking Perception and Performance 11 (3): 317–328.
* 1 2 Öllinger, M., Jones, G., & Knoblich, G. (2008). Investigating the effect of mental set on insight problem solving. Experimental Psychology, 55(4), 269-282. doi:10.1027/1618-3169.55.4.269
* ↑ Duncker, K. (1945). On Problem Solving. Psychological Monograph, Whole No. 270.
* 1 2 3 McCaffrey, T. (2012). Innovation relies on the obscure: A key to overcoming the classic problem of functional fixedness. Psychological Science, 23(3), 215-218.
* 1 2 Taconis, R., Ferguson-Hessler, M. G. M., & Broekkamp, H. (2002). Teaching science problem solving: An overview of experimental work. Journal of Research in Science Teaching, 38, 442–46
* ↑ Bransford, J. D., Brown, A. L., & Cocking, R. R. (Eds.). (2000). How people learn: Brain, mind, experience, and school. Washington, DC: National Academies Press.
* 1 2 Novick, L. R., & Bassok, M. (2005). Problem solving. In K. Holyoak & R. Morrison (Eds.), The Cambridge handbook of thinking and reasoning (pp. 321–350). Cambridge, UK: Cambridge University Press.
* ↑ Fuchs, L. S., Fuchs, D., Stuebing, K., Fletcher, J. M., Hamlett, C. L., & Lambert, W. (2008). Problem solving and computational skills: Are they shared or distinct aspects of mathematical cognition? Journal of Educa- tional Psychology, 100, 30–
* 1 2 McNeill, K., & Krajcik, J. (2008). Scientific explanations: Characterizing and evaluating the effects of teachers’ instructional practices on student learning. Journal of Research in Science Teaching, 45, 53–78.
* 1 2 3 4 5 Aleven, V. A. W. M. M., & Koedinger, K. R. (2002) An effective meta-cognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2), 147–179.
* 1 2 3 4 5 6 7 8 9 Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive Tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167.
* 1 2 3 4 5 6 Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 245–252). New York, NY, USA: ACM.
* 1 2 3 4 5 6 7 Koedinger, K. R., & Corbett, A. (2006). Cognitive tutors. The Cambridge handbook of the learning sciences, 61-77.
* ↑ Koedinger, K. R. (2002). Toward evidence for instructional design principles: Examples from Cognitive Tutor Math 6. In Proceedings of PME-NA XXXIII (The North American Chapter of the International Group for the Psychology of Mathematics Education).
* ↑ Corbett, A., Kauffman, L., Maclaren, B., Wagner, A., & J,.ones, E. (2010). A Cognitive Tutor for genetics problem solving: Learning gains and student modeling. Journal of Educational Computing Research, 42(2), 219–239.
* ↑ Corbett, A. T., & Anderson, J. R. (2008). Knowledge decomposition and sub-goal reification in the ACT programming tutor. Department of Psychology, 81.
* ↑ Corbett, A. T., & Bhatnagar, A. (1997). Student modelling in the ACT programming tutor: Adjusting a procedural learning model with declarative knowledge. In User modelling (pp. 243-254). Springer Vienna.
* ↑ Corbett, A. (2002). Cognitive tutor algebra I: Adaptive student modelling in widespread classroom use. In Technology and assessment: Thinking ahead. proceedings from a workshop (pp. 50-62).
* ↑ Koedinger, K. R. & Anderson, J. R. (1993). Effective use of intelligent software in high school math classrooms. In Proceedings of the World Conference on Artificial Intelligence in Education, (pp. 241-248). Charlottesv
* ↑ Koedinger, K., Anderson, J., Hadley, W., & Mark, M. (1997). Intelligent tutoring goes to school in the big city. Human-Computer Interaction Institute.
* ↑ Koedinger, K. R., & Corbett, A. (2006). Cognitive tutors. The Cambridge handbook of the learning sciences, 61-77.
* ↑ Resnick, L. B. (1987). Learning in school and out. Educational Researcher, 16(9), 13-20.
* ↑ Polya, G. (1957). How to Solve It: A New Aspect of Mathematical Method. (2nd ed.). Princeton, NJ: Princeton University Press.
* ↑ <Aleven, V. A. W. M. M., & Koedinger, K. R. (2002). An effective metacognitive strategy: learning by doing and explaining with a computer-based Cognitive Tutor. Cognitive Science, 26(2), 147–179. p. 173
* ↑ Corbett, A., Kauffman, L., Maclaren, B., Wagner, A., & Jones, E. (2010). A Cognitive Tutor for genetics problem solving: Learning gains and student modelling. Journal of Educational Computing Research, 42(2), 219–239.
* ↑ Morgan, Alistar. (1983). Theoretical Aspects of Project-Based Learning in Higher Education. British Journal of Educational Technology, 14(1), 66-78.
* 1 2 3 4 Blumenfeld, Phyllis C., Elliot Soloway, Ronald W. MArx, Joseph S. Krajick, Mark Guzdial, Annemarie Palincsar.. (1991). Motivating Project-Based Learning: Sustaining the Doing, Supporting the Learning. Educational Psychologist, 26(3&4), 369-398.
* 1 2 3 4 5 6 Thomas, John W. (2000). A Review of Research on Project-Based Learning. San Rafael: Autodesk Press.
* 1 2 van Merriënboer, J. J. G., Clark, R. E., & de Croock, M. B. M. (2002). Blueprints for complex learning: The 4C/ID-model. Educational Technology Research and Development, 50(2), 39–61. doi:10.1007/bf02504993.
* 1 2 Dewey, J. (1938). Experience and education. New York: Macmillan.
* 1 2 3 4 5 van Merrienboer, J. J. G., Paul A. Kirschner, & Liesbeth Kester. (2004). Taking the Load off a Learner’s Mind: Instructional Design for Complex Learning. Amsterdam: Open University of the Netherlands.
* ↑ Piaget, Jean; Cook, Margaret (Trans). The construction of reality in the child. New York, NY, US: Basic Books The construction of reality in the child. (1954). xiii 386 pp. http://dx.doi.org/10.1037/11168-000.
* ↑ Ames, C. (1992). Classrooms: goals, structures, and student motivation. Journal of Educational Psychology, 84, 261-271.
* ↑ Helle, Laura, Paivi Tynjara, Erkki Olkinora, Kristi Lonka. (2007). “Aint nothin’ like the real thing”. Motivation and study processes on a work-based project course in information systems design. British Journal of Educational Psychology, 70(2), 397-411.
* 1 2 Joseph Krajcik , Phyllis C. Blumenfeld , Ronald W. Marx , Kristin M. Bass , Jennifer Fredricks & Elliot Soloway (1998) Inquiry in Project-Based Science Classrooms: Initial Attempts by Middle School Students, Journal of the Learning Sciences, 7:3-4, 313-350, DOI: 10.1080/10508406.1998.9672057
* 1 2 3 Gorges, Julia, Thomas Goke. (2015). How do I Know what I can do? Anticipating expectancy of success regarding novel academic tasks. British Journal of Educational Psyschology, 85(1), 75-90.
* ↑ Hung, C.-M., Hwang, G.-J., & Huang, I. (2012). A Project-based Digital Storytelling Approach for Improving Students' Learning Motivation, Problem-Solving Competence and Learning Achievement. Educational Technology & Society, 15 (4), 368–379.
* 1 2 Efstratia, Douladeli. (2014). Experiential education through project based learning. Procedia – Social and Behavioral Sciences. 152, 1256-1260.
* 1 2 3 Capraro, R. M., Capraro, M. M., & Morgan, J. (2013). STEM Project-Based Learning: An Integrated Science, Technology, Engineering, and Mathematics (STEM) Approach (2nd Edition). New York, NY: Sense.
* ↑ Gary, Kevin. (2013), Project-Based Learning. Computer. (Vol 48:9). Tempe: Arizona State.
* 1 2 3 4 5 6 7 8 Cross, N. (2007). Designerly ways of knowing. Basel, Switzerland: Birkha¨user.
* ↑ Simon, H. A. (1996). The sciences of the artificial. Cambridge, MA: MIT Press.
* ↑ Aflatoony, Leila & Ron Wakkary, (2015). Thoughtful Thinkers: Secondary Schooler’s Learning about Design Thinking. Simon Fraser University: Surrey, BC.
* 1 2 Koh, Joyce Hwee Ling, Chin Sing Chai, Benjamin Wong, & Huang-Yao Hong. (2015) Design Thinking for Education. Singapore: Springer Science + Business Media.
* 1 2 3 4 Schon, D. A. (1983). The reflective practitioner: How professionals think in action (Vol. 5126). New York, NY: Basic Books.
* ↑ Hu, Weiping, Philip Adey, Xiaojuan Jia, Jia Liu, Lei Zhang, Jing Li, Xiaomei Dong. (2010). Effects of a “Learn to Think” Intervention programme on primary school students. British Journal of Educational Psychology. 81(4) 537-557.
* 1 2 Wells, Alastair. (2013). The importance of design thinking for technological literacy: a phenomenological perspective. International Journal Technol Des Educ. (23:623-636). DOI 10.1007/s10798-012-9207-7.
* 1 2 3 4 Jonassen, D.H., & Kim, B. (2010). Arguing to learn ad learning to argue: design justifications and guidelines. Education Technology & Research Development, 58(4), 439-457. DOI 10.1007/s11423-009-9143-8.
* 1 2 3 4 5 6 7 8 Macagno, F., Mayweg-Paus, W., & Kuhn, D. (2014). Argumentation theory in Education Studies: Coding and Improving Students’ Argumentative Strategies. Topoi, 34, 523-537.
* 1 2 3 Hornikx, J., & Hahn, U. (2012). Reasoning and argumentation: Towards an integrated psychology of argumentation. Thinking & Reasoning, 18(3), 225-243. DOI: 10.1080/13546783.2012.674715.
* 1 2 Aufschnaiter, C., Erduran, S., Osborne, J., & Simon, S. (2008). Arguing to learn and learning to argue: Case studies of how students' argumentation relates to their scientific knowledge. Journal of Research in Science Teaching, 45(1), 101-131. doi:10.1002/tea.20213
* ↑ Bensley, A., Crowe, D., Bernhardt, P., Buckner, C., & Allman, A. (2010). Teaching and assessing CT skills for argument analysis in psychology. Teaching of Psychology, 37(2), 91-96. doi:10.1080/00986281003626656
* ↑ Glassner, A., & Schwarz, B. B. (2007). What stands and develops between creative and critical thinking? argumentation?. Thinking Skills and Creativity, 2(1), 10-18. doi:10.1016/j.tsc.2006.10.001
* ↑ Gold J., Holman D., & Thorpe R. (2002). The role of argument analysis and story telling in facilitating critical thinking. Management Learning, 33(3), 371-388. doi:10.1177/1350507602333005
* 1 2 3 4 5 6 7 8 Bruning, R. H., Schraw, G. J., & Norby, M. M. (2011). Cognitive psychology and instruction (5th ed.) Pearson.
* ↑ Chesñevar, I., & Simari, G. (2007). Modelling inference in argumentation through labelled deduction: Formalization and logical properties. Logica Universalis, 2007, Volume 1, Number 1, Page 93, 1(1), 93-124. doi:10.1007/s11787-006-0005-4
* ↑ Ontañón, S., & Plaza, E. (2015). Coordinated inductive learning using argumentation-based communication. Autonomous Agents and Multi-Agent Systems, 29(2), 266-304. doi:10.1007/s10458-014-9256-2
* ↑ Pinto, M., Iliceto, P., & Melagno, S. (2012). Argumentative abilities in metacognition and in metalinguistics: A study on university students. European Journal of Psychology of Education, 27(1), 35-58. doi:10.1007/s10212-011-0064-7
* ↑ Bensley, A., Crowe, D., Bernhardt, P., Buckner, C., & Allman, A. (2010). Teaching and assessing critical thinking skills for argument analysis in psychology. Teaching of Psychology, 37(2), 91-96. doi:10.1080/00986281003626656
* ↑ Glassner, A., & Schwarz, B. B. (2007). What stands and develops between creative and critical thinking? argumentation?. Thinking Skills and Creativity, 2(1), 10-18. doi:10.1016/j.tsc.2006.10.001
* ↑ Gold J., Holman D., & Thorpe R. (2002). The role of argument analysis and story telling in facilitating critical thinking. Management Learning, 33(3), 371-388. doi:10.1177/1350507602333005
* ↑ Demir, B., & İsleyen, T. (2015). The effects of argumentation based science learning approach on creative thinking skills of students. Educational Research Quarterly, 39(1), 49-82.
* ↑ Chandler, S. & Dedman, D.E. (2012). Writing a Literature Review: An Essential Component of Critical Thinking. The Journal of Baccalaureate Social Work, 17. 160-165.
* 1 2 3 4 Al-Faoury, O.H., & Khwaileh, F. (2014). The Effect of Teaching CoRT Program No. (4) Entitles “Creativity” on the Gifted Learners’ Writing in Ein El-Basha Center for Gifted Students. Theory and Practice in Language Studies, 4(11), 2249-2257. doi:10.4304/tpls.4.11.2249-2257.
* 1 2 3 Kozulin, A. & Presseisen, B.Z. (1995). Mediated Learning Experience and Psychological Tools: Vygotsky’s and Feuerstein’s Perspective in a Study of Student Learning. Educational Psychologist, 30(2), 67-75.
* ↑ Presseisen, B.Z. & Kozulin, A. (1992). Mediated Learning – The Contributions of Vygotsky and Feuerstein in Theory and Practice.
* ↑ Schuler, G. (1974). The Effectiveness of the Productive Thinking Program. Paper presented at the Annual Meeting of the American Educational Research Association. Retrieved from: http://www.eric.ed.gov/contentdelivery/servlet/ERICServlet?accno=ED103479.
* 1 2 3 4 Crowell, A., & Kuhn, D. (2014). Developing dialogic argumentation skills: A 3-year intervention study. Journal of Cognition and Development, 15(2), 363-381. doi:10.1080/15248372.2012.725187
* 1 2 3 4 Crowell, A., & Kuhn, D. (2011). Dialogic Argumentation as a Vehicle for Developing Young Adolescents’ Thinking. Psychological Science, 22(4), 545-552. DOI: 10.1177/0956797611402512.
* ↑ Jonassen, D.H., & Kim, B. (2010). Arguing to learn ad learning to argue: design justifications and guidelines. Education Technology & Research Development, 58(4), 439-457. DOI 10.1007/s11423-009-9143-8.
* 1 2 3 4 Bathgate, M., Crowell, A., Schunn, C., Cannady, M., & Dorph, R. (2015). The learning benefits of being willing and able to engage in scientific argumentation. International Journal of Science Education, 37(10), 1590-1612. doi:10.1080/09500693.2015.1045958
* 1 2 3 4 5 Seixas, P., Morton, T., Colyer, J., & Fornazzari, S. (2013). The Big Six: Historical thinking Concepts. Toronto: Nelson Education.
* ↑ Osborne, K. (2013). Forward. Seixas, P., Morton, T., Colyer, J., & Fornazzari, S. The Big Six: Historical thinking Concepts. Toronto: Nelson Education.
* 1 2 Carretero, M., & van Alphen, F. (2014). Do Master Narratives Change Among High School Students? A Characterization of How National History Is Represented. Cognition and Instruction, 32(3), 290–312. http://doi.org/10.1080/07370008.2014.919298
* 1 2 3 Freedman, E. B. (2015). “What Happened Needs to Be Told”: Fostering Critical Historical Reasoning in the Classroom. Cognition and Instruction, 33(4), 357–398. http://doi.org/10.1080/07370008.2015.1101465
* 1 2 3 Wineburg, S. S. (1991). Historical problem solving: A study of the cognitive processes used in the evaluation of documentary and pictorial evidence. Journal of Educational Psychology, 83(1), 73–87. http://doi.org/10.1037/0022-0663.83.1.73
* ↑ Freedman, E. B. (2015). “What Happened Needs to Be Told”: Fostering Critical Historical Reasoning in the Classroom. Cognition and Instruction, 33(4), 357–398. http://doi.org/10.1080/07370008.2015.1101465
* 1 2 3 4 5 6 7 8 9 Seixas, P., Morton, T., Colyer, J., & Fornazzari, S. (2013). The Big Six: Historical thinking Concepts. Toronto: Nelson Education.
* ↑ Blackenship, W. (2009). Making connections: Using online discussion forums to engage students in historical inquiry. Social Education, 73(3), 127-130.
* ↑ Shreiner, T. L. (2014). Using Historical Knowledge to Reason About Contemporary Political Issues: An Expert–Novice Study. Cognition and Instruction, 32(4), 313–352. http://doi.org/10.1080/07370008.2014.948680
* ↑ Seixas, P., & Peck, C. (2004). Teaching Historical Thinking. Challenges and Prospects for Canadian Social Studies, 109–117.
* ↑ Lopez, C., Carretero, M., & Rodriguez-Moneo, M. (2014). Telling a national narrative that is not your own. Does it enable critical historical consumption? Culture & Psychology , 20 (4 ), 547–571. http://doi.org/10.1177/1354067X14554156
* ↑ Whitworth, S. A., & Berson, M. J. (2003). Computer technology in the social studies: An examination of the effectiveness literature (1996-2001). Contemporary Issues in Technology and Teacher Education [Online serial], 2(4). Retrieved from http://www.citejournal.org/volume-2/issue-4-02/social-studies/computer-technology-in-the-social-studies-an-examination-of-the-effectiveness-literature-1996-2001
* ↑ Johnson, D. W., & Johnson, R. T. (1993). Creative and critical thinking through academic controversy. The American Behavioral Scientist, 37(1), 40-53. Retrieved from https://www.proquest.com/docview/1306753602

# Learning Science and Conceptual Change

Unlike other academic areas, when it comes to learning science, children develop experience based preconceptions about the world and how it works before they even enter a classroom. These naive concepts can be useful in helping them develop in a complex world, but can ultimately result in incomplete or incorrect knowledge about the natural world. In order to correct and reshape these pre-developed conceptions about science, we must first identify where the misconceptions lie, then work with students to break them down and rebuild them using hands on experiences to foster a deeper understanding of the materials. This can be an intricate and delicate process that takes time in order for students to evolve their thinking and successfully accommodate and assimilate new information into their existing schemata.
In this chapter we discuss how these naive preconceptions tend to develop in young people, how they differ from expert thinking, and how to identify and confront such notions so that students may be able to develop their scientific and critical thinking skills, ultimately changing their conceptions. We discuss effective teaching methods and essential elements of science instruction, as well as addressing some unique challenges to teaching science at different educational levels.

## The Development of Naive Scientific Preconceptions

Children are naturally curious, constantly exploring their surroundings and questioning the world around them, which helps them to develop an understanding of the natural world and becomes their reference set when encountering new things in their environment. These naive scientific concepts, developed from personal observation and experience, tend to become strongly held and often incorrect beliefs by the time they begin school, which can make them resistant to complex and sometimes counter-intuitive scientific theories and principles.

### Naive preconceptions

The persistence of naïve conceptions about the natural world which students bring with them to the classroom has been one of the most outstanding developments in understanding science learning. From a young age, people develop scientific thinking or curiosity. Even before entering school, children are frequently used to observing and questioning their everyday life experiences. Thus, this results in both children and even a lot of adults having naïve theories, which are well-formed but scientifically incorrect thoughts about how the world operates. There are several examples of naïve beliefs about science. Firstly, prior to any formal instruction, the resistance of people’s naïve conceptions to change is particularly evident in their own theories of motion. If people are asked to describe the motion of force on a ball tossed into the air, they tend to illustrate that the motion of force starts by going upwards with the ball as it ascends, and down as the ball falls. However, it is a typically incorrect reply; the correct answer would be that the motion of force is consistently downward, though the ball gains height before falling. Another example of naïve misconception related to biology is that children possess false beliefs and incomplete knowledge about scientific information. When children are asked whether certain things are plants, they incorrectly responded that carrots, oak trees and grass are not plants. Since many people have already formulated misconceptions about science before learning formally in a classroom setting, it may be difficult to change or re-conceptualize these beliefs.
Wu and Wu explored the development of epistemological beliefs about the nature of science in children. They described three levels of epistemological beliefs about science: 1) Individuals at the novice level tend to know little about science. They hold naive preconceptions about experiments in science, they don't understand the difference between hypotheses and theories, and tend to think mostly about the procedure of an experiment and getting 'good' results, rather than thinking about what the experiment is supposed to be testing and whether it is an accurate measure, etc. They also tend to have strong beliefs about science, in that it is definitive and unchanging. 2) Individuals in the intermediate level have developed a basic understanding of the concept of hypotheses, and that theories are well tested, supported hypotheses. They define experiments as testing a hypothesis, and understand that science is uncertain. 3) Individuals at the expert level see that scientific inquiry is guided by theories, and that theories are a general explanation of a phenomenon. They understand the difference between testing a theory and testing a hypothesis within a theory. Students who have dynamic epistemological beliefs tend to be more active learners than students with static beliefs. These students will tend to form better understandings of scientific concepts, and will rise in level more quickly.
Wu and Wu also described two different types of epistemology: Formal epistemology refers to individuals' beliefs about professional science, and practical epistemology refers to individuals' ideas about scientific knowledge and how they construct this knowledge from personal experiences. Based on these concepts of epistemological beliefs, Wu and Wu asked how these beliefs developed in children, and how they affected the development of inquiry skills. They listed three key inquiry skills involved in formulating students' scientific explanations: 1) Being able to identify causal relationships between variables, 2) being able to describe their reasoning process, and 3) being able to interpret data to use as evidence. The researchers conducted an exploratory study to determine whether students improved their inquiry skills after a series of inquiry-based activities, what their practical epistemologies were before and after completing the activities, and what interactions there were between inquiry skills and practical epistemologies.
Participants in the study were two classes of fifth grade students. 34 students were chosen per class, and of those 34, 18 were girls and 12 were boys. Students were given ten learning activities covering various physics topics to be completed over a period of five weeks, or 15 class periods. Students learned about effects of force, developed experiments to test the relationship between force and spring length, collected and analyzed data, and presented their findings. Since the students had not experienced this kind of learning before, the teacher used various scaffolding techniques, such as asking guiding questions, performing demonstrations, and giving feedback during activities, in order to support the students' learning. Researchers recorded observational data, administered pre and post-tests on explanation skills, and conducted interviews with students at the end of the five week period. Results from the data analysis showed that using inquiry-based activities could improve students' explanation skills and develop their inquiry skills, allow them to put together experiments, use data to support their claims, recognize experimental errors, and better understand scientific questions, but their epistemological views about science remained at a novice level. Wu and Wu concluded by suggesting that following inquiry-based activities with reflective discussions could help to change epistemological beliefs, but further research on this topic would still be necessary to support this hypothesis.

### The difference between novice and expert thinking

There are several perspectives which look at people with novice and expert levels of scientific knowledge. Compared to novices, experts have superior ability to solve scientific problems efficiently and quickly. Experts in the field of science acquire wide-ranging knowledge and strategies which influence what they notice and how they organize, understand and signify information from their environment. Since they are trained and exposed to numerous opportunities for problem solving, they are able to build a variety of pertinent problem-solving schemata. This allows them to solve science problems much faster than novices. Experts have the ability to perceive meaningful patterns of information and to easily retrieve important aspects of their scientific knowledge more flexibly as compared to the novices. This great recall ability can be explained in terms of chunking information. For example, novices may not use chunking as much when dealing with physics principles whereas experts use chunking to demonstrate a particular set of equations which correspond to a specific problem they may be faced with. The chunking ability is enhanced when familiar patterns are organized and gathered together in certain meaningful categories. However, novices do not have such ability to process or organize their thoughts with more complex problems.
When looking at the understanding of scientific theories, there is a significant difference between novices and experts that allows them to be separated into three sub-groups; for instance, children, adults, and scientists. Identifying essential skills in scientific reasoning includes a prominent comprehension of the main point of the theory, a clear differentiation of supporting and rebutting evidences, the ability to reason why the data, graph or diagrams support the theory, theory building, and precise reflection on the theory building process. A few problems arose when searching for the differences between the groups: 1) There was a lack of domain-specific knowledge among the adult experts and 2) Children felt frustrated if they were not able to fully interpret the theories including the structures as well as the messages, and decipher how they could apply the theories.

## Identifying and Changing Naive Beliefs

Teaching scientific concepts to children is more complex than simply teaching terms and facts. It is common for children to acquire a superficial understanding of scientific concepts that enables them to recall relevant terms and even the gist of concepts presented to them. Unless they fully process the contradictions the new concepts may hold for their prior beliefs, their misconceptions will not change and their understanding of science will remain shallow. To insure that children fully learn scientific concepts, we must address any misconceptions that may block their comprehension.

### Identify students' naive preconceptions

People’s false beliefs about science will naturally be revealed as time passes and as they learn. However, one must be very cautious not to directly point out and disclose people’s misconceptions. In order to be successful and not to hurt others' feeling, but to guide them in the right direction, teachers should prepare experience-based instruction which includes activities that will inspire the learners to change their preconceptions. It is important to expose students to more encouraging and dynamic activities. The major role of teachers is to assist students in expressing their thoughts and ideas about how they think and why they think that way. As a class, students will be able to exchange their own thoughts and compare others’ with theirs; this process allows students to justify their own thoughts and to see other peoples' perspectives. Then teachers can clarify and explain their conceptions by providing adequate explanation.

### Create conceptual conflict

Once an individual's preconceptions have been brought to their attention, they must be challenged in order to create cognitive disequilibrium within the individual, motivating them to assess and reconsider their beliefs on the subject. The way instructors can do this is by offering multiple views (perhaps those of several different students in the class), then asking probing questions about which explanation seems to be the most reasonable and getting the students to think about each scenario, rather than telling them which one is correct. This form of questioning will help to stimulate discussion amongst students. Allowing time for students to discuss with each other is also important, as it exposes them to other students opinions and viewpoints. The teacher can then suggest the need for a hands-on activity, such as an experiment, in order to test the validity of the various proposed hypotheses. Running experiments can help students to learn critical thinking skills such as the importance of gathering data to back their statements and making decisions on what information is relevant. In order for an experiment or demonstration to be successful in creating conceptual conflict, however, it must eliminate all possible explanations for the outcome, except the correct scientific explanation. If this is done correctly, then students can begin reassessing their own preconceptions and altering their beliefs in order to accommodate the new information.
This process is demonstrated in a study done by Shtulman and Calabi on the effects of instruction on essentialist theories of evolution. There are common misconceptions about evolution, even among people with post-secondary education. Ideas such as, individuals are born better adapted to their environment than their parents, that traits are developed over one's lifetime and then passed on to one's offspring, or that animals are more likely to survive and adapt than to die and become extinct, are quite prevalent, even among science students. These types of misconceptions have been documented in the most novice individuals (i.e. children) and the most expert individuals (i.e. educational professionals) alike. The fact that these misconceptions are so generalized indicates a bias referred to as essentialism. Essentialism is the belief that every observable trait is due to some unobservable variable at its core, also referred to as an 'essence'. This 'essence' is passed down from parent to offspring. What makes something what it is, is not a series of traits it has in common with other members of its species, but the 'essence' it inherited from its parents. This essentialist way of thinking is not only common across varying levels of knowledge, but across cultures as well.
Two major paradigms that are used to test childrens' understanding of evolutionary concepts are the unknown-property paradigm and the switched-at-birth paradigm. The unknown-property paradigm introduces a novel property of a familiar organism (ex: a cat [familiar organism] can see in the dark [novel property]). Then novel organisms, which may or may not possess the novel property, are introduced (ex: a cat that looks like a skunk and a skunk that looks like a cat). When tested on preschool-aged children, most tended to associate the novel property with the skunk-like cat, but not with the cat-like skunk.
The switched-at-birth paradigm gives a scenario, for example, that a calf is taken from its birth parents and raised, instead, by a family of pigs. It then asks whether the calf will grow up to possess similar properties to its birth parents (i.e. straight tail, eats grass), or to its foster parents (i.e. curly tail, eats slop). Children tend to reply with the former, that it will develop like a cow, because it is a cow, not a pig. This essentialist reasoning can be useful for basic details, like what an organism should look like, how it reproduces, where it prefers to live, etc., but it falls short when applied to more complex processes like evolution and natural selection. Essentialists tend to focus on differences between species, but what is most important in evolution is the differences among individuals within a species.
Shtulman and Calabi took an interest in this phenomenon and conducted an test-retest study using college undergraduates in order to determine whether instruction in evolution could change students' essentialist preconceptions about evolution. Each participant was required to fill out a questionnaire before and after taking a course on evolution. The questionnaire tested six sections of the subject: variation, inheritance, adaptation, domestication, speciation, and extinction. By comparing pre and post-test scores and calculating the difference between the two, researchers divided the students into either 'learner' or 'non-learner' categories. Those that improved significantly (learners) were shown to have significantly more preinstructional misconceptions than the non-learners. This would imply that having more misconceptions when going into a course may facilitate learning, possibly because students run into these misconceptions quite frequently throughout the course, and are thus confronted with conflicting information more often. Having to resolve these conflicts so frequently can lead to greater conceptual change, than if one rarely encountered these informational conflicts.

### Promote reassessment of preconceptions

Once students begin to question the validity of their current beliefs, it is important for the teacher to assist them in changing those beliefs by providing further information and answering questions in order to help them change their perception of the topic or event. Success at this stage would result in the students changing their conceptions about a scientific event and would help promote better acquisition of scientific knowledge.

## Teaching Science Effectively

In order to teach any subject effectively, one must engage with the students and support them throughout the learning process. This is especially true in science. By making them question themselves and their preconceptions, they become more deeply engaged with the materials, and develop a better understanding of the concepts and, as a result, gain a deeper sense of achievement than if they were to simply read the texts and recite the facts.

### Inquiry-based teaching vs. Lecture style classrooms

Quite often classes are taught in a lecture style which tends to promote more of a fact-based or memorization style of learning. This can be a problem for science, in particular, because of students', often strongly developed, naive scientific preconceptions. In order to help reveal these misconceptions to both the teacher and the student, a more dynamic inquiry-based teaching approach is recommended.
Bruning, Schraw and Norby describe inquiry-based teaching as teachers supporting active learners. In an inquiry-based classroom environment the student takes the lead by performing hands-on activities that the teacher has set up, asking questions and forming hypotheses about the tasks, collaborating with other students and comparing ideas, and testing and reforming their hypotheses if results are contradictory to their predictions. Teachers are there to assist in their students' learning, rather than being the driving force. This allows students to voice their beliefs and to test them. If these beliefs are proven to be dysfunctional, then they may be driven to find an explanation that better supports their observations.
If students are simply given facts and information and tested on those facts, then none of their misconceptions are being identified or addressed, and they will continue to hold these misconceptions even if the facts they are memorizing for the exam contradict them. This may be because the facts alone aren't enough to show them why their beliefs are incorrect. If they don't fully understand why something is the way it is, though they may see an inconsistency between the information they were given and the beliefs they currently hold, this may not be enough for them to adopt a new belief. In order to change a child's beliefs, we must present them with information that is intelligible (can be understood by the student), plausible and believable (as sited in). This means that the information must give a better explanation of a phenomenon than their current conception.

### Providing strategies for deeper understanding of materials

Optimal strategies for dispelling misconceptions need to address two important functions in a student's learning process; assimilation and accommodation of information. Assimilation is when a student uses existing schemas (mental representations of information or experiences) to help make sense of new information, and accommodation is when students replace or alter existing schemas in order to be consistent with new information.
Longfield discusses discrepant teaching events as strategies to help identify students' misconceptions and cause cognitive disequilibrium (conflict of existing schemas and new information being presented), which would lead to the assimilation and accommodation of new information. A discrepant teaching event is an event that produces an unexpected outcome, and that forces students to become aware of dysfunctional beliefs that may need to be changed. These discrepant events can be used in almost any classroom, but for science, in particular, it can be a very effective strategy.

### Reassessment and development of teaching strategies

Inquiry-based teaching and the use of discrepant teaching events can be very difficult to master, and it can take time to make a curriculum which takes full advantage of these techniques, but it is possible to improve student learning by incorporating these elements as much as possible, and by scaffolding students in their development throughout the class. It is, therefore, essential to continue to assess and reassess one's teaching strategies in order to insure that students are getting the most help possible, and that all individuals are taken into account. By being more aware of the students' needs, one can also develop an environment where students can feel safe and secure enough to ask questions and to express their own ideas and opinions.

### Effective instruction improves science achievement

There are many factors that contribute to a student's level of science achievement, but some of the most important extrinsic factors are instructional time and quality. Studies have shown that level of achievement is strongly correlated to the amount of instruction in a subject that a student has received and the amount to which they understood the information being presented. This is further reason to invest more time in building a comprehensive curriculum that helps to foster a child's curiosity and helps to scaffold them so that they may better understand the materials given.

## Assessing and Monitoring Students' Level of Science Understanding

As important as it is to teach science to young people, it is just as important to assess how well they are understanding the materials. A better understanding of science can help them not only in school, but in everyday life. A large part of learning should be review of past materials in order to practice and maintain information in long-term memory. By incorporating practice and repetition of new materials, and checking students’ knowledge on a regular basis, you help them to retain more information for a longer period of time, as well as hopefully encouraging them to study and practice on their own.

## Essential Elements of Science Instruction

Because of the nature of science and complexity of many of the concepts, it is not something that can easily be taught simply from a text. There are several essential elements that need to be present in the curriculum in order to optimize students' learning, understanding and appreciation for science.
Design process VS Design patterns
Design process is meant for developing students’ ideas by adding new ideas, elaborating on current ideas, and organizing ideas into more coherent explanations. Its purpose in knowledge integration is to: elicite ideas, introduce new ideas, evaluate, and synthesizing those ideas. Design patterns play an important role in students’ learning of science. They include assuming predictions, conducting experiments, gathering evidence, and reflection.
Teach science as a problem-solving process
The most beneficial and effective skill in problem-solving is inquiry-based approaches to science teaching, since problem-solving strategies require cognitive perspectives rather than knowledge acquisition processes in science.
Use hands-on demonstration
Experiments and/or demonstrations are a good way of challenging students’ preconceptions. Thus, it is important for teachers to thoughtfully choose the topic, stay focused and guide the learners through, in order to adopt correct scientific views. In order to assist students to be involved in science class, hands-on activities, which help them engage in self-questioning, should be used. These activities will advance students’ maturity and improve their view of science.
Teach the nature of scientific theories
As students go up in grades, they require more advanced understanding of scientific inquiry and ability to think critically. Students, therefore, have to learn how to interpret scientific theories, how they differ from hypotheses and how they are both coordinated. To secondary school students, scientific theories might be boring or difficult to deeply understand. Yet if teachers provide enough time to process the learned materials, students will be able to handle other advanced materials much more easily and proficiently.
Give enough time to restructure knowledge
Not only in science, but in other subjects as well, teachers need to provide students with sufficient time to do their work in class as well as allow them to process the knowledge mentally. To change or modify one’s beliefs or knowledge that one has carried for such a long time requires sufficient processing time.Conceptual change in science, especially, is not a short-term, but a long-term process. Students need to be exposed to many kind of science-based views of the world in order to have their own thoughts challenged to the point that they need to reconcile the conflict between their own beliefs and the concepts that have been presented to them. Teachers should not expect rapid change in their students' thinking since it might discourage students from deeper processing of meanings. One of the best methods for changing and developing students’ knowledge is to repeatedly provide students with complex problem sets. This lets them discover new strategies on how to problem-solve, and to learn which strategies they have to apply to certain questions. Also, rather than covering many different topics, it is better to cover small sections of topics in greater detail. Doing so may help students to develop a better understanding of scientific concepts and principles.

## Unique Challenges In Teaching Science at Different Stages

Though there are a lot of common issues in teaching science, regardless of age and experience level, there are some unique challenges to teaching individuals at different stages in life and education. These challenges need to be taken into account when running a class in order to support students in every learning stage.

### Elementary level

There are some difficulties in teaching young children in elementary school not only science, but most subjects. One of the causes for these challenges might have to do with the learner’s motivation in relation to one’s specific goals. Because elementary school students are younger, they will not be focusing on desired outcomes such as knowledge attained, grades, etc. in subjects, as older students might. Hence, teachers have to make sure to encourage them, using inquiry-based instruction, and to assist student learning by simplifying and imparting their professional knowledge. One study shows that there is evidence that elementary science achievement considerably increases when the teachers instruct using inquiry-based teaching methods. Therefore, teachers have to consider how to instruct their young children in more efficient ways.
As for the significance on inquiry teaching in science education, there are some difficulties that the teachers encounter in their classes. For instance, there is a study which examines pre-service elementary teachers, and how they manage the difficulties within their lessons. About 16 seniors (fourth-year students) in an elementary teacher education program are studied based on the teacher’s inquiry lesson preparation, practice, and reflections of pre-service elementary teachers. Quantitative data such as discussion, observation of classroom teaching, and reflective writing is collected as for the data. The result has found that there are difficulties on the lesson that are missing some elements: encouraging students to have own ideas and curiosity, assisting them in valid experiments for appropriate hypothesis, scaffolding their data interpretation and discussion. These difficulties affect teachers’ task such as tension between guided and open inquiry, incorrect comprehension of hypothesis and lack of self-confidence in science knowledge. Thus, this emphasizes the importance of teacher’s job to understand students and their actions.
People might be curious as to whether gender plays a significant role in performance on certain subjects. One study investigated whether girls would perform better than boys at an elementary school level depending on the methods of science instruction. However, the study concludes that there is no correlation between accomplishment and gender in relation to method of science instruction.

### Secondary level

These days, as school education has conformed to a new structure of teaching/learning requirements, it requires teachers and students to define new learning goals, and to take an innovative direction in instruction so that students can deal with any challenges after they graduate. Some studies demonstrated that students’ poor marks for certain subjects were not actually caused by the subject's difficulty, by their study techniques, or by how they processed learned information, but by struggles students may have had in adopting teachers' teaching methods. Traditional methods of science teaching included didactic principles which related to the theoretical-action system and gave guidance to students' education for the long term. Teaching science to a new generation, however, would need to integrate technologies into a lesson plan. Of all the new orientations in educational practices, the “active-participative methods and techniques” are highlighted as new and effective methods of teaching. It provides development of students’ critical thinking by stimulating one’s capacity to discover, analyze, and build conceptual maps in their mind. Examples of this would be brain-writing, jigsaw, etc. Promoting an interactive learning environment, co-operational strategies when assessing learning material, and applying students’ own information processing, will be an important area to think about. Considering both traditional and new teaching strategies, it is important to apply advantageous points from each side. Therefore, educators now have the significant task of formulating new pedagogical systems which harmoniously combine both traditional and new teaching strategies.
Between science teacher’s instruction-based didactic methods, and active-participative and interactive didactic methods, students have shown significant difference in the efficiency of learning, especially in Physics. 148 students from two grade 6 classes and two grade 9 classes were randomly selected from the secondary school in Bucharest. Three experimental classes, which included the active-participative methods, and three controlled classes, which held didactic activities, were given written assessments such as written assignments, pretests and post-tests. From the collected data, it is noticed that pretest results showed no significant difference. However, post-test results did show a significant differences between the groups. Thus, the result reflected that the active-participative and interactive didactic teaching methods were effective when teaching science to students.
Not only do the methods and strategies that the teachers use matter, but other factors matter, too. Also, the other important role for teachers is to know the students’ physical, psychological and individual characteristics. This will help teachers when applying certain strategies to students, since a strategy reaches its maximum efficiency, and benefits learners most, when it’s been applied to the best learning situation possible, where students are fully involved. When teachers use these methods, it is more likely for students to manage and achieve individual learning tasks and increase their motivation. Overall, secondary level years are when students’ scientific thinking skills are formed and their critical analysis skills are developed. Thus, educators have to understand students’ situations and their individual differences in order to come up with more meaningful ways of administering science education and applying adequate teaching strategies.

### Post-Secondary or University level

Of all the different education levels, post-secondary students are most likely to be involved in classes which use technologies such as the Internet. The vast majority of post-secondary students frequently use the Internet to communicate and access websites. Despite the fact that students are familiar with the Internet, there are some issues raised planning and teaching a curriculum. The challenges include how well teachers are able to use the internet and how to effectively incorporate internet use into the class. In order to consider how to improve one’s teaching using technology, the instructors first need to carefully choose an appropriate range of websites. Then they need to introduce and explain what the procedures are, engage students in various activities which are inquiry-led, assign them into groups if necessary and ask them to investigate scientific questions. Most likely, demonstrating these processes will be a faster and more efficient way of giving guidance to students so that they can visualize in their minds and draw out what they should do. However, a more important challenge is to transfer these thoughts listed above into practical performance. Real life performance does not always proceed in the same direction as the thoughts envisioned in our minds, but in fact often conflicts and goes unexpected directions; thus, instructors must always keep in mind that their roles need to be well defined and their curricula need to be planned as well as possible.
Some key issues of where teachers need to develop their practical pedagogical skills are as follows: 1) a narrow range of criteria for selecting appropriate websites, 2) give thought on how students should be grouped in the Internet lessons, 3) students’ plagiarisms, 4) more variety of ways to use Internet in science teaching, 5) limited consideration about the role of the teacher, 6) science objectives of the chapter being vanished when using Internet, 7) geographical setting of classroom. As teachers, it is essential to have backup plans to secure all science lessons. Teachers have to keep in mind that they must keep students on task, give clear instructions, make the lesson student centered, check availability of resources, use plenaries to reinforce learning and implement various kinds of activities rather than just using the Internet. These innovative deployments of Internet technology in instruction demonstrate the effects of the Internet and information technology in various contexts in higher education. However, it provides some challenges for teachers when planning science lessons as well as teaching in classrooms. Such challenges and difficulties may be decreased in relation to how much effort the teachers puts in and how they try to guide their students.

## Suggested readings

Childs, A., Sorensen, P., & Twidle, J. (2011). Using the Internet in science teaching? Issues and challenges for initial teacher education. Technology, Pedagogy And Education, 20(2), 143-160. doi:10.1080/1475939X.2011.588413
Dinescu, L., Dinica, M. & Miron, C. (2010). Active strategies - option and necessity for teaching science in secondary and high school education. Procedia - Social and Behavioral Sciences, 2(2), 3724–3730.
Longfield, J. (2009). Discrepant Teaching Events: Using an Inquiry Stance to Address Students' Misconceptions. International Journal Of Teaching And Learning In Higher Education, 21(2), 266-271.
Yoon, H.G., Joung, Y. J. & Kim, M. (2011) The Challenges of Science Inquiry Teaching for Pre-Service Teachers in Elementary Classrooms: Difficulties on and under the Scene. Research in Science Education, 42(3), 1-20. DOI 10.1007/s11165-011-9212-y0.

## Glossary

Accommodation: Replacing or altering existing schemas with new information.
Assimilation: The use of existing schemas to help interpret new information.
Chunking: Utilizing a letter, number, or word which may contribute to short-term memory capacity.
Cognitive disequilibrium: Conflict between existing schemas and new information being presented.
Discrepant teaching event: An event in which an unexpected outcome occurs. Used to bring to light dysfunctional student beliefs and to insight change.
Essentialism: The belief that every observable trait is due to some unobservable variable at its core, also referred to as an 'essence'.
Formal epistemology: Individuals' beliefs about professional science.
Inquiry-based teaching: Student is seen as the active learner with teacher taking a supportive role.
Naïve beliefs: Inaccurate beliefs about a phenomenon, acquired through uncontrolled observation.
Naïve theories: Incorrect conceptual frameworks for understanding a domain and important processes within that domain.
Practical epistemology: Individuals' ideas about scientific knowledge and how they construct this knowledge from personal experiences.
Schemas: Mental representations of information or experiences.

## References

* Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* Childs, A., Sorensen, P., & Twidle, J. (2011). Using the Internet in science teaching? Issues and challenges for initial teacher education. Technology, Pedagogy and Education, 20(2), 143-160. doi: 10.1080/1475939X.2011.588413
* Dinescu, L., Dinica, M. & Miron, C. (2010). Active strategies - option and necessity for teaching science in secondary and high school education. Procedia - Social and Behavioral Sciences, 2(2), 3724–3730. doi: 10.1016/j.sbspro.2010.03.579
* Kensinger, S. H. (2013). Impact of instructional approaches to teaching elementary science on student achievement. Dissertation Abstracts International Section A, 73.
* Longfield, J. (2009). Discrepant Teaching Events: Using an Inquiry Stance to Address Students' Misconceptions. International Journal Of Teaching And Learning In Higher Education, 21(2), 266-271.
* Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* Yoon, H.G., Joung, Y. J. & Kim, M. (2011). The Challenges of Science Inquiry Teaching for Pre-Service Teachers in Elementary Classrooms: Difficulties on and under the Scene. Research in Science Education, 42(3), 1-20. doiː 10.1007/s11165-011-9212-y
* Science_vision image retrieved from Wikimedia commons, scientific pictures and images
* Meyers_b13_s0595c image retrieved from Wikimedia commons, zoological illustrations

# Learning to Read

Reading is a crucial skill as it helps us learn in all academic subjects and is so important for success outside the classroom. Learning to read is a complex, multi-year process of learning to recognize the sounds and meanings of symbols and written words. Reading ability is an important achievement for children because it is their entry point into the world of literacy and learning upon which much of life depends.
This chapter covers several aspects of learning to read, beginning with the cognitive factors of reading including memory and attention. Different types of reading difficulties and disabilities are reviewed, with some implications for teaching. As each child is different, there is no single method that can be used to teach all children with reading difficulties or disabilities. The chapter discusses the three stages of reading, moving from children who do not know how to read or recognize any words all the way to children who have the ability to connect letters and their sounds in order to decode unfamiliar words. Reading instruction today tends to combine and adapt methods derived from different theories to address the needs of individual learners. Finally, we discuss several ways of effectively assessing reading progress.

## Cognitive Factors of Reading

Success in reading depends on using the cognitive abilities of working and long-term memory, and also focusing attention in order to make meaning of the text. In addition, the reader must have some knowledge about the world around them in order to comprehend the information.

### Memory

Working and long-term memory are cognitive factors that have to do with children’s success in learning to read. Reading is an act of memory because it depends on world and linguistic knowledge. When a child is learning a word they have to keep that word in their mind long enough to build up the more complex meaning of phrases, sentences, and whole passages. The temporary storage of material that a child has read depends on working memory. The working memory is different from the other forms of memory due to the fact that it reflects both processing and storage. Working memory is often studied when learning about children’s reading development, and Baddeley's model is often used to describe the relation between working memory and reading development. This model involves two basic aspects: the phonological loop and the visual sketchpad. The processing of phonological information has an inner rehearsal aspect, called the articulatory loop, which allows the phonological information needed for word decoding and reading comprehension to be retained longer in memory. When children are not able to, or have problems decoding words, it is then associated with difficulties in phonological awareness. Children with these difficulties are unable to understand or have access to the sound structure of spoken language. When children are young their working memory capacity is restricted due to the fact that they lack the well-developed skills needed for encoding and rehearsal. In order to make reading meaningful both working and long term memory are needed. Thus, when children learn new information, the information must be kept fresh in their working memory while they retrieve previously learned information from their long-term memory. In order for children to become great readers, they must decode words at a reasonable speed so they don’t have to hold the meaning of the words in their memory for too long when figuring out the meaning of a sentence or paragraph. When poor readers are unable to decode words at a reasonable speed they are required to spend extra time trying to decode, resulting in further stress on their ability to comprehend the text..

### Attention

When it comes to attention and reading, there is no doubt that attention is crucial to the understanding and overall comprehension of the text being read. Without attention, one cannot read. Teaching young students to read can often be challenging, as some don't have the ability to sit still for prolonged periods of time, or simply are not interested in the material they are supposed to be reading. In order for a child to read, they must have a book open in front of them, they must be oriented towards the text. Even getting some children to this point can be a great accomplishment, as some children simply do not have the attention span or capacity to focus on tasks such as this for so long. In addition to having children pay attention to the actual book in front of them, it is necessary for them to make connections while they are reading in order for them to see how smaller pieces of the reading process relate to larger ones. A great deal of attention is needed for this as well, as there are often several instances in which a student can overlook a small point that will play a bigger part in their learning later on. Though older readers do not need to focus a lot of energy on the reading process, young readers must do so, simply because they haven't learned or practiced the process as much. Things such as eye movements and moving their eyes from left to right are included in this type of attention that is needed. Attention must also move systematically from word to word as they read, as well as making sure the words being read can be connected to the overall message of the text. In addition, attention needs to be shifted from images or illustrations to the text, and back again in order for all elements of the story to make sense.

## Reading Disabilities

As much as reading requires the child's ability to comprehend letters and words and draw on prior knowledge, the child must be taught these skills. However, some students will find difficulties in the learning process, which can in some cases be attributed to learning disabilities. There are several ways to effectively teach students the necessary means to developing literacy while working with any struggles they may be having.

### Diagnosing Reading Difficulties

When it comes to disabilities, learning how to read can be a struggle for both teachers and students. A disability in learners can hinder the learning process, meaning teachers and instructors often have to adapt their teaching style to help the student grasp the information. Though the word “disability” can be viewed as a general term to mean a number of different things, each disability is different, and each can effect reading in a different way. Oftentimes, the most difficult part of determining why a student is having troubles reading lies in diagnosing what the trouble is. In terms of diagnosis, there are three principles used to guide the process. First, an analysis must make a specific as possible diagnosis of the student's reading habits to discover which parts are not functioning properly. Second, the analysis has to be based upon any available and relevant facts. Last, a sense of open-mindedness must be maintained when looking over any data. Though open-mindedness may not seem important in the scientific realm of things, it does make an impact on the diagnosis process, as the point of discovering a reading disability is not to prove or disprove a theory or method, but to find exactly what is troubling the student. In addition, it might be found that a student doesn't fall into the category of one specific learning disability, but perhaps show signs of struggling in more than one aspect. This is a case where open-mindedness plays a large part in the diagnosis and evaluation stages. Part of this is because though a child does have a reading disability, it doesn't necessarily need to fit a specific model or formula for what is considered a disability and what isn't.
Principles of diagnosis aside, there are six general steps in the diagnosis process: 1. "Measuring the reading achievement of the class or school" - Reading tests are administered to students to gauge the level of reading in each class, and deficiencies in scores can be seen. 2. "Selecting the major reading problems for each grade" - Test scores are analyzed and the greatest problem for each grade is determined, and any student below the average needs attention and support. 3. "Selecting pupils deficient in reading" - Students below the average are given attention. 4. "Obtaining additional information about the pupils selected for individual diagnosis" - Information about the student's health, general intelligence and attitude are taken into account. Oftentimes factors such as these can have a large impact on the general leaning abilities of the student. Other factors such as perceptual span, the number and regularity of fixations, dyslexia, vocalization, and breathing habits should also be taken into account. 5. "Determining the types of reading deficiencies" and 6. "Determining the causes of the defects in reading" can be grouped into one larger step, as the four preceding steps will help determine how and why the student is having difficulties learning.
Though these steps can be used as guidelines in determining what a student may be having difficulties with, each case is unique and can be approached in other manners that may apply to that specific instance.
In diagnosing dyslexia, there are five tasks that help to determine if a child is in fact dyslexic or not: oral word and pseudoword reading, oral text reading, oral pseudoword text reading, oral word list reading, and spelling words and pseudowords. In doing these tests, four types of reading speeds and four levels of reading and spelling accuracy are taken into account. If a child lands in the bottom 10th percentile of the scores for each tested task, it is found that they have deficient skills in that area of reading and comprehension. To be completely diagnosed with dyslexia, the child must score in the bottom 10th percentile in a minimum of three of the four accuracy tests, or in three of the four fluency tests, or in two of the accuracy tests and two of the fluency tests. In the past, learning disabilities were assessed through IQ tests and achievement scores on reading tests. If their IQ was found to be average but showed a low reading achievement score, the child was diagnosed as having a learning disability. Known as the discrepancy model based procedure, this process of diagnosis was used in many schools, placing students in classrooms that could provide them with the assistance they need.
Today, the Component Model of Reading is used more often to help understand and diagnose reading and learning disabilities. There are three domains of the Component Model of Reading (CMR): cognitive, which includes the two components of word recognition and comprehension, psychological, which includes the components of motivation and interest, locus of control, learning styles and gender differences, and ecological, which includes the components of home and classroom environment and culture, parental involvement and dialect. One thing to note is that the components of the cognitive domain can satisfy the condition of independence in a student, but the psychological and ecological domains do not do this as well. In a 2005 study regarding the effectiveness of the Component Model of Reading, it was found that IQ tests previously used to determine reading disabilities can only predict about 25% of variability in reading comprehension, whereas with the Component Model of Reading, 38-41% of variability can be found. Overall, the diagnosis of reading and learning disabilities is a process that has evolved over time, and is becoming more and more precise. Though there are multiple types of reading disabilities, each one should be approached with a sense of open mindedness, as well as a conscious awareness that each child and disability will be different. In terms of ways of diagnosis, the steps included in this section have proven to work, though they are subject to change in the future as educators learn more about intervention and how disabilities progress or change.

### Types of Reading Difficulties and Disabilities

When discussing reading difficulties and disabilities, it is important to remember that there is a wide array of factors that can affect reading and comprehension, and not all students who experience trouble reading are diagnosed with a "reading disability". Sometimes students are not developmentally ready for leaning to read and struggle with understanding the linguistics of reading, while others come from cultural or linguistic backgrounds that don't match with the type of reading instruction taught in the school. In addition, some students may have difficulty learning to read even with good instruction. This can be attributed to low general ability, meaning they struggle more with comprehension than the reading itself.
Students can also have reading disabilities even if they are of average or above average intelligence, which is different than students who are poor readers. Speech problems are often paired with difficulties in writing and spelling, which in turn would hinder the student's ability to successfully read and comprehend what they're reading.
One common reading difficulty lies in the phonics of a word, specifically when a student is unable to match the sounds of the letter to the visual symbol. In this case, the problem is central rather than sensory. Word blindness, or dyslexia, is another common reading disability in which letters and words are mixed around in the student's brain, causing great difficulty in the comprehension of what is being read.
Dyslexia is a reading deficiency that runs in families. The risk of a child developing dyslexia increases to 40% if a parent or relative also has the disability. Left handed students often have difficulty learning to read, as reading from left to right is natural for right handed students - they are used to leading away from the centre of the body rather than to it, which is the opposite for left handed students.
Slow, silent reading can be caused by visual defects as well as a narrow span of recognition and dyslexia, while poor reading comprehension of slow reading can be caused by an inability to focus, organize main ideas, or lack of attention. However, though reading comprehension can be hindered by a lack of attention, too much attention or focus on single words can also cause problems. If a student focuses too much on individual words, they can be unable to bring a sentence together as a whole.
In terms of Attention Deficit Hyperactivity Disorder (ADHD), it is found that 8% to 20% of students have the disorder, but only 3% to 7% show severe enough symptoms that they are given a clinical diagnosis and are provided with special education intervention and services. Though ADHD does not always mean that a student has a reading disability, it can often coincide with one, in most cases causing students to misinterpret texts, or have general comprehension issues regarding common connections in what is being read.

### Implications for Teaching

As with any student who is struggling with any subject matter, teachers and learning aids need to make changes in teaching styles and material to help make the material learnable. One approach is the Reading Recovery method, which was developed in New Zealand. This method consists of four steps: 1. "Children are assessed on a variety of literacy tasks, such as their ability to identify letters, read words, write, and do oral reading, as well as on their literacy knowledge and strategies" 2. "A series of 30-minute daily tutorials in which a Reading Recovery teacher works one-on-one with an individual student." 3. "Standardized sessions that provide a systematic set of activities, including having the child practice letters and words, read from short books, and produce short compositions that are cut up and re-read." 4. "A systematic process of staff development in which teachers are trained by Reading Recovery trainers."
This level of scaffolding is both practical and efficient in propelling the learning of students, and provides a high level of support for both students and teachers.
In a 2005 study conducted by Robert Schwartz, the Reading Recovery program was examined in terms of the effectiveness in aiding first grade students. In this study, 47 Reading Recovery teachers in 14 states sent information of 107 students, 53% of which were male and 47% of which were female. The students were paired with a Reading Recovery teacher who led them through the program, which includes daily tutorials that span the length of 30 minutes that are targeted toward structured activities that include practicing letters and words, reading short books, producing small pieces of writing that are later divided up and re-read. At the end of the study, 65% of students “graduated” the program, 16% were recommended for further help, and 16% did not complete the program. This can be compared to the national Reading Recovery data: 56% of students graduated, 15% were recommended for further help, 19% were labelled as incomplete, 5% “moved” and 4% were labelled as “none of the above”.Reading Recovery has proven to be a very successful program in regards to rehabilitation and intervention. One interesting aspect of the Reading Recovery program is that though it's been around since the 80's, the system and process is still effective, and hasn't needed to undergo any major changes. A testament to the success of the program is that is it used internationally in English speaking countries, and has a high success rate.
As with any situation in which a student is struggling, teaching methods and procedures must be adjusted to adapt to the struggle of the child. Intervention of reading disabilities is very important in the stages of learning to read, as a problem developed with learning to read can plague a student throughout their entire lives if not caught soon enough. This is exactly why programs such as Reading Recovery and the help of teachers are so important in the process of learning to read. Though the help of SEA's and other support workers can greatly benefit the students and help take some of the load off of teachers, it is still important to remember that each child does need to have a unique learning plan catered to what they need and don't need assistance with.
Though not all students are diagnosed with a reading disability, it is not uncommon for students to struggle with material taught in class. Sometimes this can be attributed to an undiagnosed disability. In the case of an undiagnosed student, or a student who is not disabled but still faces challenges with learning, intervention and understanding can play a large part in the future development for the child.

## Stages of Reading

Like learning any other thing in life, learning to read requires steps or stages. When children start to learn to read there are three main stages that they will all go through. Children start off learning to read by not being able to decode any words (pre-alphabetic stage), they then start to use phonic cues and other reading strategies (partial alphabetic stage), and finally get to the point where they are able to distinguish between similar spelt words and are able to learn new words while making connections (full alphabetic stage). Reading develops in multiple dimensions before individuals reach conventional literacy. Each of these stages is described as young children move from displaying very little literacy-related behaviours to eventually being able to systematically decode language.

### Pre-Alphabetic Stage

The pre-alphabetic stage consists of children who know quite a bit about literacy but don’t know how to read any words. Children at this stage have no alphabetic knowledge which is why the stage is called pre-alphabetic stage. Children however do know a lot of words, can speak in full sentences, and have conversations with others, but are just not able to read any actual words. However, they may say a word by looking at the symbol associated with it. For example, reading the word ‘McDonald’s’ by looking at the big ‘M’ or saying the word 'dog' by looking at a picture of one. Children have no recognition of the word ‘McDonald’s’ or the word 'dog' nor will they be able to read the word once the picture is taken away. Children are simply responding to their environment and not to the print. Despite children knowing quite a bit of words and being able to say full sentences they are just not able to read the words or any print on their own. Another type of group in this stage try “linking a word’s look with its pronunciation and meaning”. However, the memory demands of reading in this way become very overwhelming and exhausting for children and they soon try relying more on phonetic information while reading.

### Partial Alphabetic Stage

Children enter the partial alphabetic stage when they learn the names or sounds of the alphabet and then use this knowledge to read words. This is the stage where the actual reading starts to occur. Children are no longer just looking at the images and reading the word, they are actually trying to read the print. They now have knowledge of the letters in the alphabet and are using this knowledge to help them read words. Children in this stage generally focus more on the first and last letters of words, for example, the letter s and n to read spoon. When children are asked to write down words they tend to write down the letters whose sound they can hear when pronouncing the word. For example, children might write the word giraffe as jrf. Children's reading at this stage is only partial because they are simply just looking at some of the letters in words and usually only some sound for pronunciation.
To better understand the difference of pre-alphabetic stage readers and partial alphabetic stage readers, a study was conducted by Ehri and Wilce in 1985. This study tested kindergartners by separating them into the two different stages mentioned above. Each stage was given several practice trials to learn to read two kinds of spellings. One kind involved visual spellings with varied shapes but no relationship to sounds, so for example, mask spelled uHo. The other kind involved phonetic spelling that had letters represent some sounds in the words, so for example, mask spelled MSK.
The results were that the pre-alphabetic stage readers learned to read visual spellings a lot easier than the phonetic spellings. Ehri explained that this confirmed their idea that pre-alphabetic stage readers depend on visual cues because they lack knowledge of letters. The partial alphabetic stage readers displayed the opposite pattern and were able to use letter-sound cues to remember the words.

### Full Alphabetic Stage

When children are able to learn sight words by forming complete connection between letters in spelling and phonemes in pronunciations, they have moved onto the full alphabetic stage. In the partial alphabetic stage children will write down words with only the letters they can clearly hear when pronouncing the word. However, in the full alphabetic stage children are now able to decode unfamiliar words when reading, they can invent spellings that represent all the phonemes, and are able to remember the spelling of words a lot better.
To show the difference in the phases of reading a study was conducted by Ehri and Wilce in 1987. This study was done to show the differences in sight word learning between full and partial stage readers. For this study, kindergartners who were already in the partial alphabetic stage were randomly selected. They then were randomly assigned to a treatment or a control group. The treatment group then received training to become full alphabetic stage readers by having them practice reading similarly spelled words. This required processing all the grapheme-phoneme relations in the words to read them correctly. The control grouped received no training what so ever and remained as partial stage readers. Following this, both groups of kindergartners got practice learning to read a set of fifteen words over several trials. All the words in the list had similar spellings which made it harder for children to learn by remembering partial cues. The list of words included words such as spin, stab, stamp, or stand. Before the study none of the children could read more than two of these words prior to training.
The results of the study showed huge differences between the two groups of kindergartners. Full-alphabetic stage readers learned to read most of the words in the list in three trials but the partial alphabetic stage readers never even reached this level of learning. The study did say that the reason for difficulty for the partial alphabetic stage readers is due to them confusing similarly spelled words. Which goes to show the advantage readers get when they can form full connections to retain sight words in memory.
It is important to note how just being one stage behind in reading can cause such a huge difference in the results. Just by being at the full alphabetic stage the kindergartners were able to read the similarly spelled words in three trials whereas the partial alphabetic stage kindergartners had so much trouble doing so. It goes to show the importance of each reading stage and how important it is for teachers to make sure students are ready to go onto the next stage. Teachers need to be sure that students have learnt everything they need in the previous stage to help them pass the next one.

### Consolidated Stage

Children get to this final stage of reading when they have retained more sight words in their memory and are familiar with letter patterns. Children are now familiar with letter patterns that appear repeatedly in different words and the grapheme-phoneme connections begin to get consolidated into larger units. For example, words such as printing is learned more easily now because fewer connections are required to secure the word in memory and the word is no longer being processed as many separate letter-sound connections but as two syllable sized chunks.

### Implications for Teaching

The English language is not perfect and therefore it can have one letter represent one sound, two or more letters represent that one sound, a silent final vowel can change the sound of the medial vowel, and many words contain letters that have no sound. When teachers are starting to teach their students how to read they should be aware of the interconnection between letters and sounds and know the different stages children go through when they are developing their literacy skills. Throughout the three alphabetic stages, teachers should focus on decoding and vocabulary as the mastery as these dimensions are vital to successful reading. Children need to be able to decode words or in other words be able to think about letter and sound relationships and correctly pronounce written words. Games can be used to teach children the correct pronunciation of letters and their sounds. Having children hear the different sounds of letters with the letter in front of them will help them understand better. Since beginner readers are visual learners, pictures might help them understand the relationship between letters and the sound. For example, a teacher may put up a picture of bat and write on the board the word bat but skipping the first letter. Now the teacher can ask the students what the first letter might be by repeatedly saying the word bat and helping the students sound it out. It is important for children to decode and fluently read words, but it is also vital that children understand the meaning of specific contextual words. When children can read and understand the meaning of the words they are able to truly comprehend a text. Teachers should help children in discovering the meaning of words found in a text by having them place words connected to each other in specific categories, create connected categories of words, pointing out relationships between words, using dictionaries or thesauruses to extend word meaning, and having students self-select words for vocabulary study and stating specific reasons for choosing these words.

## Teaching to Read

There is often great importance placed on literacy and the skill of reading, and so teachers may feel pressured to find the best approaches or methods for teaching their students how to read. Deciding which areas to focus on can be challenging when teaching beginning readers. Ideally, reading instruction should touch on each of the foundations of language, as well as the benefits of learning to read. Over the course of the history of reading instruction, there have been numerous controversies about which methods are the best to teach children to read. In 1967, Jeanne Chall grouped reading methods into two categories that are still useful in understanding the divide on reading instruction: code-emphasis methods and meaning-emphasis methods. Code-emphasis methods focus on decoding and learning letters and sounds, while meaning-emphasis methods focus on making meanings and using one’s general knowledge store. The following approaches to teaching reading are separated by their methodology, but today, models of reading strive for a balance between the two types of reading methods because they are both recognized as essential for learning to read. Reading and literacy development have many different dimensions, but we also must not forget the importance of teaching children that reading can be enjoyable.

### Phonics-Based Approach

A phonics-based approach to teaching reading is a type of code-emphasis method. Primary goals include making sure children can: understand letter-sound correspondences, automatically recognize familiar words, and decode unfamiliar words. Researchers advocating for a more phonics-based approach believe that phonemic awareness is a requirement for learning to connect alphabetic symbols to their sounds, and that these letter-sound connections are required for learning to identify individual words and learning to read in general. From a logical standpoint, learning letter-sound correspondences may seem the most salient for beginning readers, especially since words are made up of combinations of letter-sound correspondences. Within a phonics-based approach, there are two types of instruction: an explicit phonics approach and an implicit phonics approach. In an explicit phonics approach, sounds are associated with the letters by themselves, and then are blended together to form words. In the classroom, a teacher might directly tell students the sound represented by an individual letter. Once students have learned a few letter-sound correspondences, they begin to learn to read by blending the sounds together. The main strategy used for identifying words in an explicit phonics approach is based on the student’s knowledge of letter-sound correspondences. When a student encounters an unfamiliar word, they are encouraged to sound it out and they are not directed to the context of the word until after the word has been identified. In this case, context is only a metacognitive strategy used to understand the text as a whole. In an implicit phonics approach, sounds of letters are identified in the context of whole words rather than letters in isolation. During instruction, the teacher might write the word hand on the board, and underline the letter h. Then, the teacher would have the students say “hand” to elicit from them that h makes the sound /h/. In addition, the context of the word and picture clues may be used to sound out unfamiliar words. A common problem that has been identified in using context to teach letter-sound correspondences is that some students fail to learn these correspondences because they are unable to split words into their individual sounds, since they lack the skills needed to infer sounds from a whole word. Evidence from research indicates that a large majority of poor readers are deficient in alphabetic coding and phonemic awareness. As stated earlier, ideal reading instruction should involve both code-emphasis methods and meaning-emphasis methods. As such, more people are against over-emphasis of phonics and prescriptive teaching methods than there are people against phonics instruction itself.
A study by Maddox and Feng (2013) compared the efficacy of whole language reading instruction versus phonics instruction for improving students’ reading fluency and spelling accuracy. The researchers hypothesized that explicit phonics instruction would have a more positive effect on students’ reading fluency and spelling accuracy than whole language instruction, and that the students receiving explicit phonics instruction would show greater gains in reading fluency and spelling accuracy than students receiving whole language instruction. Twenty-two first graders from one classroom were randomly assigned to either the experimental group or the control group. The experimental group became the phonics group and received explicit phonics instruction, while the control group became the whole language group and did not receive explicit phonics instruction. With the experimental group, the teacher taught phonics patterns and the group practiced segmenting, coding, blending and working with these patterns, but did not read any stories. With the control group, the teacher read the students fourteen stories from the Raz-kids reading program; the words in the stories contained the same phonics patterns as those taught in the phonics group and the students focused on picture walks, story predictions, and meaning of vocabulary. Both groups met with their teacher (who was also one of the experimenters) for twenty minutes, five days a week, over a span of four weeks. Before the training sessions began, students’ pretest scores were gathered using the Aimsweb Reading Curriculum Based Measure (RCBM) and the Aimsweb Spelling Curriculum Based Measure (SCBM). After the four weeks of training, the same tests were administered to the students again to calculate posttest scores to measure changes in reading fluency and spelling accuracy. The results indicated no statistically significant differences in reading fluency or spelling accuracy of either group. The phonics group had higher reading scores on average and increased their reading fluency by 8.00 points compared to the whole language group, who increased their reading fluency by 4.09 points. Data for spelling accuracy showed the phonics group had positive results with an increase in 1.00 point while the whole language group regressed with a decrease of -0.27 points. A direct comparison indicates the phonics group made greater gains in both reading fluency and spelling accuracy.

### Phonemic Awareness

How does phonemic awareness affect learning to read? Phonemic awareness is described as the ability to focus on and manipulate phonemes in spoken words. Phonemes are the smallest units that make up spoken language, and are combined to form syllables and words, thus, phonemic awareness is a code-emphasis method. Research has posited that sight-reading words from memory requires phoneme segmentation skills, and that phonemic awareness is thought to help children write words by enabling them to invent letter-sound spellings or retrieve spellings from memory.
A study by Castle, Riach and Nicholson (1994) was done with the aim of determining whether training in phonemic awareness would get children off to a better start in reading and spelling, even if they were already being instructed within a whole language program. The experiment was done with children in New Zealand during their first few months of school, during the time they were just learning to read and write. Thirty 5-year-olds from three different schools were divided and matched into one experimental group and one control group. The experimental group had 20-minute training sessions twice a week for 10 weeks, totalling 6.7 hours in overall training time. The topics covered during these sessions were chosen with the purpose of increasing phonemic awareness, including phoneme segmentation, phoneme substitution, phoneme deletion, and rhyme. The control group had the same amount of instructional time, but the children were involved in process writing activities as part of the whole language approach in New Zealand schools, in which children wrote their own stories and invented their own spellings of words. A series of pretests were administered before the training sessions began, including: Roper’s measure of phonemic awareness, a Wide Range Achievement of Spelling test, an experimental spelling test, and a diction test. These same tests were also administered as posttests after the training sessions were completed. The results from the study showed significant gains for both groups in phonemic awareness, but there was a considerable difference between the experimental and control groups that indicates that the training program used in the study was effective in improving phonemic awareness skills. There was also a significant difference between the groups on two of the spelling tests (Wide Range Achievement of Spelling test and experimental spelling test), showing that improvement in phonemic awareness skills leads to better spelling skills. In conclusion, the findings of the study suggest that the ability to link letters and their sounds is associated with spelling progress, and that phonemic awareness promotes spelling acquisition.
It is important to note that the impact of phonemic awareness instruction is greatest in the preschool and kindergarten years, and may become smaller beyond first grade. As students move beyond first grade, phonemic awareness skills becomes less important than the need to learn spelling patterns. Explicit instruction in phonemic awareness may not be as effective for older students, however; it may be effective for children who have not made normal reading progress and students with reading disabilities, thus, phonemic awareness skill instruction can help with these students’ reading and spelling difficulties.

### Whole Language Approach

In a whole language approach, literacy is viewed as a top-down process. The whole language approach is a philosophy that emphasizes reading words and sentences are of greater importance than learning the sounds and phonemes that make up words. Letter sound-correspondences are not taught independently of reading, and so it is a type of meaning-emphasis method. Students are engaged with language as a whole, rather than separating out the parts and practicing each one on its own. Reading is meant to occur naturally, as when children first learned to speak: with very little direct instruction and lots of encouragement. By experiencing the wholeness of reading, only then do students learn the subparts of words. A whole language approach is very much a context-driven process, and words are not presented out of context. In order to make sense of the text at hand, students are meant to use their store of accumulated knowledge, illustrations, phonetic strategies and prior experiences to make sense of the text and any unfamiliar words. Teachers who support a whole language approach often use “real books” rather than basal readers (often seen in code-emphasis methods) because they promote reading fluency and making meanings. Furthermore, it is stressed in a whole language approach that the process of learning is not always smooth and certain, and that students must take ownership and responsibility for their learning goals. The whole language approach is often looked at from a Vygotskian perspective: teachers are mediators who make learners’ transactions with the world possible. According to Vygotsky, learning is a social interaction, and children need to converse with others in order to exchange and form meanings. As such, the whole language approach to reading instruction flourishes through all kinds of social interaction because learners can more effectively solve problems when they are collaborating on the same problems and developing the same skills. When students work together, their discussion can often lead to not only solving the problem at hand, but also forming new meanings and accumulating new knowledge from information derived from collaboration. Vygotsky also stated that literacy experiences should be structured so that they are necessary for something, that is, there is a purpose for learning how to read and write. Using examples in class that are relatable or have a parallel comparison to students’ experiences outside the classroom can increase reading motivation. One other Vygotskian model for reading instruction using a whole language approach is to work within a student’s zone of proximal development, which is a person's area of learning between what they can do alone and what they can do with help. During reading, asking students questions about the language or for clarification can build on skills they already posses. Asking students questions about the material and fostering meaning making can have a positive effect on reading comprehension. Research suggests that when real reading is considered the main element of whole-language reading instruction, the approach is beneficial to reading comprehension tests. After all, reading comprehension is a main goal of reading instruction.
Manning and Kamii’s study (2000) on reading and writing tasks in kindergarten students compared the effectiveness of whole language versus isolated phonics instruction. Thirty-eight children from two kindergarten classes at one school in the United States were examined. The teacher of one class identified as a whole language teacher, and the other as a phonics teacher. In the phonics classroom, the students had daily phonics worksheets and oral-sound training, and often used flashcards to practice sight words and letter-sound correspondences. There were posters that displayed various phonics rules, marked with symbols to indicate long or short vowel sounds. In the whole language classroom, children did a lot of shared reading and writing, such as independent journal writing and also group writing activities. Books were read aloud by the teacher for over an hour each day, spread out over the day. All the children were interviewed individually five times throughout the year, where they were asked to write eight words and then read two to four sentences. The researchers then scored the students according to their level of writing and ability to identify a word in a given sentence. Results showed that although the whole language group started out the year at a lower level, many more children ended the year at a higher level than the phonics group. In the phonics group, there were more instances of regression, and overall advanced less and became more confused during their kindergarten year.

### Schema Theory

Schema theory is an explanation of how readers use their prior knowledge to comprehend text. The term schema (plural: schemata) was first introduced in psychology to describe a mental framework that organizes a person’s knowledge, and was then later used in reading instruction to describe the role that students’ prior knowledge plays in reading comprehension. According to schema theory, people organize everything they know into schemata. Everyone’s schemata are individualized, and the more elaborated a person’s schema is for any specific topic, the more easily they will be able to learn new information in that topic area. A person’s existing knowledge structures are malleable and constantly changing; when a person learns new information, their pre-existing schema may need to adjust to accommodate this new information. In regards to reading, the main idea of schema theory is that written text does not carry meaning alone; rather, the text provides guidance for how readers should retrieve or construct meaning from previously existing knowledge structures. In addition to having schemata for content, learners also have schemata for reading processes and different kinds of text structures. Understanding the text is a reciprocal and interactive process between the reader’s prior knowledge and the actual text because effective comprehension requires the ability to relate prior knowledge to the text. Schema theory has two kinds of processing during reading comprehension: bottom-up processing is schema activation (when textual stimuli signal recall of relevant schemata) through specific information in the text, while top-down processing starts with general knowledge and moves down towards more specific details, and as more stimuli are presented, the reader’s specific schemata pertaining to the text can be activated. These two types of processing occur simultaneously and interactively in order to comprehend text.
Research suggests that when readers activate prior knowledge by previewing the text, they use schemata immediately when they start reading and focus instead on new information, with the aim of building connections between old and new information. Without existing schema regarding the structure or content of the text, reading comprehension will not occur. Due to the importance of pre-existing knowledge, teachers can build on and activate students’ schema prior to reading. Previewing the text can include brainstorming or group discussions, or even reviewing strategies and skills for reading the text. It is also important to note that differences and students’ schemata relate in differences in reading comprehension, but previewing text also allows a reader to realize in advance that they have knowledge of the subject, increasing the student’s self-efficacy for that reading task.
A study of Iranian students examined whether schema activation through pre-reading activities has an effect on reading comprehension of culturally based texts. The subjects consisted of seventy-six English as a Foreign Language (EFL) students either majoring in English Literature, or majoring in Teaching English as a Foreign Language (TEFL). All the participants were sophomore students in their fourth semester at the Islamic Azad University of Kerman in Iran. To make sure all students were of the same English proficiency, they were categorized from a basic to upper-intermediate level of English based on their results on the Oxford Placement Test. Participants were then separated into two groups: one experimental group and one control group. The researchers tested two null hypotheses: the first, that there would be no significant difference between the mean pretest and mean posttest scores of the experimental group after schema activation; and the second, that there is no relationship between the pretest and posttest scores of the experimental group when the students’ schemas are activated through pre-reading activities. During the procedure, both the experimental and control groups were administered a reading comprehension test about the origins and customs of Halloween as a pretest. The topic was chosen because the holiday is culturally loaded, and so students from another country may have difficulties understanding it. Then, the experimental group had two training sessions of schema activation with a researcher– these sessions included pre-reading activities, previewing, pre-teaching vocabulary, and looking at pictures to make the students more familiar with Halloween customs. The group was then asked to talk about what they knew about Halloween, and this served as a basis for group discussion. During the training sessions, the researcher asked the group questions about new vocabulary word, and provided synonyms and definitions when necessary. The experimental group was then given the same reading comprehension test as a posttest two weeks later. The control group was only administered the initial pretest, and did not have any training sessions or a posttest. The results showed that both null hypotheses were rejected, and that both groups scored about the same on the pretest – the experimental group with a mean score of 16.42 and the control group with a mean score of 16.57 – but after the experimental group’s training sessions, their posttest mean score increased to 18.70. The researchers also found a significant relationship between the pretest and posttest scores of the experimental group. In conclusion, as the experimental group received more background knowledge, reading comprehension was enhanced, and the researchers strongly believe that the results and implications of the study are applicable to other less culturally bound materials. Teacher guidance is crucial for helping students connect new information to existing schemas, and spending time on schema activation activities leads to better student performance.

## Assessing Reading Progress

When children start school, one of the very first things they start to learn is reading. Formally learning to read starts in kindergarten and continues throughout our lifetime. When children are learning to read it is important to give them feedback and assess them along the way to see how they are doing. In order to gauge the success and improvement of a student's reading skills, there are some frequently assessed markers to determine one's reading progress: phonemic awareness, letter knowledge, and oral reading fluency. Before children learn to print, they need to be aware of how the sounds of the letters in words work. Phonemic awareness is basically assessing just that, children need to have the ability to notice, think about, and manipulate the phonemic segments of spoken words. If children are not aware of the sound structure of language they will be not be able to attend to the separate sounds in spoken words and thus will not be able to establish letter-sound correspondences. It is believed that this letter-sound link is a foundational skill in decoding, and are important early skills in literacy. Letter knowledge is measured by children’s ability to name upper and lower case letters and know the sounds of each of the letters in the alphabet. This is key for children to know because it is only when they understand the letters and their sounds that they can start to read. While reading, children have to know how to sound out words, decode them, and pronounce them and this is only possible if they have mastered the letters and their sounds. The third type of assessment used to measure early reading progress is known as reading fluency. This is basically trying to measure children’s ability to read quickly, accurately, and with expression. This type of assessment is controversial for some people because they don’t believe that by reading quickly children have progressed. Reading quickly and accurately isn’t the real purpose of reading, it's understanding and recalling what you have read that is important.
Effective assessment should be an ongoing process and shouldn’t just stop after children can quickly read a text and understand it. Which is why there are some authentic assessment measures that teachers are able to use to determine students’ skills and learning and to inform present and future instruction. Teachers are able to use assessments such as oral and written story retellings which will informally measure students’ reading comprehension, literacy portfolios can be used to showcase student’s oral and written processes, products, and skills, and checklists can be used to help the teacher’s observations of students and to determine students’ literacy needs and growth.
Assessing the reading progress of students’ is important and makes teachers aware of what stage their students’ are at. This will help teachers to better assist their students’ needs. Assessing students has a lot of benefits to it but teachers should always be aware that assessing isn’t everything and not all students can be assessed at the same time or in the same way.

## Glossary

Bottom-up processing: An approach to information processing that involves piecing together smaller pieces of information and building up to bigger concepts.
Code-emphasis methods: Approaches to reading that stress the importance of decoding letters and words, and letter-sound correspondences.
Discourse: Structured, coherent sequences of language in which sentences are combined into higher order units, such as paragraphs, narratives, and expository texts, i.e., conversations.
Explicit phonics approach: Reading instruction in which the sounds associated with letters (letter-sound correspondences) are identified first independently, then are later blended together to form words.
Full Alphabetic Stage: Readers who, as they conclude the early stages of reading, can identify the separate sounds in words and understand that spellings correspond to pronunciation.
Implicit phonics approach: Reading instruction in which the sounds of letters are identified within the whole word rather than independently.
Meaning-emphasis methods: Approaches to reading that focus more on making meanings from the words and using one's general knowledge store.
Partial Alphabetic Stage: Readers who, in the early stages of reading, read by associating some but not all of words’ letters with sounds.
Phoneme: The smallest unit of sound that makes up a word.
Phonemic Awareness: The ability to identify and manipulate the individual phonemes in a word.
Pragmatics: The meanings, messages, and uses of language.
Pre-Alphabetic Stage: It is the stage when children know quite a bit of literacy but do not know how to read any words.
Schema:The idea of a mental framework that helps us organize knowledge and the relationships between these pieces of information.
Schema activation:The process by which textual stimuli signal the recall of relevant schemata from memory for the present reading task.
Semantics: The study of words and their meanings.
Syntax: Ways words in a language are grouped into larger units, such as in phrases, clauses, and sentences.
Top-down processing: An approach to information processing that involves using general knowledge to fill in what is known and working down towards smaller details.
Zone of proximal development: A concept created by Vygotsky that describes the area of learning between what a student is capable of doing by themselves, and what they can do with help, i.e., from a teacher, parents, or caregiver.

## Recommended Readings

Bukowiecki, E. M. (2007). Teaching children how to read. Kappa Delta Pi Record, 43(2), 58-65. doi: 10.1080/00228958.2007.10516463
Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188. doi: 10.1207/s1532799xssr0902_4
Hempestall, K. (2005). The whole language‐phonics controversy: A historical perspective. Australian Journal of Learning Disabilities, 10(3-4), 19-33. doi: 10.1080/19404150509546797
Tracey, D.H., & Morrow, L.M. (2012). Lenses on Reading, Second Edition : An Introduction to Theories and Models. Guilford Press.

## References

* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* ↑ Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* ↑ Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* ↑ Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* ↑ Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* ↑ Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* ↑ Wu, H., & Wu, C. (2011). Exploring the Development of Fifth Graders' Practical Epistemologies and Explanation Skills in Inquiry-Based Learning Classrooms. Research In Science Education, 41(3), 319-340.
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Shtulman, A., & Calabi, P. (2013). Tuition vs. Intuition: Effects of Instruction on Naive Theories of Evolution. Merrill-Palmer Quarterly, 59(2), 141-167.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* ↑ Longfield, J. (2009). Discrepant Teaching Events: Using an Inquiry Stance to Address Students' Misconceptions. International Journal Of Teaching And Learning In Higher Education, 21(2), 266-271.
* ↑ Longfield, J. (2009). Discrepant Teaching Events: Using an Inquiry Stance to Address Students' Misconceptions. International Journal Of Teaching And Learning In Higher Education, 21(2), 266-271.
* ↑ Longfield, J. (2009). Discrepant Teaching Events: Using an Inquiry Stance to Address Students' Misconceptions. International Journal Of Teaching And Learning In Higher Education, 21(2), 266-271.
* ↑ Longfield, J. (2009). Discrepant Teaching Events: Using an Inquiry Stance to Address Students' Misconceptions. International Journal Of Teaching And Learning In Higher Education, 21(2), 266-271.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ (Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011).Cognitive psychology and instruction (5th ed.). Boston, MA: Pearson)
* ↑ Yoon, H.G., Joung, Y. J. & Kim, M. (2011) The Challenges of Science Inquiry Teaching for Pre-Service Teachers in Elementary Classrooms: Difficulties on and under the Scene. Research in Science Education, 42(3), 1-20 DOI 10.1007/s11165-011-9212-y0.
* ↑ Kensinger, S. H. (2013). Impact of instructional approaches to teaching elementary science on student achievement. Dissertation Abstracts International Section A, 73.
* ↑ Dinescu, L., Dinica, M. & Miron, C. (2010). Active strategies - option and necessity for teaching science in secondary and high school education. Procedia - Social and Behavioral Sciences, 2(2), 3724–3730.
* ↑ Childs, A., Sorensen, P., & Twidle, J. (2011). Using the Internet in science teaching? Issues and challenges for initial teacher education. Technology, Pedagogy And Education, 20(2), 143-160. doi:10.1080/1475939X.2011.588413
* ↑ Childs, A., Sorensen, P., & Twidle, J. (2011). Using the Internet in science teaching? Issues and challenges for initial teacher education. Technology, Pedagogy And Education, 20(2), 143-160. doi:10.1080/1475939X.2011.588413
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Swanson, H. L., & Ashbaker, M. H. (2000). Working memory, short-term memory, speech rate, word recognition and reading comprehension in learning disabled readers: Does the executive system have a role? Intelligence, 28(1), 1-30. doi:10.1016/S0160-2896(99)00025-2
* ↑ Swanson, H. L., & Ashbaker, M. H. (2000). Working memory, short-term memory, speech rate, word recognition and reading comprehension in learning disabled readers: Does the executive system have a role? Intelligence, 28(1), 1-30. doi:10.1016/S0160-2896(99)00025-2
* ↑ Verhoevan, L., Reitsma, P., & Siegel, L. S. (2011). Cognitive and linguistic factors in reading acquisition. Reading and Writing, 24(4), 387-394. doi:10.1007/s11145-010-9232-4
* ↑ Verhoevan, L., Reitsma, P., & Siegel, L. S. (2011). Cognitive and linguistic factors in reading acquisition. Reading and Writing, 24(4), 387-394. doi:10.1007/s11145-010-9232-4
* ↑ Verhoevan, L., Reitsma, P., & Siegel, L. S. (2011). Cognitive and linguistic factors in reading acquisition. Reading and Writing, 24(4), 387-394. doi:10.1007/s11145-010-9232-4
* ↑ Verhoevan, L., Reitsma, P., & Siegel, L. S. (2011). Cognitive and linguistic factors in reading acquisition. Reading and Writing, 24(4), 387-394. doi:10.1007/s11145-010-9232-4
* ↑ Verhoevan, L., Reitsma, P., & Siegel, L. S. (2011). Cognitive and linguistic factors in reading acquisition. Reading and Writing, 24(4), 387-394. doi:10.1007/s11145-010-9232-4
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Brooks, F. D. (1926). How to diagnose reading deficiencies. In , The applied psychology of reading: With exercises and directions for improving silent and oral reading (pp. 168-185). New York, NY, US: D Appleton & Company. doi:10.1037/14870-012
* ↑ Brooks, F. D. (1926). How to diagnose reading deficiencies. In , The applied psychology of reading: With exercises and directions for improving silent and oral reading (pp. 168-185). New York, NY, US: D Appleton & Company. doi:10.1037/14870-012
* ↑ Brooks, F. D. (1926). How to diagnose reading deficiencies. In , The applied psychology of reading: With exercises and directions for improving silent and oral reading (pp. 168-185). New York, NY, US: D Appleton & Company. doi:10.1037/14870-012
* ↑ Brooks, F. D. (1926). How to diagnose reading deficiencies. In , The applied psychology of reading: With exercises and directions for improving silent and oral reading (pp. 168-185). New York, NY, US: D Appleton & Company. doi:10.1037/14870-012
* ↑ Brooks, F. D. (1926). How to diagnose reading deficiencies. In , The applied psychology of reading: With exercises and directions for improving silent and oral reading (pp. 168-185). New York, NY, US: D Appleton & Company. doi:10.1037/14870-012
* ↑ Brooks, F. D. (1926). How to diagnose reading deficiencies. In , The applied psychology of reading: With exercises and directions for improving silent and oral reading (pp. 168-185). New York, NY, US: D Appleton & Company. doi:10.1037/14870-012
* ↑ Eklund, K. M., Torppa, M., & Lyytinen, H. (2013). Predicting reading disability: Early cognitive risk and protective factors. Dyslexia: An International Journal Of Research And Practice, 19(1), 1-10. doi:10.1002/dys.1447
* ↑ Eklund, K. M., Torppa, M., & Lyytinen, H. (2013). Predicting reading disability: Early cognitive risk and protective factors. Dyslexia: An International Journal Of Research And Practice, 19(1), 1-10. doi:10.1002/dys.1447
* ↑ Aaron, P. G., Joshi, R. M., Gooden, R., & Bentum, K. E. (2008). Diagnosis and treatment of reading disabilities based on the component model of reading: An alternative to the discrepancy model of LD. Journal Of Learning Disabilities, 41(1), 67-84. doi:10.1177/0022219407310838
* ↑ Aaron, P. G., Joshi, R. M., Gooden, R., & Bentum, K. E. (2008). Diagnosis and treatment of reading disabilities based on the component model of reading: An alternative to the discrepancy model of LD. Journal Of Learning Disabilities, 41(1), 67-84. doi:10.1177/0022219407310838
* ↑ Aaron, P. G., Joshi, R. M., Gooden, R., & Bentum, K. E. (2008). Diagnosis and treatment of reading disabilities based on the component model of reading: An alternative to the discrepancy model of LD. Journal Of Learning Disabilities, 41(1), 67-84. doi:10.1177/0022219407310838
* ↑ Aaron, P. G., Joshi, R. M., Gooden, R., & Bentum, K. E. (2008). Diagnosis and treatment of reading disabilities based on the component model of reading: An alternative to the discrepancy model of LD. Journal Of Learning Disabilities, 41(1), 67-84. doi:10.1177/0022219407310838
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Hoillingworth, L. S. (1923). Reading. In , Special talents and defects: Their significance for education (pp. 57-97). New York, NY, US: MacMillan Co. doi:10.1037/13549-004
* ↑ Hoillingworth, L. S. (1923). Reading. In , Special talents and defects: Their significance for education (pp. 57-97). New York, NY, US: MacMillan Co. doi:10.1037/13549-004
* ↑ Eklund, K. M., Torppa, M., & Lyytinen, H. (2013). Predicting reading disability: Early cognitive risk and protective factors. Dyslexia: An International Journal Of Research And Practice, 19(1), 1-10. doi:10.1002/dys.1447
* ↑ Dearborn, W. F. (1932). Difficulties in learning. In W. V. Bingham, W. V. Bingham (Eds.) , Psychology today: Lectures and study manual (pp. 186-194). Chicago, IL, US: University of Chicago Press. doi:10.1037/13342-021
* ↑ Brooks, F. D. (1926). How to diagnose reading deficiencies. In , The applied psychology of reading: With exercises and directions for improving silent and oral reading (pp. 168-185). New York, NY, US: D Appleton & Company. doi:10.1037/14870-012
* ↑ Zentall, S. S., & Lee, J. (2012). A reading motivation intervention with differential outcomes for students at risk for reading disabilities, ADHD, and typical comparisons: 'Clever is and clever does'. Learning Disability Quarterly, 35(4), 248-259. doi:10.1177/0731948712438556
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Schwartz, R. M. (2005). Literacy Learning of At-Risk First-Grade Students in the Reading Recovery Early Intervention. Journal Of Educational Psychology, 97(2), 257-267. doi:10.1037/0022-0663.97.2.257
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Ehri, L. C. (2005). Learning to read words: Theory, findings, and issues. Scientific Studies of Reading, 9(2), 167-188, doi: 10.1207/s1532799xssr0902_4
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bukowiecki, E. M. (2007). Teaching children how to read. Kappa Delta Pi Record, 43(2), 58-65. doi: 10.1080/00228958.2007.10516463
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bukowiecki, E. M. (2007). Teaching children how to read. Kappa Delta Pi Record, 43(2), 58-65. doi: 10.1080/00228958.2007.10516463
* ↑ Bukowiecki, E. M. (2007). Teaching children how to read. Kappa Delta Pi Record, 43(2), 58-65. doi: 10.1080/00228958.2007.10516463
* ↑ Bukowiecki, E. M. (2007). Teaching children how to read. Kappa Delta Pi Record, 43(2), 58-65. doi: 10.1080/00228958.2007.10516463
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Vellutino, F.R. (1991). Introduction to three studies on reading acquisition: Convergent findings on theoretical foundations of code-oriented versus whole-language approaches to reading instruction. Journal of Educational Psychology, 83(4). 437-443. doi:10.1037/0022-0663.83.4.437
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Stein, M., Johnson, B., & Gutlohn, L., (1999). Analyzing Beginning Reading Programs: The Relationship Between Decoding Instruction and Text. Remedial and Special Education, 20(5), 275-287. doi:10.1177/074193259902000503
* ↑ Vellutino, F.R. (1991). Introduction to three studies on reading acquisition: Convergent findings on theoretical foundations of code-oriented versus whole-language approaches to reading instruction. Journal of Educational Psychology, 83(4). 437-443. doi:10.1037/0022-0663.83.4.437
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Maddox, K., & Feng, J. (2013). Whole Language Instruction vs. Phonics Instruction: Effects on Reading Fluency and Spelling Accuracy of First Grade Students. Retrieved from ERIC database. (ED545621)
* ↑ Ehri, L. C., Nunes, S. R., Willows, D. M., Schuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T.. (2001). Phonemic Awareness Instruction Helps Children Learn to Read: Evidence from the National Reading Panel's Meta-Analysis. Reading Research Quarterly, 36(3), 250–287. Retrieved from http://www.jstor.org/stable/748111
* ↑ Ehri, L. C., Nunes, S. R., Willows, D. M., Schuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T.. (2001). Phonemic Awareness Instruction Helps Children Learn to Read: Evidence from the National Reading Panel's Meta-Analysis. Reading Research Quarterly, 36(3), 250–287. Retrieved from http://www.jstor.org/stable/748111
* ↑ Castle, J.M., Riach, J., & Nicholson, T. (1994). Getting Off to a Better Start in Reading and Spelling: The Effects of Phonemic Awareness Instruction Within a Whole Language Program. Journal of Educational Psychology, 86(3), 350-359. doi:10.1037/0022-0663.86.3.350
* ↑ Ehri, L. C., Nunes, S. R., Willows, D. M., Schuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T.. (2001). Phonemic Awareness Instruction Helps Children Learn to Read: Evidence from the National Reading Panel's Meta-Analysis. Reading Research Quarterly, 36(3), 250–287. Retrieved from http://www.jstor.org/stable/748111
* ↑ Ehri, L. C., Nunes, S. R., Willows, D. M., Schuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T.. (2001). Phonemic Awareness Instruction Helps Children Learn to Read: Evidence from the National Reading Panel's Meta-Analysis. Reading Research Quarterly, 36(3), 250–287. Retrieved from http://www.jstor.org/stable/748111
* ↑ Ehri, L. C., Nunes, S. R., Willows, D. M., Schuster, B. V., Yaghoub-Zadeh, Z., & Shanahan, T.. (2001). Phonemic Awareness Instruction Helps Children Learn to Read: Evidence from the National Reading Panel's Meta-Analysis. Reading Research Quarterly, 36(3), 250–287. Retrieved from http://www.jstor.org/stable/748111
* ↑ McCaslin, M. M.. (1989). Whole Language: Theory, Instruction, and Future Implementation. The Elementary School Journal, 90(2), 223–229. Retrieved from http://www.jstor.org.proxy.lib.sfu.ca/stable/1002031
* ↑ Stone, T. J. (1993). Whole language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Vellutino, F.R. (1991). Introduction to three studies on reading acquisition: Convergent findings on theoretical foundations of code-oriented versus whole-language approaches to reading instruction. Journal of Educational Psychology, 83(4). 437-443. doi:10.1037/0022-0663.83.4.437
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ McCaslin, M. M.. (1989). Whole Language: Theory, Instruction, and Future Implementation. The Elementary School Journal, 90(2), 223–229. Retrieved from http://www.jstor.org.proxy.lib.sfu.ca/stable/1002031
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Stone, T. J. (1993). Whole-language reading processes from a Vygotskian perspective. Child & Youth Care Forum, 22(5), 361-373. doi:10.1007/BF00760945
* ↑ Krashen, S.D. (2002). Defending whole language: the limits of phonics instruction and the efficacy of whole language instruction. Reading Improvement, 39(1), 32-42.
* ↑ Manning, M., & Kamii, C. (2000). Whole Language vs. Isolated Phonics Instruction: A Longitudinal Study in Kindergarten With Reading and Writing Tasks, Journal of Research in Childhood Education, 15(1), 53-65. doi: 10.1080/02568540009594775
* ↑ Shuying, A. (2013). Schema Theory in Reading. Theory & Practice in Language Studies, 3(1), 130-134. doi:10.4304/tpls.3.1.130-134
* ↑ Shuying, A. (2013). Schema Theory in Reading. Theory & Practice in Language Studies, 3(1), 130-134. doi:10.4304/tpls.3.1.130-134
* ↑ Tracey, D.H., & Morrow, L.M. (2012). Lenses on Reading, Second Edition : An Introduction to Theories and Models. Guilford Press.
* ↑ Tracey, D.H., & Morrow, L.M. (2012). Lenses on Reading, Second Edition : An Introduction to Theories and Models. Guilford Press.
* ↑ Tracey, D.H., & Morrow, L.M. (2012). Lenses on Reading, Second Edition : An Introduction to Theories and Models. Guilford Press.
* ↑ Shuying, A. (2013). Schema Theory in Reading. Theory & Practice in Language Studies, 3(1), 130-134. doi:10.4304/tpls.3.1.130-134
* ↑ Tracey, D.H., & Morrow, L.M. (2012). Lenses on Reading, Second Edition : An Introduction to Theories and Models. Guilford Press.
* ↑ Shuying, A. (2013). Schema Theory in Reading. Theory & Practice in Language Studies, 3(1), 130-134. doi:10.4304/tpls.3.1.130-134
* ↑ Shuying, A. (2013). Schema Theory in Reading. Theory & Practice in Language Studies, 3(1), 130-134. doi:10.4304/tpls.3.1.130-134
* ↑ Shuying, A. (2013). Schema Theory in Reading. Theory & Practice in Language Studies, 3(1), 130-134. doi:10.4304/tpls.3.1.130-134
* ↑ Al-Faki, I., & Siddiek, A. G. (2013). The role of background knowledge in enhancing reading comprehension. World Journal of English Language, 3(4), 42-n/a. Retrieved from https://www.proquest.com/docview/1525981385
* ↑ Tracey, D.H., & ;Morrow, L.M. (2012). Lenses on Reading, Second Edition : An Introduction to Theories and Models. Guilford Press.
* ↑ Tracey, D.H., & ;Morrow, L.M. (2012). Lenses on Reading, Second Edition : An Introduction to Theories and Models. Guilford Press.
* ↑ Al-Faki, I., & Siddiek, A. G. (2013). The role of background knowledge in enhancing reading comprehension. World Journal of English Language, 3(4), 42-n/a. Retrieved from https://www.proquest.com/docview/1525981385
* ↑ Maghsoudi, Najmeh. (2012). The impact of schema activation on reading comprehension of cultural texts among Iranian EFL learners. Canadian Social Science, 8(5), 196-201. doi:10.3968/j.css.1923669720120805.3131
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Walsh, R. (2009). Word games: The importance of defining phonemic awareness for professional discourse. Australian Journal of Language & Literacy, 32(3), 211-225. Retrieved from http://web.a.ebscohost.com.proxy.lib.sfu.ca/ehost/pdfviewer/pdfviewer?sid=e203b503-b43d-4f66-b2e9-2534fa1b2905%40sessionmgr4002&vid=13&hid=4104
* ↑ Walsh, R. (2009). Word games: The importance of defining phonemic awareness for professional discourse. Australian Journal of Language & Literacy, 32(3), 211-225. Retrieved from http://web.a.ebscohost.com.proxy.lib.sfu.ca/ehost/pdfviewer/pdfviewer?sid=e203b503-b43d-4f66-b2e9-2534fa1b2905%40sessionmgr4002&vid=13&hid=4104
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bukowiecki, E. M. (2007). Teaching children how to read. Kappa Delta Pi Record, 43(2), 58-65. doi: 10.1080/00228958.2007.10516463
* ↑ Bukowiecki, E. M. (2007). Teaching children how to read. Kappa Delta Pi Record, 43(2), 58-65. doi: 10.1080/00228958.2007.10516463
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.
* ↑ Bruning, R.H., Schraw, G.J., & Norby, M.M. (2011). Cognitive Psychology and Instruction (5th ed). Pearson.

# Learning Mathematics

Mathematics contains many areas of study such as geometry, algebra, calculus, and probability; each requiring the mastery of specialized concepts and procedures. The challenges of teaching and learning mathematics can be understood and overcome through analysis of cognitive processes. In this chapter we examine cognitive theories and research that inform the practice of mathematics education. We discuss the relevant aspects of Piaget’s theory of cognitive development and the criticism that it has received. We explain the factors that influence individual students' abilities to learn mathematics and how teachers can account for these factors when designing lessons.

## What is Mathematics?

Mathematics is the study of numbers, quantities, geometry and space, as well as their relationships and functions. It utilizes a combination of conceptual, procedural, and declarative knowledge. In order to successfully solve mathematical problems, students need to acquire this set of knowledge. To fully engage in their learning of mathematics, students must first gain a conceptual understanding, which requires utilizing background knowledge of learned concepts. Conceptual understanding of mathematics leads to the acquisition of more mathematical knowledge, helping to construct the other strands of mathematical proficiency: productive disposition, procedural fluency, strategic competence and adaptive reasoning. Growth in each proficiency leads to growth in the other proficiencies and leads to more knowledge. That is, conceptual knowledge enhances procedural knowledge and so on. For example, there are many different algorithms in mathematics that students need to be familiar with. When students have a clear understanding of mathematical principles and concepts, they will be able to select and re-create the appropriate algorithm for any mathematical problem. This demonstrates the connection between conceptual knowledge and procedural knowledge because students can have many learned strategies but they have to select the correct one and build upon it. In addition, when there are successes or failures while using certain procedures to solve complex mathematical problems, students can often learn more. Students can learn from their failure by self-questioning their mistakes and can reconstruct their existing knowledge. As a result, this increases their conceptual knowledge. Declarative knowledge is definitely related to both conceptual and procedural knowledge because it requires students to retrieve mathematical concepts (i.e., conceptual knowledge) and specific mathematical algorithms (i.e., procedural knowledge) from the long-term memory. Deficiency in any one, or all, of these knowledge areas may cause learning difficulties in mathematics. Thus, this combination of conceptual, procedural, and declarative knowledge influences learning since they are all associated with one another.

## Cognitive Theory and Mathematics
### Piaget's theory of Cognitive Development

Jean Piaget has indicated four primary stages of cognitive development from birth to young adulthood, these includes sensorimotor (from birth to age 2), preoperational (about age 2 to age 7), concrete operational (about age 7 to age 11), and formal operational (about age 11 to age 15). Although everyone progresses through these stages differently, Piaget believed that every child would eventually experience every stage of thinking in the sequence and no one would miss a stage because one would not be able to develop to the next stage until they understand the previous one; it’s just a matter of time.
Piaget also pointed out that children’s learning is usually developed through movement and the five senses from birth to age 2. During the infants’ first few weeks, they start learning how to track objects and to get a hold of them by constantly practicing, which can help the parts of the brain that process and connect visual and motor behaviour to start developing. Once the infants recognize that learning follows by repetition, then they will start learning how to plan in advance and reach for the objects that they want by using a more efficient approach. Piaget claimed that infants are able to link numbers to objects at this stage and there is also evidence that children have already acquired some knowledge of the concepts of the numbers and counting. In order to develop the mathematical skills of infants at this stage, educators can offer activities that will integrate with numbers and counting. For example, educators can read books that have pictograms in them. This not only helps children to relate the pictures of objects to their corresponding numbers, but also helps build their reading and comprehension capabilities. During this period, Piaget has demonstrated that infants can already build their own ways of dealing with objects and knowledge about them, which can support gains reflective intelligence. Since Piaget believed that an individual needs to build upon knowledge that is acquired from the prior stage and therefore cannot move to the next stage until the current stage is mastered. Thus, in order to enhance infants' understanding of numbers, educators can provide a general foundation of mathematics by engaging activities that incorporate counting.
Children start acquiring language ability, symbolic thought, egocentric perspective and some degree of logic at around age 2 to age 7. During this period, children learn how to employ problem-solving skills that integrate with objects, such as numbers, blocks, etc. Although children have already gained some knowledge of concepts of numbers, they only have limited logic association, and cannot process operations in a reverse order. For example, children who understand that 5+3=8 may not have the mindset that 3+5=8 also. According to Piaget, this is because children can only identify one aspect or dimension of an object with the loss of other aspects. In order to enhance the children’s mathematical capabilities in this period, educators can ask them to build a specific object by using building blocks. While they are building it, they can learn how to group them based on their identical features, and also help them understanding that there are always multiple methods of combining them together.
According to Piaget, children’s cognitive development accelerates between ages 7 and 11. They can start using their five senses to distinguish objects, which can help them to identify two or three aspects of dimensions at once. For instance, Piaget used an experiment of pouring the same amount of liquid into different size containers. Children at this stage are able to notice the levels of liquid will be different based on the dimension of the container. Another major cognitive growth that occurs during this period is the ability of classification and seriation to separate objects.Children learn classification by grouping objects based on similar features, and acquire the ability of seriation by categorizing objects based on their increased or decreased value, such as length, width, volume, etc. Even though they may have already acquired some basic arithmetical operations at this stage, they do not know how to apply these concepts into solving math problems. For example, when they are being asked to count the pieces that are made of 3 rows of 5 building blocks, they do not know how to apply multiplication while counting. In other words, the abstract concepts of arithmetic must be directly related to physically available elements and operations. This also implies that they are still not capable of setting up a consistent system based on measurement at this stage.
The final stage of cognitive development often occurs at around age 11 to age 15. At this stage, children are able to form their own theories and construct their own mathematics concepts. They can also relate abstract concepts to concrete situations now. For example, when they encounter an algebra problem, they are now able to solve it by themselves instead of having a teacher to refer to a concrete condition. The reason that they can now develop abstract thought patterns into concrete situations is that that they start building their reasoning skills, which includes clarification, inference, evaluation, and application. In order to make students comfortable with these concepts, teachers can teach students on how to separate the word problems and understand the differences between related and unrelated information in the problem.
Piaget believed that if a child has a difficulty understanding a concept, it is because of the too-rapid progress from the qualitative structure of the problem to the mathematical formulation. According to Piaget, in order to help the children to understand the concept, teachers should find an active approach that allows children to explore spontaneously, so they can learn and reconstruct their own concept, instead of having the teachers to give them the answers directly.

### Critiques of Piaget's Theory

| Piaget’s belief of cognitive development | Criticism |
| 1) Children start developing an understanding of the object permanence | Piaget overlooked children’s need for motivation Children’s memory capacity has increased |
| 2) Children’s sensory abilities and cognitive development occur in their first 6 months of birth | Not all learners are the same, they might be placed in a higher or lower category based on their unique abilities |
| 3) Every child will experience the four stages in a specific order | Piaget neglected the external factors, such as heredity culture, and education |
| 4) Piaget separated the cognitive development into definite stages | The stages of cognitive development should be viewed as a gradual and continuous progress |
Even though Piaget’s theory is widely used by teachers to monitor their students’ cognitive development in the classroom nowadays, his theory is controversial. Lots of educators rely on Piaget’s theory to measure students’ readiness for learning math. On the other hand, Hiebert and carpenter advised that Piaget's theory is not a useful guide, as lots of researches have proved that children who fail to follow Piaget’s theory are still able to learn the math concepts and skills. While Piaget focused on children’s internal exploration for knowledge, and believed that children start developing an understanding of object permanence (such as how to track for a hidden object) from birth to age 2, other researchers argue that Piaget neglected the children's need for motivation. Berger believes that external motivations and teachings play an important impact also. Kagan believes that the reason why an infant is able to reach for objects even with displacement is because their memory capacity has increased, not, as Piaget pointed out, in terms of the new cognitive structure. Piaget has also been criticized for broadly speaking of children’s abilities. He deduced that children’s sensory abilities and cognitive development occur in their first six months of birth. While Piaget believed that each child must go through those stages in a particular order, Heuvel-Panhuize argued that Piaget’s theory underestimates young children’s abilities. For example, he found that since early childhood teachers’ belief of stages of cognitive development deeply relied on Piaget’s theory, they may have lower expectations for children’s knowledge of symbols, the counting sequence, and arithmetic operations than what the children are actually capable of. Beger also argued that their perceptual learning might actually develope before birth. Even though a child is supposed to be in a certain stage based on his or her age, not all learners are the same. They might be placed in a higher or lower stage based on their unique abilities. For instance, Gelman and Gallistel have pointed out that children in their preoperational stage are capable of thinking abstractly in terms of counting objects. In addition, Piaget fails to demonstrate the aspects of emotional and personality development of children. Even though Piaget’s theory explains an effective approach that can measure children’s intelligence and memory development, he neglects the remarkable aspects of creativity and social interaction of individuals. Christina Erneling argues that the pattern of development can be established only if the children are put in the right conditions. She believes that any concepts of learning require an expansive theory of education, and the fundamental part of cognitive development is to acknowledge the differences of an individual’s social and cultural backgrounds. In other words, Piaget seemed to be overlooking cultural effects. Since his research was done in a Western country, his theory of cognitive development may only represent Western society and culture. According to Piaget, scientific thinking and formal operations can only be reached at a certain stage. On the other hand, Edwards et. al argued that Piaget’s research was unreliable due to the lack of controls and small samples. He believed that there could a higher regard for the basic level of concrete operations in other cultures. Beger also argues against Piaget’s definite stages, he judges that Piaget had explicitly explained the children’s internal search for knowledge, but he tended to overlook the external factors, such as heredity, culture, and education. He suggested that Piaget’s stages of cognitive development should rather be seen as a gradual and continuous progress instead of separating into definite stages. Piaget’s theory has also been criticized for not offering a sufficient description of cognitive development in his last stage. He supposed that everyone will be able to develop abstract reasoning between age 11 to age 15. On the other hand, Paplia et.al believes that not everyone can acquire the skills of formal operations at this stage. And even though they may not attain this ability, it does not mean they are immature. We can only conclude that they have different phases of mature thought. Hence, a more persuasive belief of cognitive development should be perceived as an irregular process as children attain new skills and different behaviors individually at each stage.

### Cognitive Domains

Cognitive theory and its relevance to learning mathematics has come a long way since Piaget. Numerous studies have been done which demonstrate the relationships between different cognitive abilities and mathematical abilities. As early as 1978, researchers were studying the relationship between academic abilities and patterns of brain related behaviour. In 1978, Rourke and Finlayson studied 9-14 year old children with learning disabilities and found that children lacking abilities in arithmetic performed as would be expected if their right cerebral hemisphere was not functioning correctly. More recent studies have been able to identify repeating patterns of even more specific relationships for cognitive abilities and functional deficiencies in math.
In 2001, Hanich, Jordan, Kaplan and Dick studied the mathematical performance of grade 2 students.. Children were divided into four groups, consisting of normal achieving students, children with math deficiencies, children with reading deficiencies, and children with both math and reading deficiencies. Children in each of the four groups were given seven mathematics tests in the same order, to assess performance in: a.) exact calculation in arithmetic combinations, b.) story problems, c.) approximate arithmetic, d.) place value, e.) calculation principles, f.) forced retrieval of number facts, and g.) written computation. They found that children with math and reading deficiencies struggled with both word problems and with standard computation (such as number facts, number combination and procedural computations); whereas children deficient in just math struggled only with standard computational skills. This, and subsequent studies, have led researchers to conclude that there is more than one cognitive domain for math, with each domain using different processes of the brain.
Fuchs, Fuchs, Stuebing, Fletcher, Hamlett, and Lambert noted that a number of studies have consistently found that predictors for computational success include: a.) working memory, b.) visual-spatial working memory, c.) attention ratings, d.) phonological processing (detecting and discriminating differences in speech sounds), and e.) vocabulary knowledge (2008). During a long-term, large scale study of students who were randomly sampled, the authors undertook to determine whether or not problem solving and computation were distinct aspects of mathematics. The authors assessed students for computational and word-problem solving abilities, phonological skills, non-verbal problem solving, working memory, attentive behaviour, processing speed, and reading skills. They found that attentive behaviour and processing speed played dominant roles for computational difficulty.
Further, Fuchs et al also noted that working memory, short term memory, non-verbal problem solving (ability to complete patterns presented visually), concept formation, and language ability (including reading) were all predictors of problem solving ability. They also noted that deficiencies in language skills was a discerning factor for students who exhibited problem solving difficulties.

#### Processes of the brain for each cognitive math domain

| Computation Cognitive abilities | Problem Solving Cognitive abilities |
| Predictors for computational success: | Predictors for problem solving success: |
| • Working memory Auditory working memory Visual-spatial working memory • Attention ratings • Processing speed • Language ability Phonological processing (detecting and discriminating differences in speech sounds) Vocabulary knowledge | • Working memory Auditory working memory • Short term memory • Non-verbal problem solving (ability to complete patterns presented visually) • Concept formation • Language ability First language, cultural differences Phonemes, vocabulary |

### The Importance of Working Memory in Learning Mathematics

Working memory is the system responsible for temporarily holding new or previously-stored information which is being used for the completion of a current task. Its capacity is limited. There are two types of working memory: auditory memory and visual-spatial memory. Visual-spatial memory has been found to be important for solving computational problems. Auditory memory has been found to be important for all mathematical domains. The variation of an individual's capacity for working memory may be due to how fast information is processed, one's knowledge, or one's ability to ignore irrelevant knowledge. Executive processing activities, such as planning, organization and flexible thinking, may also affect working memory.
On the other hand, short term memory is responsible for temporarily storing information which must be used, but not necessarily manipulated. Again, the capacity for short term memory is limited, maybe only a few seconds. This is where we store information such as a telephone number we need to remember for only a few seconds while we dial it.
In their study, The Relationship Between Working Memory and Mathematical Problem Solving in Children at Risk and Not at Risk for Serious Math Difficulties (2004), Swanson and Beebe-Frankenberger concluded that working memory plays a critical role in integrating information during problem solving. They argue that working memory is highly important to integrating information during problem solving because "(a) it holds recently processed information to make connections to the latest input and (b) it maintains the gist of information for the construction of an overall representation of the problem."
A new study by H. Lee Swanson suggests that the capacity of working memory moderates the influence of cognitive strategies on problem solving accuracy. The author conducted an intervention study to ascertain what role working memory capacity played in strategy intervention outcomes and the role of strategy instruction on word problem solving accuracy.
Both verbal and visual-spatial working memory were measured for all children in the study group. Children, both with and without math disabilities, were were then divided into three treatment groups for a randomized control trial. Group 1 was given verbal strategies for problem solving; Group 2 was given visual-spatial strategies for problem solving; and Group 3 was given a combination of both verbal and visual-spatial strategies. Each of the groups was also provided with lesson plans that regularly increased irrelevant information within the word problems. The author's strategy of adding irrelevant information was meant to teach the children to attend to relevant information only. This strategy was prompted by a number of other studies which showed that learning to differentiate between relevant and irrelevant information is significantly correlated with problem solving accuracy for students at risk for math disabilities.
The results of the study support the view that strategy instruction facilitates solution accuracy. However, it must be noted that the effects of strategy instruction were moderated by individual differences in working memory capacity. Those children with low working memory capacity did not benefit as much as expected. It was the children with higher working memory capacity, both with and without math disabilities, who were most likely to benefit from the learning strategies. All children with math disabilities, whether possessing high or low working memory capacity, did benefit from strategies that used visual information, however children with low working memory capacity needed the combination of both verbal and visual strategies. Lastly, the results suggest, academic tasks that train processes related to working memory for controlled attention may, in fact, influence later working memory performance.
Implications of this study would suggest that students with math disabilities be evaluated for working memory capacity and then strategies for addressing their individual concerns be determined based on their working memory capacity.

## Factors that Affect Learning and Teaching Mathematics
### Individual Differences

Every learner has their own distinct skills, background knowledge, culture, and interests. These aspects can affect learning and teaching mathematics because instructional strategies should be modified accordingly.

#### Differences in Skills

All learners have their own strengths and weaknesses. They may be skilled in some aspects in mathematics but may be incompetent in another area. It is important for teachers to know what skills the students have because they can utilize these skills to help improve the students’ weaknesses. If teachers do not recognize the students’ strengths and weaknesses, they might give students challenges. Students will face difficulty in the given task because they do not have the required skills. As a consequence, it may even influence the students' self-efficacy and create learned helplessness when students cannot accomplish the task. Hence, if teachers know what students are proficient in, then students will not have problems in learning new knowledge of mathematics. Mathematical problems require a set of pre-skills such as simple arithmetic, algebra and logic reasoning. For instance, solving word problems require mental representation of the problem and simple arithmetic to transform the word problem into a mathematical equation. As a result, students who are not skilled at formulating a mathematical equation will not be able to solve the word problem. Teachers should adjust their instructional practices according to the different pre-skills that the students have because these pre-skills play a big part in solving mathematical problems. When students gain more conceptual and procedural skills in mathematics, they become more competent and efficient in learning mathematics. In modern high schools, there are different levels in the course of mathematics such as beginner, principle, and advance level. Students are placed accordingly to their set of mathematical skills level. Otherwise, they can choose which level they want to be in. In this case, it is important that teachers support and evaluate the students' performance to see whether or not if they are suitable in the chosen level. Students do not want to be in a math class that is too difficult or else it would be too overwhelming, neither should it be too easy or else it would be too boring. Hence, by knowing what skills the students have, students can achieve new mathematical knowledge.

#### Differences in Background Knowledge

Students’ knowledge of mathematics can be affected from their background knowledge. Indeed, all students have different background knowledge because they all have different experiences in the social world. These real-life experiences are crucial because they learn about the functionality of mathematics symbols from these observations. For example, students can learn simple arithmetic from grocery shopping which involves dealing with money. Students can learn how to estimate the total cost of goods and how much change they should received back. Therefore, when mathematical concepts are taught in a way that is related to their background knowledge, students will be able to interpret these concepts more easily. In addition, students are more motivated and engaged when their learnings of mathematics are related to their real-world situations. This is because they find the acquired learnings very meaningful and important as they are applicable in their daily living. For instance, many students might find learning mathematics from a textbook boring or difficult. However, if mathematics are taught to solve real-life problems such as calculating the interest gained in the bank, the total cost of living expenses, or the probability of winning in a poker game. As a result, students will have a better understanding of mathematical symbols and concepts when these learnings are related to their prior experiences. In addition, challenging mathematical problems not only require background knowledge of mathematics, but also some knowledge of other subject areas such as physics terms or chemistry terms. Mathematical word problems require a good understanding of the text meaning before it can be solved which means that students need to be able to utilize their language knowledge to comprehend the text. As a result, students' background knowledge can impact their learning in mathematics. For instance, many math courses in University require prerequisite courses because the advance level math courses require understanding of some basic mathematical knowledge. Without these background knowledge, students will have difficulty comprehending the new math materials.

#### Differences in Interests

Everyone has different interests. Some students might enjoy mathematics because they were born or taught at a young age with strong mathematical skills, while other students might hate mathematics because they always face failure with mathematics which discourages them to continue to learn. Having interests in mathematics can increase students’ motivation to learn mathematics. This concept is an intrinsic motivation because students want to study mathematics out of their own interests. As a result, they are more engaged in the tasks and would try their best to solve the challenge. Students' interests are related with their beliefs on their self-perceptions, their ability, and their academic achievement. Thus, it is important to develop interests in mathematics for students in order to increase their academic performance. Indeed, there are many ways to increase interest in mathematics such as family, classmates, and teachers. Family can show support and encouragement to students in mathematics at home which can increases students’ value on mathematics. Students usually have social comparisons and like to follow what other classmates are doing. Hence, classmate influences play a big role in students. When students see their classmates enjoying a mathematics problem or game such as sudoku or a puzzle, students will also be interested in solving. Most importantly, teachers can organize fun and interactive games in a classroom setting while showing enthusiasm in their teaching. This will enhance students’ interests in learning a subject they do not enjoy. As a result, it is important that teachers create an enjoyable setting for students to learn in order to promote interests in mathematics. It would be very difficult to teach students mathematics if the learners hate mathematics. They will not want to learn the materials and only study because they have to.

#### Cultural Differences

Students with different cultural background have different academic achievement levels and different goals. Also, their values on mathematics might be different depending on their culture. When a culture values a particular subject such as mathematics, these children tend to be trained at a young age at school and at home. Hence, these students will have a higher efficiency of mathematics performance. Students who study mathematics regularly are likely to have a high level of automaticity because they have sufficient practices of the mathematical problems. They will be able to select the appropriate strategy and solve the mathematical problem more efficiently. Vice versa, when a culture does not believe that mathematics is important, these children might not be taught vigorously and will performed at lower competence levels. In order to excel in a subject area, it is important to have practices both at school and at home. Students who only practice their mathematics skills at school by the teachers' support do not have enough training because they are not encouraged to study actively and intensively at home. In addition, cultures that hold positive beliefs on performance such as high standards, effort, and positive attitudes can lead to high academic proficiency levels. Different cultures have different languages. By all means, their way of wording a mathematical problem may also differ. Research shows that the structure of Chinese number languages (e.g., 15 is ten five) is easier to learn than Indo-European number languages which is English (e.g., 12 is twelve and -teens words are often inconsistent). It is often to faster to pronounce Chinese number languages than in English which affects students’ mathematics efficiency. Hence, Chinese has the ability to retain these numbers in short-term memory longer especially in complex mathematical problems with multi-digit numbers. Cultural differences should be taken into consideration when designing instructional practices since different students have different cultures that can affect how they approach mathematical problems.

### Self-Efficacy in Mathematics

Students' self-efficacy in math is their belief in their ability to solve math questions. Students with a higher level of self-efficacy believe that they are capable in solving math questions, which they are more likely to engage in math-related tasks and have higher academic performance in math. On the other hand, students with low self-efficacy believe that they are not capable in solving math questions, which they will feel more anxious in solving math questions and have lower academic performance in math. Therefore, students' self-efficacy in math has strong connections with their engagement and academic performance in math.

#### Self-Efficacy's Impact in Math

Self-efficacy can influence the way students think, understand, and feel about their learning in math. Students with high self-efficacy believe that they have the ability and skill to perform well in math. Having the thought that they are capable in solving math, students will be more motivated to learn and study math. By doing so, students will encounter self-fulfilling prophecy which fits their belief of their ability in math when their math improved after they studied. On the other hands, students with low self-efficacy in math will believe that they do not have the ability to perform well in math.With this belief, students might have the thought that they cannot achieve math even if they tried very hard. Therefore, they are less motivated in doing math questions. Also, students with low self-efficacy in math might give up easily after a few trials of questions by thinking that they do not have the ability to get the right answer. When they do so, it reinforces their belief of their disability in math. The student will encounter self-fulfilling prophecy which they act in a way that fulfill their belief in their low ability in math.

#### Assessing Students' Self-Efficacy

It is important to assess students' self-efficacy and know whether or not if they are confident in learning a particular topic in math because it may affect their performance. One of the ways to assess students' self-efficacy is to construct a list of first-person statement and have students to rate their self-efficacy for each statement. First, teachers have to identify the topic that they would like to assess their students' self-efficacy on. For instance, if the topic is on finding surface area, teachers then construct a list of first-person statements on that topic. Then teachers can have students to rate the statement using a scale range from 0-100 (0 which the statement is false and 100 which the statement is true). The following chart is an example of a student's rating his self-efficacy on the topic of surface area.
| Rate (0-100) | Statement |
| 80 | I know what information do I need in order to find the surface area for a parallelogram. |
| 100 | I can find the surface area of a rectangle when given the length and width. |
| 60 | I can write the equation for the surface area of a trapezoid. |
| 50 | I can explain to my classmate why the equation for the surface area of a triangle is bxh ÷2. |
| 90 | I can calculate the area for a square which have the length of 4cm. |
After the student rated the statement, the teacher can estimate how confident the student is on that topic by adding up the scores. For the above example would be 80+100+60+50+90. From the scores, the teacher will have an idea on student's self-efficacy on that topic. Furthermore, the teacher can compare student's self-efficacy for a particular topic to their general efficacy in math. Also, when assessing students' self-efficacy, teachers should keep in mind that students' self-efficacy may impact their learning motivation and learning behavior. Therefore, teachers should adjust their teaching instructions to increase students' self-efficacy and match their level respectively.

#### Development of Students' Self-Efficacy

Bandura has proposed four major influences on the development of self-efficacy. The first influence is students' mastery experiences. For instance, when students succeed in a math test, their level of confidence in that area of math will go up. This will have a positive effect on students future performance, which students will be more confident that they have to ability to solve it when facing similar questions. The second influence is students' various experience. By observing others, especially peers with similar ability, students self-efficacy in doing a particular task will increase. When the teacher introduced a new topic in math, which students are uncertain about the level of difficult for that topic, by observing their peers completing the questions, their level of confident in understanding and completing the questions in the new topic will go up. Moreover, even watching a documentary on mathematicians doing math improves students' math self-efficacy. The third influence is social persuasion. This could be a positive phrase from the people which the students interact with, such as their parents, peers or teachers. Positive feedback from the teacher, such as "you are getting better in solving algebra questions" will increase students confident in solving algebra questions. The fourth influence is students psychological state. This refers to students emotional reaction toward a situation. For example, a student might feel that her failure of a math test is due to her inability of math, which in reality is a result of her anxiety. In this case, student misjudged her ability and lowered her confident in math. Another case might be student seeing her successful performance in a math test as luck, instead of her ability in performing well. In this case, the student lost a chance of building her confident in math. Therefore, students perception toward both positive and negative situations have an effect on building their self-efficacy. The way to increase students self-efficacy in this route is to have them to recognize their true ability in math and increase their positive feelings of their ability.
Usher has conducted a research on measuring the four different sources of middle school students self-efficacy's development in math, by interviewing the students, parents, and teachers.The result of the research is consistent with Bandura's proposed idea on the development of self-efficacy, which mastery performance, vicarious experiences, social persuasion and physiological states all have a connection with students confidence in math. For mastery performance, it showed a strong relationship with students development of self-efficacy. A strategy that usher suggested which math teacher can use to increase students confident through mastery performance is to "deliver instruction in a way that maximize the opportunity for mastery experiences, however small." For instance, a teacher could teach the students the correction strategy on math topic like algorithm and algebra. An example question is 18 ÷ 6 =?. The teacher could teacher the students to self-check the answer by multiplying the quotient by the divisor (3 x 6= 18) and if the answer is the same as the dividend, then is correct. Students who have been taught and used the correction strategy had increased their mastery performance in math. Assign challenging assignments for students which are within their ability to complete it will also increase students' mastery experience.
In addition, some evidence in the Usher's research has shown that the four sources have a connection with each others too. For vicarious experiences, the finding has shown that both of the parents and the teachers' experience with math have a connection with students math confidence. One of the compelling findings in the research is which a student interpreted his parents' failure in math as evidence that he could be different. This shows that not only successful experiences, unsuccessful experiences with math could have a connection with have students math confident. The finding also shows students' physiological states would have an effect on how they interpret others' experience. For social persuasion, the finding has shown that the messages both parents and teachers have sent to the children could largely impact students' belief in their ability. For instance, a message that belief math is a fixed ability would result in student's lack of motivation. So, if parents tell their children that with math ability they either have it or not, their children might end up believing that they do not have the ability to perform well and lower their confidence in math. In this case, social persuasion could have an effect on students' physiological states.

#### Teachers Efficacy

Teachers' teaching efficacy refers to the belief that they can make a significant change in their students,such as students' academic performance, self-efficacy, motivation, attitude and interest in learning. In order for teachers to establish a high level of teaching efficacy, they need to have a positive attitude, rich pedagogical knowledge and content knowledge toward their teaching subject. Teachers' attitude towards math may have a strong influence on students' attitudes and academic performance. A study has examined teachers' attitudes toward math in four different groups through interviewing the teachers and having them to complete a teacher attitude scale. The four different groups are K-4 teachers, middle school teachers, other educators (Principals, other administrators) and special education teachers. The result indicated that among the four groups, middle school teachers have the strongest positive attitude toward math (60% strongly positive, 30% neutral, 10% strongly negative), whereas K-4 teachers have the strongest negative attitude toward math (43% strongly positive, 23% neutral, 34% strongly negative). The result shows that math is less emphasize and valued in elementary level then in middle school level. By having a negative attitude towards math, teachers are less likely belief that they can make a change in their students' learning, which is correlated to their teaching efficacy. Teachers' pedagogical knowledge and content knowledge in math are also factors that affect their teaching efficacy. A current research has studied teachers' math pedagogical knowledge and math content knowledge in relation to teachers' teaching efficacy and students' achievement in the topic of algebra i. The result have found that they are strong correlation between teacher's teaching efficacy with their pedagogical knowledge and content knowledge, which indicating that teachers with a rich pedagogical knowledge and content knowledge are more confidence with their teaching and more likely to believe that they can make a significant change in their students' learning.
Teachers' teaching efficacy can affect students' learning in many different ways. One of the more observable factors is students' academic achievement. A study had conducted K-12 school teachers' self-efficacy beliefs and found that their self-efficacy beliefs are positively associated with students' achievement. Besides students' achievement, teachers' teaching efficacy could also affect student motivation, interest and strategies use in learning. This is because teachers with higher teaching efficacy are more likely to use praise instead criticism, to be more accepting and more task oriented. Another research has found that teachers with higher efficacy will teach their students more learning strategies and have more focused academic learning time, which will increase students' performance.

### Self-Regulated Learning

People might think that students' low mathematics achievement is due to their low ability in math or the consequences of not studying. But that may not be the case in all situations. Sometimes, students' low mathematics achievement might be a result of not using the most appropriate strategies to study due to their lack of self-regulated learning skills. Self-Regulated Learning is students' ability to control all aspect of their learning, from advance planning to how they evaluate their own performance afterward. There are three core components for self-Regulated Learning. The first one is metacognitive awareness, which refer to how students' set their goal and their plan of reaching that goal.The second one is strategies use, which refer to a list of self-regulated strategies that students could apply to their studying. Skilled learners use more effective strategies when they are learning. The last one is motivation control, which is students' ability to set goals and their positive belief on their academic skills and performance. The ability of self-regulated learning has a big impact on students' mathematic achievement. Students will use better strategies and have a better understanding on how to study mathematics, when their self-regulated learning skills improved, which will increase this mathematic achievement.

#### Mathematics Self-Regulated Learning Program Study

A research in Southeast Asia had established a mathematics self-regulation learning program and the result had shown that when students are being taught with self-regulated learning skills, their mathematic achievement increases. The research involved with 60 lower mathematic achieving students in elementary level. 30 students are being placed in the experimental group, which they have to attend a mathematics self-regulated learning program.
This program contains 30 sessions, which serve a purpose of increasing students' self-regulated learning skill by increasing their motivational control and teaching them the self-regulation strategies. (Sessions 1-5) The program started with developing students' self-regulation belief system. They introduced students' to the value of personal responsibility, self-efficacy, learning goal and attribution to effort by lecturing students with storytelling and having them to share their ideas in a group. (Sessions 6-11) Then, they introduced students the 14 self-regulated learning strategies that were proposed by Zimmerman. Each strategy was explained by emphasizing its usage and important in learning mathematics. Afterward, students are given the opportunity to practice each strategy on their own. (Sessions 12-30) Lastly, students are guided to apply self-regulated learning strategies in their regular mathematic lessons. Also, they have to evaluate their own progress by completing the goal setting, self-evaluation and self-consequating forms. After the students completed the 30 sessions in mathematics self-regulated learning program, they will take a mathematic achievement test and a self-regulated learning test. The results have shown that students who attended the program scored higher in both tests compared to those who did not attend the program.
Applying Self-Regulated Learning strategies in Mathematic
| Strategies | Application in Mathematic |
| Self-evaluation | Students do so by making sure that they have the right answers for the questions with the appropriate steps. |
| Organizing and transforming | Students' ability to organize math questions. Some of the ways are using graphs, equations, and diagrams. |
| Goal-setting and planning | Students setting goals and their plan of achieving those goals. |
| Keeping records and monitoring | Taking notes in class. Organizing the equations. |
| Environment structuring | Studying in an environment which benefits their study. |
| Self-consequences | Students' own punishment or reward on its own success or failure in mathematic. |
| Rehearsing and memorizing | Student learned by doing a lot of different forms of math questions. |
| Seeking information | Student seek information from the nonsocial source. |
| Seeking social assistance | Students seek help from their peers, teacher or other adults. |
| Reviewing records | Students re-read textbook, notes or their homework questions. |
After attending 30 sessions of a mathematics self-regulated learning program, students showed significant improvement in their mathematical achievement and self-regulated learning test. This shows that it is possible to teach lower-achieving math students with self-regulated learning skills. When they were equipped with these skills and taught to focus on the processes and strategies, their math solving skills improved. With improvement, students will gradually recognize their ability to do better in math. Praising and rewarding themselves for their improvement will provide students will have even greater improvement in math. As a result, their self-efficacy and their interest in math will rise. This creates a positive cycle: when students believe that they have the ability to achieve math, they will work even harder in math with the appropriate self-regulated learning skills.
In the traditional classroom, math is viewed as an answer-centred subject rather than a process-centred subject. By emphasizing speed and accuracy, students will develop skills in copying and memorizing mathematical facts instead of understanding math. Also, the learning only flows one way, from teacher to students. In this kind of classroom setting it would be hard for students to apply self-regulated learning strategies because when the students are not allowed to have choice and control over their study, they are not likely to learn strategies for self-regulation, nor willingly self-initiate and control the use of various strategies.Therefore, in order for students to apply self-regulation skills, the classroom environment is very important. One of the best ways to develop self-regulated learning skills is to give a certain degree of control to students for their own learning. Math teachers should promote the sharing of knowledge and decision making. When students have a voice in setting goals, planning activities and evaluating their own performance, they have a chance to practice their self-regulated learning skills, which will have a positive impact on their math achievement.
Upper-grade students can apply self-regulated learning skills better than lower grade students.This is because older students are more capable of understanding concepts and ideas that are presented in self-regulated learning theory. Also, some of the self-regulated learning strategies require prior knowledge and skills, such as writing a plan or organizing learning materials. Therefore, it is easier for upper-grade students to learn some of the self-regulated strategies. As a result, upper-grade students show more improvement in mathematical achievement than lower grade students when they are taught self-regulated learning skills.

## Implications for Teaching
### Mathematics-Learning Disabilities

Recent studies into cognition, working memory and mathematics learning disabilities all point to a need to distinguish between computation and problem solving learning disabilities in math. To this point, mathematics assessments have been generic and have not given appropriate consideration to the different features of each domain. Professionals must consider these two skills separately when diagnosing students. Teachers should also take into account the different domains when instructing children with mathematical learning disabilities. Some suggestions and tools that may help students with their mathematical learning are:

### External Representation

External representation is a helpful tool in mathematics because mathematical problems can be complicated to solve mentally at times. By using external representation, it provides a clear understanding on the concept of mathematics by which students can develop knowledge acquisition. Some external representations are worked-out examples, animations, and diagrams.

#### Worked-out Examples

Worked-out examples are a useful instructional method that teachers use to facilitate students in learning mathematics. Research shows that using worked-out examples can increase the students who have low mathematics performance level. One reason is when students are given a problem to solve, their optimal goal is to solve the problem rather than to learn mathematics. In contrast, when students are given worked-out examples, they actually learn and try to interpret the materials on their own. Thus, worked-out examples focus more on intentional learning for students. Students usually do not understand the mathematical theory or proof because they are complicated to comprehend. However, worked-out examples are easier for students to acquire learning and understand the concept of mathematics. Without giving explicit instruction, teachers simply show the steps of how to solve the mathematical problem as an example for the students to refer to. There are detailed explanations on the steps required to solve the mathematical problem. Then, students have the autonomy to self-explain similar types of mathematical problem on their own. Thus, they can use the worked-out examples as references to solve many mathematical problems. They can explicitly reflect their thinking on how to solve the problem by referring to the worked-out examples that the teachers provide. Hence, this can also enhance the students in self-regulated learning as they are practicing their critical thinking in solving the problem. This metacognitive strategy can help students improve their problem-solving skills especially on mathematical word problems. Metacognitive strategies include self-questioning, self-evaluating, summarizing, and illustrating the problem. These strategies are believed to acquire knowledge for students while constructing a deeper understanding from the worked-out examples. Research shows that students who can self-explain the problem and solve them have higher mathematics achievement. When students explain the steps of how to solve the mathematical problem on their own, they are exercising their reflective thinking which can construct a greater understanding beyond what the information was given. Indeed, students can develop new and sophisticated knowledge of mathematics because they consolidate the newly learned materials with their prior knowledge. In addition, worked-out examples can also be used in group settings where students can discuss with their classmates in solving mathematical problems. Research found two ways that students can use worked-out examples in classrooms. One way is students who understand the worked-out examples can explain to those who do not understand. The other way is students interpret the worked-out examples altogether by using their logic and reasoning skills. Both ways engage students in learning in a social interactive setting by discussing the details of the worked-out examples. Learning in a social setting can strengthen the understanding of the materials because students are elaborating the examples more in depth. They can also ask any questions that they have with the worked-out examples in order to get a clear comprehension. Therefore, it is important that students should discuss further on the worked-out examples in small groups to reflect on the problem procedure and to generate knowledge acquisition beyond their existing knowledge.

#### Animations

To increase the students’ interest in learning mathematics, animation is a great instructional tool to use to teach students. Since mathematics can be quite boring and uninteresting at times, animations can attract students’ interest in learning mathematics. Most importantly, animations claim to facilitate students’ problem-solving skills in mathematics. Before students solve any mathematical problem, it is important that students identify the problem and know what to solve. Henceforth, when students find the problem hard to translate, animation becomes most effective because it consists of visual representation that makes it easier for students to interpret the question. In contrast, when students just take notes on the problem, they do not have a clear understanding of what the problem means because they are just simply copying the text. By having a pictorial representation along with the verbal explanation of the problem, students can visualize what is happening in the problem fully. For instance, the concept of addition and subtraction is hard to explain through text to an elementary school student. However, when using animations to display a before and after frame of what happened in the problem can construct a clear comprehension. In the case of addition or subtraction mathematical problems, animations can demonstrate an increase or decrease of objects to represent the solution. In addition, animations can illustrate abstract math theories by showing visible objects, concrete results, and specific instances. Thus, animations can be used to convey the abstract concepts of mathematics with reference to distinct examples. Animations can facilitate the acquisition of abstract principles and the comprehension of worked-out examples due to the visual representation of the problems. Although worked-out examples are known as a effective instructional practice, animations can be used to effectively improve these examples. Worked-out examples may not always have a pictorial representation but only have written texts. Therefore, when each of the steps of the solution procedure of the worked-out examples have a visual representation, students can imagine what is going on in the problem. Students can also interpret the worked-out examples better with the explanations and the pictures given. As a result, it is recommended that teachers should use animations as an instructional tool in their practices to fully consolidate the students’ learning in mathematics.

#### Diagrams

To produce an informational diagram can be a very difficult procedure, because students do not only required to interpret the verbal information into the visual information, but also needed to identify and integrate the related information together before associating to their prior knowledge. Larkin and Simon believed that diagrammatic representation is easier and more efficient than sentential representation because of three aspects in regards to searching, matching, and inference. First, it clearly retains all the information about the topographical and geometric relations between the elements of the word problems. Therefore, students can search for particular information easily. Second, since all the related elements are grouped together, it shows the connections between the concrete representations and the pictograms. Hence, it can simplify the process of identifying the related information. Besides that, the memory load is lower if the problem is produced by drawing a diagram, as the students can clearly see the essential inference between the related information. Many studies have suggested that the use of diagrams can improve the efficiency in problem solving.
Banerjee has conducted a research on the effects of using diagramming as a representational technique on high school students’ achievements in solving math word problems. The result has proved that the diagramming method (such as focusing on the creation and labels of diagrams to represent the mathematics) can significantly improve their achievements in solving math word problems.
In a study with the use of diagrams in solving the math word problems, Uesaka, Manalo, and Ichikawa made a comparison of students in Japan and New Zealand. The diagram that was drawn by a Japanese student was using a one-object problem, and the one that was produced by a New Zealand student was using a two dimensional object to solve the math word problems. Results indicated that the percentages of correct answers by the New Zealand students were significantly higher than the Japanese students. The reason is that producing a diagrammatic representation can index the sentences by location, so students can observe the details at a specific location explicitly, which ease them on understanding the problem. In order to promote students on using diagrams to solve math problems, teachers should first teach them on 1) what diagrams are, 2) the importance of using diagrams to solve problems, 3) when to apply the diagrams in solving problems, 4) which type of diagram should be using for the math problems, 5) how to generate a diagram, and 6) how to use a diagram effectively. The reason that students should know the fundamental concepts of diagrams is that diagrams may not apply on all the math problems. Uesaka and Manalo pointed out that students tend to use diagrams when solving math word problems in regards of length and distance instead of spatial problems, because it usually involves a concrete relationships and known quantities. After teaching them the important concepts of diagrams, teachers can then instruct them the 3 step procedure – Ask, Do, and Check. Van Garderen and Scheuermann suggested that students should first concentrate on what needs to be solved; then they should produce a diagram. Finally, they can solve the problem by using the diagrams. For example, in order to focus on what needs to be solved, students can use the key word method to search for the information, and place the information that is given from the problem. In conclusion, diagrams can be an effective strategy when solving math problems; it does not only help students to think critically, but also aid them in solving problems by using a different approach.

### Algorithms

An algorithm is a series of steps to help students solving math problems. If they follow these procedures, they will always be able to compute a correct answer every time. Algorithm involves with repeating sequences, it applies to addition, subtraction, multiplication, and division. By using algorithms, students can learn how to explain what is happening in each step, and able to track their mistakes if they yield an incorrect answer in the end. It requires them be attention to details when they are problem solving, that is, when they are working through a multiple step solutions, they are required to recall the algorithms from their long term memory and have a set of steps in their mind already. Also, teachers should instruct students that algorithms must be solved in a sequential order, none of the steps can be jumped over. For example, when students are learning basic arithmetic operations, they have to learn that there is a specific order to solve a problem like 5+8×6. Students need to understand that they have to do the multiplication first, then the addition part. If they can follow the correct order, they can always yield to a correct answer. However, Paul Cobb has conducted a study in regards of Grade 1 and 2 students solving double-digit addition problems. He noticed that all of the students were managed to give a correct answer for 16+9 by using various methods. Conversely, if they were asked to use the traditional school algorithm with carrying to solve the same problem but with a vertical context, many of them tend to yield an incorrect answer. He concluded that the reason of causing the students to have a higher possibility of making errors with a traditional school algorithm is that they were only forcing themselves to follow the rules instead of fully understanding how the algorithms work. J.S. Brown and Burton found out that there is a significant amount of students are using one or more wrong versions of algorithm consistently to solve their math problems. Even though lots of incorrect algorithms yield to a correct answer, yet it may not apply to all cases. For example, some children had a preconception that the subtraction algorithm means taking the smaller number from the larger in every single column, regardless of which number was on the top. The diagram on the left can explain why incorrect algorithms may not produce a correct solution all the time.
Brown and Burton pointed out that even though the children who have the wrong perception of the subtraction algorithm may seem to understand the arithmetic operations of subtraction, as this can guide them to yield the correct solution on part a) and part c). However, they will yield an incorrect answer on part b) and part d), as the numbers on top in the second columns are smaller than the numbers in the bottom. Nagel and Swingen believed that the traditional algorithms with carrying or borrowing can only increase their efficiency and accuracy, yet neglect the sense-making for the students.
Therefore, in order to deal with the serial aspects of algorithms effectively, educators should teach students to use their spatial abilities when applying multiple steps to solve a problem. For example, they need to learn how to keep numbers aligned and spaced correctly to solve the problems successfully; especially when they are computing column subtraction, multiple digit multiplication, etc. Teachers should encourage students to develop and use their own algorithms to solve problems. They can encourage their students to incorporate mnemonics with algorithms; this approach can help them to remember things such as the procedures in solving problems. For example, PEDMAS can tell them the order when carrying out operations. Instead of simply solving an arithmetic operation from left to right, they now understand that they have to solve the brackets first. Moreover, teachers should ask the students to look over the entire problems first before trying to solve for an answer, then they should teach them how to break the problem into small parts and to determine which parts will require using the algorithms. They should also know which algorithms they should apply on for each parts; and finally, they should reflect on their answers for every steps. By showing steps, students can always track their mistakes and come to a correct solution ultimately.

### Word Problem Strategies

Word problems present a special case for all children, but especially those with problem solving learning disabilities. The most significant difference between computational problems and word problems is the addition of linguistic information. In other words, children must first read written words and filter out the information in order to translate the written problem into a computational number sentence. Children must then identify the missing information, as well as the relevant information, before completing the actual math portion of the problem.
Word problems are challenging for many students to comprehend but the problem is compounded when the learner’s first language is not English. According to Jan, S. and Rodrigues, S. (2012), children with English as a second language cannot comprehend problem statements due to language barriers. They tend to rely on key words or misinterpret the problem statement and so their resulting solution may be incorrect. Relying on key words can distract students from trying to understand the problem. “Key words can cause confusion in differentiating between everyday language and mathematical language.”
Findings from this study suggest that class or small group discussions will provide students with an opportunity to clarify the nature of a problem so that they can understand what is being given and what is being asked. Providing students with opportunities to read, understand, share each other’s ideas, and to consider the problem and solution from a number of different tactics will provide the students with a greater understanding of the problem.
In taking a cognitive approach to teaching word problems, it is important for the teacher to provide ample opportunity for students to think about and discuss the meaning of the word problems, and then consider multiple solutions with their classmates. This approach is valuable for both those students who have language barriers and those students with math learning disabilities.
The Council for Learning Disabilities recommends some of the following strategies for instructing students in problem solving:
FAST DRAW (Mercer & Miller, 1992) Find what you’re solving for. Ask yourself, “What are the parts of the problem?” Set up the numbers. Tie down the sign.
Discover the sign. Read the problem. Answer, or draw and check. Write the answer.
Questions and Actions (Rivera, 1994) Step a. Read the problem. Questions Are there words I don’t know? Do I know what each word means? Do I need to reread the problem? Are there number words? Actions Underline words. Find out definitions. Reread. Underline. b. Restate the problem. What information is important? What information isn’t needed? What is the question asking? Underline. Cross out. Put in own words. c. Develop a plan. What are the facts? How can they be organized? How many steps are there? What operations will I use? Make a list. Develop chart. Use manipulatives. Use smaller numbers. Select an operation. d. Compute the problem. Did I get the correct answer? Estimate. Check with partner. Verify with calculator. e. Examine the results. Have I answered the question? Does my answer seem reasonable? Can I restate question/answer? Reread question. Check question/answer. Write a number sentence.
3. TINS Strategy (Owen, 2003) Different steps used to analyze and solve word problems are represented with this acronym. Thought: Think about what you need to do to solve this problem and circle the key words. Information: Circle and write the information needed to solve this problem; draw a picture; cross out unneeded information. Number Sentence: Write a number sentence to represent the problem. Solution Sentence: Write a solution sentence that explains your answer. Example: Kyle bought 6 baseball cards. The next day, he added 11 more cards to his collection. How many cards does he have in all? Thought: + Information: 6 baseball cards, 11 baseball cards Number Sentence: 6 + 11 = Solution Sentence: Kyle has 17 baseball cards in his collection.
4. Problem Solving (Birsh, Lyon, Denckla, Adams, Moats, & Steeves, 1997) Read the problem first. Highlight the question. Circle the important information. Develop a plan. Use manipulatives to represent the numbers. Implement the plan. Check your work.

### Cognitive Tutor for teaching algebra

In 1985, Anderson, Boyle, and Reigser added the discipline of cognitive psychology to the Intelligent Tutoring Systems. Since then, the intelligent tutoring system adopted this approach to construct cognitive models for students to gain knowledge was named Cognitive Tutors. The most widely used Cognitive Tutor is Cognitive Tutor® Algebra I. Carnegie Learning, Inc., the trademark owner, is developing full-scale Cognitive Tutor®, including Algebra I, II, Bridge to Algebra, Geometry, and Integrated Math I, II, III. Cognitive Tutor® now includes Spanish Modules, as well.

#### How to teach

Two built-in algorithms, model tracing and knowledge tracing can help monitor students' learning during using the software. Model tracing can provide just-in-time feedback, on-demand hints, and give content-specific advice based on every step of the students’ performance trace. Knowledge tracing can individualize learning tasks for every user based on the prior knowledge.
You can go to the Chapter of Problem Solving, Critical Thinking, and Argumentation (2.5.2 The theoretical background of Cognitive Tutor) to get more detailed information of how Cognitive Tutor can facilitate algebra learning via just-in-time feedback, on-demand hints, content-specific advice, and personalized tasks.

#### Mixed effects of Cognitive Tutor® Algebra I

Regarding the effectiveness of Cognitive Tutors, previous research evidence supports more effectiveness of Cognitive Tutors than classroom instruction. However, recent independent large-scale study, What Works Clearinghouse, established by the U.S Department of Education's Institute of Education Sciences, reviewed 6 out of 22 studies on Cognitive Tutor® Algebra I which includes 12,840 students in grade 8-13 in 118 locations. The researchers found that Cognitive Tutor® Algebra I has mixed effects on algebra and no statistically significant or substantively important effect on general mathematics achievement for secondary students.
Morgan and Ritter, conducted a with-in teacher experiment in grade nine algebra classes in five different schools in Moore, Oklahoma. In this study, each teacher was assigned at least one Cognitive Tutor® Algebra I integrated classroom and one traditional classroom. The findings suggested that students who learned with Cognitive Tutor® Algebra I performed better than their peers who did not use the software, as well as tending to have positive attitudes towards mathematics, such as greater confidence in math.
Cabalo, Jaciw, and Vu conducted a randomized experiment to examine the effectiveness of Cognitive Tutor® Algebra I in five secondary schools settings in Maui County, Hawaii. After six months implementation of Cognitive Tutor® Algebra I, the students were required to take the NWEA Algebra End-of-Course Achievement Level Test at the end of 2005-06 school year. The findings suggested students overall reported positive attitudes towards Cognitive Tutor® software, and most students, whether using the software or not, showed improvements on math tests. However, students who had low scores before using Cognitive Tutor® improved significantly compared to those students with high initial scores.
Campuzano, Dynarski, Agodini, and Rall conducted a 2-year congressionally-mandated study on the effectiveness of technology-based instruction, including employing Cognitive Tutor® Algebra I in the second year in nine high-poverty schools in four districts. The researchers adopted the methods of randomized controlled trial and randomly assigned the teachers to either use the software or keep using the existing school curriculum. All the students were taken ETS End-of-Course tests in fall and spring, and the students who used the software had significantly higher scores in the second year compared to which in the first year. However, the difference in exam scores between the intervention group and the comparison group is little (p<0.3).
Pane, Griffin, MaCaffrey, and Karam adopted randomized controlled trial to examine the effectiveness of the technology integrated algebra curriculum in America. The research lasted for two consecutive school years, and the Cognitive Tutor® Algebra I software was implemented both in the teacher-directed classroom instruction (3 days a week) and the computer-guided instruction (2 days a week). The results in high schools showed a little difference of learning achievements between students in the intervention group and the comparison group in the first school year (p<0.46). However, the evidence firmly supported the benefits of integrating Cognitive Tutor® Algebra I in the second year (p<0.04), the lower achievement students in the intervention group had larger improvements compared to high-performance students in the same group.

## Glossary

Algorithm is a procedure with a series of steps in mathematics that when used appropriately to solve a mathematical problem, it will yield a correct solution.
Application occurs when students are able to make associations between mathematical concepts and daily life situations.
Clarification occurs when students identify and analyze aspects of a problem, it allows them to interpret the information that they need in order to solve the problem.
Classification is the ability of grouping objects based on similar characteristics.
Conceptual knowledge is the mental structures that promote students' reasoning and understanding of mathematics.
Declarative knowledge is when mathematical concepts, that are factual knowledge, are being retrieved from the long-term memory; hence, using these concepts to solve other complex mathematical problems.
Evaluation occurs when students can use a particular rubric to determine the correctness of a problem solution.
Inference occurs when students are able to use general concepts to specific situations and distinguish the similarities and differences among objects.
Intrinsic motivation is when students want to perform mainly for their own personal interests.
Metacognitive is the knowledge used to control one's thinking and learning.
Procedural knowledge is the knowledge about how to solve mathematical problems using the sequence of strategy steps.
Seriation is the ability of ordering objects from small to large based on the sizes, such as length, weight, or volume.
Self-regulated learning is the ability to control one's learning, from planning to how one evaluate performance afterward.
Short term memory is responsible for temporarily storing information which must be used, but not necessarily manipulated.
Working memory is the system responsible for temporarily holding new or previously-stored information which is being used for the completion of a current task.

## Suggested Reading

* A case study of novice teachers' mathematics problem solving beliefs and perceptions. Baker, C. K. (2015). A case study of novice teachers' mathematics problem solving beliefs and perceptions. Dissertation Abstracts International Section A, 75
* Piaget and Vygotsky: Many resemblances, and a crucial difference. Lourenço, O. (2012). Piaget and Vygotsky: Many resemblances, and a crucial difference. New Ideas In Psychology, 30(3), 281-295. doi:10.1016/j.newideapsych.2011.12.006

## References

Fuchs, L. S., Fuchs, D., Stuebing, K., Fletcher, J.M., Hamlett, C. L. , & Lambert, W. (2008). Problem solving and computational skill: Are they shared or distinct aspects of mathematical cognition? Journal of Educational Psychology 100 (1), 30
Hanich, L. B., Jordan, N. C., Kaplan, D., & Dick, J. (2001). Performance across different areas of mathematical cognition in children with learning disabilities. Journal of Educational Psychology, 93, 615–626.
Rourke, B. P., & Finlayson, M. A. J. (1978). Neuropsychological significance of variations in patterns of academic skills: Verbal and visual-spatial abilities. Journal of Abnormal Child Psychology, 6, 121–133.
Swanson, H. L., & Beebe-Frankenberger, M. (2004). The relationship between working memory and mathematical problem-solving in children at risk and not at risk for serious math difficulties. Journal of Educational Psychology, 96, 471–491.
Swanson, H. L. (2003). Age-related differences in learning disabled and skilled readers’ working memory. Journal of Experimental Child Psychology, 85, 1–31.
* ↑ Wong, B., Graham, L., Hoskyn, M., & Berman, J. (Eds.). (2008). The ABCs of learning disabilities (2nd ed.). Boston: Elsevier Academic Press.
* ↑ Bruning, R., Schraw, G., & Norby, M. (2011). Cognitive Psychology and Instruction (5th ed.). Boston: Pearson Education.
* ↑ Bruning, R., Schraw, G., & Norby, M. (2011). Cognitive Psychology and Instruction (5th ed.). Boston: Pearson Education.
* ↑ Wong, B., Graham, L., Hoskyn, M., & Berman, J. (Eds.). (2008). The ABCs of learning disabilities (2nd ed.). Boston: Elsevier Academic Press.
* ↑ Piaget, J. (1977). Epistemology and psychology of functions. Dordrecht, Netherlands: D. Reidel Publishing Company.
* ↑ Fuson, K. C. (1988). Children’s counting and concepts of numbers. New York: Springer.
* ↑ Piaget, J. (1963). The origins of intelligence in children. New York, NY: W.W. Norton & Company, Inc.
* ↑ Thompson, C. S. (1990). Place value and larger numbers. In J. N. Payne (Ed.), Mathematics for young children (pp. 89–108). Reston, VA: National Council of Teachers of Mathematics.
* ↑ Piaget, J. (1977). Epistemology and psychology of functions. Dordrecht, Netherlands: D. Reidel Publishing Company.
* ↑ Piaget, J. (1968). Genetic epistemology. New York, NY: Columbia University Press Retrieved June 13, 2003 from the World Wide Web: http://www.marxists.org/reference/subject/philosophy/works/fr/piaget.htm
* ↑ Hiebert, J., and T. P. Carpenter. "Piagetian Tasks as Readiness Measures in Mathematics Instruction: A CriticalReview." EDUCATIONAL STUDIES IN MATHEMATICS 13: 329-345, 1982
* ↑ Berger, K.S. (1988). The developing person through the life span (2nd ed.). New York: Worth Publishers Ltd.
* ↑ Berger, K.S. (1988). The developing person through the life span (2nd ed.). New York: Worth Publishers Ltd.
* ↑ Cross, C. (2009). The Early Childhood Workforce and Its Professional Development. In Mathematics learning in early childhood paths toward excellence and equity. Washington, DC: National Academies Press.
* ↑ Berger, K.S. (1988). The developing person through the life span (2nd ed.). New York: Worth Publishers Ltd.
* ↑ Papalia, D. E., Olds, S. W., and Feldman, R. D. (1998). Human development(7th ed.). Boston: McGraw-Hill.
* ↑ Edwards, L., Hopgood, J., Rosenberg, K., & Rush, K. (2000). Mental Development and Education. Retrieved April 25, 2009, from Flinders University
* ↑ Berger, K.S. (1988). The developing person through the life span (2nd ed.). New York: Worth Publishers Ltd.
* ↑ Papalia, D.E., Olds, S.W., & Feldman, R.D. (1998). Human development (7th ed.). Boston: McGraw-Hill.
* ↑ Berger, K.S. (1988). The developing person through the life span (2nd ed.). New York: Worth Publishers Ltd.
* ↑ Rourke, B. P., & Finlayson, M. A. J. (1978). Neuropsychological significance of variations in patterns of academic skills: Verbal and visual-spatial abilities. Journal of Abnormal Child Psychology, 6, 121–133.
* ↑ Hanich, L. B., Jordan, N. C., Kaplan, D., & Dick, J. (2001) Performance across different areas of mathematical cognition in children with learning disabilities. Journal of Educational Psychology, 93, 615–626.
* ↑ Fuchs, L. S., Fuchs, D., Stuebing, K., Fletcher, J.M., Hamlett, C. L. , & Lambert, W. (2008). Problem solving and computational skill: Are they shared or distinct aspects of mathematical cognition? Journal of Educational Psychology 100 (1), 30
* ↑ Swanson, H. L., & Beebe-Frankenberger, M. (2004). The relationship between working memory and mathematical problem-solving in children at risk and not at risk for serious math difficulties. Journal of Educational Psychology, 96, 471–491
* ↑ Swanson, H. L. (2003). Age-related differences in learning disabled and skilled readers’ working memory. Journal of Experimental Child Psychology, 85, 1–31.
* ↑ Swanson, H. L., & Beebe-Frankenberger, M. (2004). The relationship between working memory and mathematical problem-solving in children at risk and not at risk for serious math difficulties. Journal of Educational Psychology, 96, 471–491.
* ↑ Swanson, H. L. (August 4,2015). Cognitive strategy interventions improve word problem solving and working memory in children with math disabilities. Front Psychol., 2015; 6: 1099. Retrieved from doi: 10.3389/fpsyg.2015.01099
* ↑ Wong, B., Graham, L., Hoskyn, M., & Berman, J. (Eds.). (2008). The ABCs of learning disabilities (2nd ed.). Boston: Elsevier Academic Press.
* ↑ Bruning, R., Schraw, G., & Norby, M. (2011). Cognitive Psychology and Instruction (5th ed.). Boston: Pearson Education.
* ↑ Wong, B., Graham, L., Hoskyn, M., & Berman, J. (Eds.). (2008). The ABCs of learning disabilities (2nd ed.). Boston: Elsevier Academic Press.
* ↑ Wong, B., Graham, L., Hoskyn, M., & Berman, J. (Eds.). (2008). The ABCs of learning disabilities (2nd ed.). Boston: Elsevier Academic Press.
* ↑ Bruning, R., Schraw, G., & Norby, M. (2011). Cognitive Psychology and Instruction (5th ed.). Boston: Pearson Education.
* ↑ Bruning, R., Schraw, G., & Norby, M. (2011). Cognitive Psychology and Instruction (5th ed.). Boston: Pearson Education.
* ↑ Upadyaya, K., & Jacquelynne, S. E. (2014). How do Teachers’ Beliefs predict Children’s Interests in Math from Kindergarten to Sixth Grade? Merrill-Palmer Quarterly, 60(4). Retrieved from http://web.b.ebscohost.com.proxy.lib.sfu.ca/ehost/pdfviewer/pdfviewer?sid=aaa92fa2-22f8-4cd7-9d94-49896f602e89%40sessionmgr120&vid=0&hid=109
* ↑ Frenzel, A.C., Goets T., Pekrun R., & Watt, H.M.G. (2010). Development of Mathematics Interest in Adolescence: Influences of Gender, Family and School Context. Journal of Research on Adolescence, 20(2), 507-537. doi:10.1111/j.1532-7795.2010.00645.x
* ↑ Frenzel, A.C., Goets T., Pekrun R., & Watt, H.M.G. (2010). Development of Mathematics Interest in Adolescence: Influences of Gender, Family and School Context. Journal of Research on Adolescence, 20(2), 507-537. doi:10.1111/j.1532-7795.2010.00645.x
* ↑ Tsao, Y-L. (2004). A Comparison of American and Taiwanese Students: Their Math Perception. Journal of Instructional Psychology, 31(3). Retrieved from http://web.b.ebscohost.com.proxy.lib.sfu.ca/ehost/pdfviewer/pdfviewer?sid=97c91599-4222-4cc8-8717-703e6b774b06%40sessionmgr110&vid=19&hid=109
* ↑ Imbo, I., & LeFevre, Jo-Anne. (2009). Cultural differences in Complex Addition: Efficient Chinese versus Adaptive Belgians and Canadians. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(6),1465-1476. doi:10.1037/a0017022
* ↑ Imbo, I., & LeFevre, Jo-Anne. (2009). Cultural differences in Complex Addition: Efficient Chinese versus Adaptive Belgians and Canadians. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(6),1465-1476. doi:10.1037/a0017022
* ↑ Imbo, I., & LeFevre, Jo-Anne. (2009). Cultural differences in Complex Addition: Efficient Chinese versus Adaptive Belgians and Canadians. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(6),1465-1476. doi:10.1037/a0017022
* ↑ Imbo, I., & LeFevre, Jo-Anne. (2009). Cultural differences in Complex Addition: Efficient Chinese versus Adaptive Belgians and Canadians. Journal of Experimental Psychology: Learning, Memory, and Cognition, 35(6),1465-1476. doi:10.1037/a0017022
* ↑ Schunk, D. H., & Zimmerman, B.J. (2006). Competence and control beliefs: Distinguishing the means and ends. In P.A. Alexander & P.H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp.349-367). Yahweh, NJ: Erlbaum.
* ↑ Schunk, D. H., & Zimmerman, B.J. (2006). Competence and control beliefs: Distinguishing the means and ends. In P.A. Alexander & P.H. Winne (Eds.), Handbook of educational psychology (2nd ed., pp.349-367). Yahweh, NJ: Erlbaum.
* ↑ Bandura, A. (1997). Self-efficacy: The exercise of control.New York, NY:Freeman.
* ↑ Bandura, A. (1997). Self-efficacy: The exercise of control.New York, NY:Freeman.
* ↑ Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall, Inc.
* ↑ Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall, Inc.
* ↑ Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall, Inc.
* ↑ Hekimoglu, S., & Kittrell, E. (2010). Challenging students' beliefs about mathematics: The use of documentary to alter perceptions of efficacy. PRIMUS: Problems, Resources, And Issues In Mathematics Undergraduate Studies, 20(4), 299-331. doi:10.1080/10511970802293956
* ↑ Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall, Inc.
* ↑ Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, US: Prentice-Hall, Inc.
* ↑ Usher, E. L. (2009). Sources of middle school students’ self-efficacy in mathematics: A qualitative investigation. American Educational Research Journal, 46(1), 275-314. doi:10.3102/0002831208324517
* ↑ Usher, E. L. (2009). Sources of middle school students’ self-efficacy in mathematics: A qualitative investigation. American Educational Research Journal, 46(1), 275-314. doi:10.3102/0002831208324517
* ↑ Ramdass, D., & Zimmerman, B. J. (2008). Effects of self-correction strategy training on middle school students' self-efficacy, self-evaluation, and mathematics division learning. Journal Of Advanced Academics, 20(1), 18-41.
* ↑ Usher, E. L. (2009). Sources of middle school students’ self-efficacy in mathematics: A qualitative investigation. American Educational Research Journal, 46(1), 275-314. doi:10.3102/0002831208324517
* ↑ Usher, E. L. (2009). Sources of middle school students’ self-efficacy in mathematics: A qualitative investigation. American Educational Research Journal, 46(1), 275-314. doi:10.3102/0002831208324517
* ↑ 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
* ↑ Kolstad, R. K., Hughes, S., & Briggs, L. D. (1994). Teacher attitudes toward mathematics. Journal Of Instructional Psychology, 21(1), 44-48.
* ↑ Kolstad, R. K., Hughes, S., & Briggs, L. D. (1994). Teacher attitudes toward mathematics. Journal Of Instructional Psychology, 21(1), 44-48.
* ↑ Fox, A. M. (2015). Teacher self-efficacy, content and pedagogical knowledge, and their relationship to student achievement in algebra i. Dissertation Abstracts International Section A, 75,
* ↑ Fox, A. M. (2015). Teacher self-efficacy, content and pedagogical knowledge, and their relationship to student achievement in algebra i. Dissertation Abstracts International Section A, 75,
* ↑ Chears-Young, J. B. (2015). The association between math teachers' moral judgment development and self-efficacy beliefs, and their relationship with student achievement. Dissertation Abstracts International Section A, 76,
* ↑ Kagan, D.M. (1992). Implications of research on teacher belief. Educational psychologists, 27, 65-90.
* ↑ Ghaith, G., & Yaghi, H. (1997). Relationships among experience, teacher efficacy, and attitudes toward the implementation of instructional innovation. Teaching and Teacher Education, 13, 451-458.
* ↑ Zimmerman, B. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp.13-39). San Diego, CA: Academic Press.
* ↑ Zimmerman, B. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp.13-39). San Diego, CA: Academic Press.
* ↑ Zimmerman, B. J., & Martinez-Pons, M. (1990). Student differences in self-regulated learning: Relating grade, sex, and giftedness to self-efficacy and strategy use. Journal of Educational Psychology, 82, 51-59.
* ↑ Zimmerman, B. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp.13-39). San Diego, CA: Academic Press.
* ↑ Camahalan, F. G. (2006). Effects of Self-Regulated Learning on Mathematics Achievement of Selected Southeast Asian Children. Journal Of Instructional Psychology, 33(3), 194-205.
* ↑ Zimmerman, B. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp.13-39). San Diego, CA: Academic Press.
* ↑ Camahalan, F. G. (2006). Effects of Self-Regulated Learning on Mathematics Achievement of Selected Southeast Asian Children. Journal Of Instructional Psychology, 33(3), 194-205.
* ↑ Camahalan, F. G. (2006). Effects of Self-Regulated Learning on Mathematics Achievement of Selected Southeast Asian Children. Journal Of Instructional Psychology, 33(3), 194-205.
* ↑ Camahalan, F. G. (2006). Effects of Self-Regulated Learning on Mathematics Achievement of Selected Southeast Asian Children. Journal Of Instructional Psychology, 33(3), 194-205.
* ↑ Renkl, A. (1999). Learning Mathematics from worked-out examples: Analyzing and Fostering self-explanations. European Journal of Psychology of Education, 14(4), 477-488. doi: http://dx.doi.org.proxy.lib.sfu.ca/10.1007/BF03172974
* ↑ Tu, C-T. (2011). An Instructional experiment: Using worked-out examples in mathematics problem-solving of elementary school students. Bulletin of Educational Psychology, 43(1), 25-50.
* ↑ Tajika, H., Nakatsu, N., Nozaki H., Neumann, E., & Maruno S. (2007). Effects of Self-Explanation as a Metacognitive Strategy for Solving Mathematical Word Problems. Japanese Psychological Research, 49(3), 222-233. doi: 10.1111./j.1468-5884.2007.00349.x
* ↑ Tajika, H., Nakatsu, N., Nozaki H., Neumann, E., & Maruno S. (2007). Effects of Self-Explanation as a Metacognitive Strategy for Solving Mathematical Word Problems. Japanese Psychological Research, 49(3), 222-233. doi: 10.1111./j.1468-5884.2007.00349.x
* ↑ Mevarech, Z. R., & Kramarski, B. (2003). The Effects of Metacognitive Training versus Worked-out Examples on Students’ mathematical reasoning. British Journal of Educational Psychology, 73(4), 449-471. doi: http://dx.doi.org.proxy.lib.sfu.ca/10.1348/000709903322591181
* ↑ Mevarech, Z. R., & Kramarski, B. (2003). The Effects of Metacognitive Training versus Worked-out Examples on Students’ mathematical reasoning. British Journal of Educational Psychology, 73(4), 449-471. doi: http://dx.doi.org.proxy.lib.sfu.ca/10.1348/000709903322591181
* ↑ Scheiter, K., Gerjets, P., & Schuh, J. (2010). The Acquisition of Problem-Solving Skills in Mathematics: How Animations Can Aid Understanding of Structural Problem Features and Solutions Procedures. Instructional Science, 38(5), 487-502. doi: http://dx.doi.org.proxy.lib.sfu.ca/10.1007/s11251-009-9114-9
* ↑ Scheiter, K., Gerjets, P., & Schuh, J. (2010). The Acquisition of Problem-Solving Skills in Mathematics: How Animations Can Aid Understanding of Structural Problem Features and Solutions Procedures. Instructional Science, 38(5), 487-502. doi: http://dx.doi.org.proxy.lib.sfu.ca/10.1007/s11251-009-9114-9
* ↑ Scheiter, K., Gerjets, P., & Schuh, J. (2010). The Acquisition of Problem-Solving Skills in Mathematics: How Animations Can Aid Understanding of Structural Problem Features and Solutions Procedures. Instructional Science, 38(5), 487-502. doi: http://dx.doi.org.proxy.lib.sfu.ca/10.1007/s11251-009-9114-9
* ↑ van Garderen, D., Scheuermann, A., & Poch, A. (2014). Challenges students identified with a learning disability and as high-achieving experience when using diagrams as a visualization tool to solve mathematics word problems. ZDM Mathematics Education, 46, 135–149.
* ↑ Larkin, J., & Simon, H. (n.d.). Why a Diagram is (Sometimes) Worth Ten Thousand Words. Cognitive Science, 65-100.
* ↑ Banerjee, B. (2011). The effects of using diagramming as a representational technique on high school students' achievement in solving math word problems. Dissertation Abstracts International Section A, 71, 394
* ↑ Uesaka, Y., Manalo, E., & Ichikawa, S. (2007). What kinds of perceptions and daily learning behaviors promote students' use of diagrams in mathematics problem solving?. Learning And Instruction, 17(3), 322-335.
* ↑ Uesaka, Y., Manalo, E., & Ichikawa, S. (2007). What kinds of perceptions and daily learning behaviors promote students' use of diagrams in mathematics problem solving?. Learning And Instruction, 17(3), 322-335.
* ↑ Uesaka, Y., & Manalo, E. (2012). Task-related factors that influence the spontaneous use of diagrams in math word problems. Applied Cognitive Psychology, 26, 251–260.
* ↑ van Garderen, D., & Scheuermann, A. (2015). Diagramming word problems: A strategic approach for instruction. Intervention in School and Clinic
* ↑ Walker, D. W., & Poteet, J. A. (1989-90). A comparison of two methods of teaching mathematics story problem-solving with learning disabled students. National Forum of Special Education Journal, 1, 44-51.
* ↑ Cobb, P. (1991). Reconstructing Elementary School Mathematics. Focus on Learning Problems in Mathematics, 13(2), 3–32.
* ↑ Brown, J.S., Burton, R.B. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155-192
* ↑ Nagel, N., & Swingen, C. C. (1998). Students’ explanations of place value in addition and subtraction. Teaching Children Mathematics, 5(3), 164–170.
* ↑ Nelson, P. M., Burns, M. K., Kanive, R., & Ysseldyke, J. E. (2013). Comparison of a math fact rehearsal and a mnemonic strategy approach for improving math fact fluency. Journal Of School Psychology, 51(6), 659-667. doi:10.1016/j.jsp.2013.08.003
* ↑ Jeon, K. (2012). Reflecting on PEMDAS. Teaching Children Mathematics, 18(6), 370-377.
* ↑ Jan, S. and Rodrigues, S., (2012). Students' difficulties in comprehending mathematical word problems in English language learning contexts. International Researcher, Vol. 1, Issue 3, Accessed Dec 4, 2015 fromhttp://www.academia.edu/2078056/STUDENTS_DIFFICULTIES_IN_COMPREHENDING_MATHEMATICAL_WORD_PROBLEMS_IN_ENGLISH_LANGUAGE_LEARNING_CONTEXTS
* ↑ Jan, S. and Rodrigues, S., (2012). Students' difficulties in comprehending mathematical word problems in English language learning contexts. International Researcher, Vol. 1, Issue 3, P. 156, Accessed Dec 4, 2015 fromhttp://www.academia.edu/2078056/STUDENTS_DIFFICULTIES_IN_COMPREHENDING_MATHEMATICAL_WORD_PROBLEMS_IN_ENGLISH_LANGUAGE_LEARNING_CONTEXTS
* ↑ http://www.council-for-learning-disabilities.org/mathematics-disabilities
* 1 2 3 4 Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). Cognitive Tutors: Lessons learned. Journal of the Learning Sciences, 4(2), 167.
* 1 2 Koedinger, K. R., & Corbett, A. (2006). Cognitive tutors. The Cambridge handbook of the learning sciences, 61-77.
* ↑ Koedinger, K. R. & Anderson, J. R. (1993). Effective use of intelligent software in high school math classrooms. In Proceedings of the World Conference on Artificial Intelligence in Education, (pp. 241-248). Charlottesv
* ↑ Koedinger, K., Anderson, J., Hadley, W., & Mark, M. (1997). Intelligent tutoring goes to school in the big city. Human-Computer Interaction Institute.
* ↑ Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in computer-based tutoring: Impact on learning rate, achievement and attitudes. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 245–252). New York, NY, USA: ACM.
* ↑ What Works Clearinghouse (ED). (2016). Carnegie Learning Curricula and Cognitive Tutor[R]. What Works Clearinghouse Intervention Report. What Works Clearinghouse.
* ↑ Morgan, P., & Ritter, S. (2002). An experimental study of the effects of Cognitive Tutor® Algebra I on student knowledge and attitude. Pittsburgh, PA: Carnegie Learning, Inc.
* ↑ Cabalo, J. V., Jaciw, A., & Vu, M. (2007). Comparative effectiveness of Carnegie Learning’s Cognitive Tutor® Algebra I curriculum: A report of a randomized experiment in the Maui School District. Palo Alto, CA: Empirical Education, Inc.
* ↑ Campuzano, L., Dynarski, M., Agodini, R., & Rall, K. (2009). The effectiveness of reading and mathematics software products: Findings from two student cohorts. Washington, DC: U.S. Department of Education, Institute of Education Sciences.
* ↑ Pane, J. F., Griffin, B. A., McCaffrey, D. F., Karam, R., Daugherty, L., & Phillips, A. (2013). Does an algebra course with tutoring software improve student learning?.