Cognitive load

Learning theories are conceptual frameworks describing how information is absorbed, processed, and retained during learning. Cognitive, emotional, and environmental influences, as well as prior experience, all play a part in how understanding, or a world view, is acquired or changed and knowledge and skills retained.

In cognitive psychology, cognitive load refers to the total amount of mental effort being used in the working memory. Cognitive load theory was developed out of the study ofproblem solving by John Sweller in the late 1980s.

Sweller argued that instructional design can be used to reduce cognitive load in learners. Cognitive load theory differentiates cognitive load into three types: intrinsic, extraneous, and germane.

Intrinsic cognitive load is the effort associated with a specific topic. Extraneous cognitive load refers to the way information or tasks are presented to a learner. And, germane cognitive load refers to the work put into creating a permanent store of knowledge, or a schema.

Researchers Paas and Van Merriënboer developed a way to measure perceived mental effort which is indicative of cognitive load

 Heavy cognitive load can have negative effects on task completion, and it is important to note that the experience of cognitive load is not the same in everyone. The elderly, students, and children experience different, and more often higher, amounts of cognitive load.

High cognitive load in the elderly has been shown to affect their center of balance.

Theory

"Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance". Sweller's theory employs aspects of information processing theory to emphasize the inherent limitations of concurrent working memory load on learning during instruction. It makes use of the schema as primary unit of analysis for the design of instructional materials.

History

The history of cognitive load theory can be traced to the beginning of Cognitive Science in the 1950s and the work of G.A. Miller. In his classic paper. Miller was perhaps the first to suggest our working memory capacity has inherent limits. His experimental results suggested that humans are generally able to hold only seven plus or minus two units of information in short-term memory. And in the early 1970s Simon and Chase, were the first to use the term "chunk" to describe how people might organize information in short-term memory. This chunking of memory components has also been described as schema construction.

     In the late 1980s John Sweller developed cognitive load theory (CLT) while studying problem solving. Studying learners as they solved problems, he and his associates found that learners often use a problem solving strategy called means-ends analysis. He suggests problem solving by means-ends analysis requires a relatively large amount of cognitive processing capacity, which may not be devoted to schema construction. Sweller suggests that instructional designers should prevent this unnecessary cognitive load by designing instructional materials which do not involve problem solving. Examples of alternative instructional materials include what are known as worked-examples and goal-free problems.

      In the 1990s, cognitive load theory was applied in several contexts. The empirical results from these studies led to the demonstration of several learning effects: the completion-problem effect, modality effect, split-attention effect, worked-example effect, and expertise reversal effect.

Computation representation of mental workload

        Human mental workload has gained importance, in the last few decades, as a fundamental design concept in human-computer interaction, education and other fields. For people interacting with interfaces, computers and technological devices in general, the construct plays an important role. At a low level, while processing information, often people feel annoyed and frustrated; at higher level, mental workload is critical and dangerous as it leads to confusion, it decreases the performance of information processing and it increases the chances of errors and mistakes. It is extensively documented that either mental overload or under load negatively affect performance. Hence, designers and practitioners who are ultimately interested in system or human performance need answers about operator workload at all stages of system design and operation.

         At an early system design phase, designers require some explicit model to predict the mental workload imposed by their technologies on end-users so that alternative system designs can be evaluated. However, human mental workload is a multifaceted and complex construct mainly applied in cognitive sciences. A plethora of ad-hoc definitions can be found in the literature. Generally, it is not an elementary property, rather it emerges from the interaction between the requirements of a task, the circumstances under which it is performed and the skills, behaviours and perceptions of the operator. Although measuring mental workload has advantages in interaction and interface design, its formalisation as an operational and computational construct has not sufficiently been addressed. Many researchers agree that too many ad-hoc models are present in the literature and that they are applied subjectively by mental workload designers thereby limiting their application in different contexts and making comparison across different models difficult.

In a recent work, the nature of the concept of mental workload, from a computational perspective, is argued to be a defeasible phenomenon. It is a concept built upon a set of reasons that can be defeated by additional reasons. The reasonable assumptions behind this are:

• Assumption 1: human mental workload is a complex construct built over a network of pieces of evidence;
• Assumption 2: accounting and understanding the relationships of accounted pieces of evidence as well as resolving the inconsistencies that might arise from their interaction is essential in modelling human mental workload.

The main contribution of this thesis, is the introduction of a methodology, developed as a formal modular framework, to represent mental workload as a defeasible computational concept and to assess it as a numerical usable index. The research contributes to the body of knowledge by providing a modular framework built upon defeasible reasoning and formalised using argumentation theory in which workload can be measured, analysed, explained and applied in different contexts. The framework follows the Popperian test offalsifiability, this being also flexible and replicable. The preliminary solution proposed was designed for those scholars interested in engaging in the multi-disciplinary domain of mental workload and it is aimed at increasing its understanding and use. A further goal is to offer a new perspective on the formalisation of mental workload, encouraging further research on its representation, assessment and application in more general fields such as human–computer interaction, education, and educational psychology.

Theory

"Cognitive load theory has been designed to provide guidelines intended to assist in the presentation of information in a manner that encourages learner activities that optimize intellectual performance". Sweller's theory employs aspects of information processing theory to emphasize the inherent limitations of concurrent working memory load on learning during instruction. It makes use of the schema as primary unit of analysis for the design of instructional materials.

History

The history of cognitive load theory can be traced to the beginning of Cognitive Science in the 1950s and the work of G.A. Miller. In his classic paper. Miller was perhaps the first to suggest our working memory capacity has inherent limits. His experimental results suggested that humans are generally able to hold only seven plus or minus two units of information in short-term memory. And in the early 1970s Simon and Chase, were the first to use the term "chunk" to describe how people might organize information in short-term memory. This chunking of memory components has also been described as schema construction.

     In the late 1980s John Sweller developed cognitive load theory (CLT) while studying problem solving. Studying learners as they solved problems, he and his associates found that learners often use a problem solving strategy called means-ends analysis. He suggests problem solving by means-ends analysis requires a relatively large amount of cognitive processing capacity, which may not be devoted to schema construction. Sweller suggests that instructional designers should prevent this unnecessary cognitive load by designing instructional materials which do not involve problem solving. Examples of alternative instructional materials include what are known as worked-examples and goal-free problems.

      In the 1990s, cognitive load theory was applied in several contexts. The empirical results from these studies led to the demonstration of several learning effects: the completion-problem effect, modality effect, split-attention effect, worked-example effect, and expertise reversal effect.

Computation representation of mental workload

        Human mental workload has gained importance, in the last few decades, as a fundamental design concept in human-computer interaction, education and other fields. For people interacting with interfaces, computers and technological devices in general, the construct plays an important role. At a low level, while processing information, often people feel annoyed and frustrated; at higher level, mental workload is critical and dangerous as it leads to confusion, it decreases the performance of information processing and it increases the chances of errors and mistakes. It is extensively documented that either mental overload or under load negatively affect performance. Hence, designers and practitioners who are ultimately interested in system or human performance need answers about operator workload at all stages of system design and operation.

         At an early system design phase, designers require some explicit model to predict the mental workload imposed by their technologies on end-users so that alternative system designs can be evaluated. However, human mental workload is a multifaceted and complex construct mainly applied in cognitive sciences. A plethora of ad-hoc definitions can be found in the literature. Generally, it is not an elementary property, rather it emerges from the interaction between the requirements of a task, the circumstances under which it is performed and the skills, behaviours and perceptions of the operator. Although measuring mental workload has advantages in interaction and interface design, its formalisation as an operational and computational construct has not sufficiently been addressed. Many researchers agree that too many ad-hoc models are present in the literature and that they are applied subjectively by mental workload designers thereby limiting their application in different contexts and making comparison across different models difficult.

In a recent work, the nature of the concept of mental workload, from a computational perspective, is argued to be a defeasible phenomenon. It is a concept built upon a set of reasons that can be defeated by additional reasons. The reasonable assumptions behind this are:

• Assumption 1: human mental workload is a complex construct built over a network of pieces of evidence;
• Assumption 2: accounting and understanding the relationships of accounted pieces of evidence as well as resolving the inconsistencies that might arise from their interaction is essential in modelling human mental workload.

The main contribution of this thesis, is the introduction of a methodology, developed as a formal modular framework, to represent mental workload as a defeasible computational concept and to assess it as a numerical usable index. The research contributes to the body of knowledge by providing a modular framework built upon defeasible reasoning and formalised using argumentation theory in which workload can be measured, analysed, explained and applied in different contexts. The framework follows the Popperian test offalsifiability, this being also flexible and replicable. The preliminary solution proposed was designed for those scholars interested in engaging in the multi-disciplinary domain of mental workload and it is aimed at increasing its understanding and use. A further goal is to offer a new perspective on the formalisation of mental workload, encouraging further research on its representation, assessment and application in more general fields such as human–computer interaction, education, and educational psychology.

Transfer of learning

Transfer of learning is the idea that what one learns in school somehow carries over to situations different from that particular time and that particular setting.Transfer was amongst the first phenomenons tested in educational psychology. Edward Lee Thorndike was a pioneer in transfer research. He found that though transfer is extremely important for learning, it is a rarely occurring phenomenon. In fact, he held an experiment where he had the subjects estimate the size of a specific shape and then he would switch the shape. He found that the prior information did not help the subjects; instead it impeded their learning.

One explanation of why transfer does not occur often can be explained in terms of surface structure and deep structure. The surface structure is the way a problem is framed. The deep structure is the steps for the solution. For example, when a math story problem changes contexts from asking how much it costs to reseed a lawn to how much it costs to varnish a table, they have different surface structures, but the steps for getting the answers are the same. However, many people are more influenced by the surface structure. In reality, the surface structure is unimportant. Nonetheless, people are concerned with it because they believe that it will give them background knowledge on how to do the problem. Consequently, this interferes with people's understanding of the deep structure of the problem. Even if somebody is trying to concentrate on the deep structure, transfer still may be unsuccessful because the deep structure is not usually very obvious. Therefore, surface structure gets in the way of people's ability to see the deep structure of the problem and transfer the knowledge they have learned to come up with a solution to a new problem.

Current learning pedagogies focus on conveying rote knowledge, independent of the context within which gives it meaning. Because of this, students often struggle to transfer this stand-alone information into other aspects of their education. Students need much more than abstract concepts and self-contained knowledge; they need to be exposed to learning that is practiced in the context of authentic activity and culture. Critics of situated cognition, however, would argue that by discrediting stand-alone information, the transfer of knowledge across contextual boundaries becomes impossible.There must be a balance between situating knowledge while also grasping the deep structure of material, or the understanding of how one arrives to know such information.

Some theorists argue that transfer does not even occur at all. They believe that students transform what they have learned into the new context. They say that transfer is too much of a passive notion. They believe students, instead, transform their knowledge in an active way. Students don't simply carry over knowledge from the classroom, but they construct the knowledge in a way that they can understand it themselves.The learner changes the information they have learned to make it best adapt to the changing contexts that they use the knowledge in. This transformation process can occur when a learner feels motivated to use the knowledge; however, if the students does not find the transformation necessary it is less likely that the knowledge will ever transform.

Techniques and benefits

There are many different conditions that influence transfer of learning in the classroom. These conditions include features of the task, features of the learner, features of the organization and social context of the activity. The features of the task include practicing through simulations, problem-based learning, and knowledge and skills for implementing new plans. The features of learners include their ability to reflect on past experiences, their ability to participate in group discussions, practice skills, and participate in written discussions. All of the unique features will contribute to a student’s ability to use transfer of learning. There are structural techniques that can aid learning transfer in the classroom. These structural strategies include hugging and bridging.

Hugging uses the technique of simulating an activity in order to encourage reflexive learning. An example of the hugging strategy is when a student practices teaching a lesson or when a student role plays with another student. These examples encourage critical thinking which will engage the student and help them understand what they are learning which is one of the goals of transfer of learning as well as desirable difficulties.

Bridging is when instruction encourages thinking abstractly by helping to identify connections between ideas and to analyze those connections. An example is when a teacher lets the student analyze their past test results and the way in which they got those results. This includes amount of study time and study strategies. By looking at their past study strategies it can help them come up with strategies in the future in order to improve their performance. These are some of the ideas that are important to successful practices of hugging and bridging.

There are many benefits of transfer of learning in the classroom. One of the main benefits is the ability to quickly learn a new task. This has many real-life applications such as language and speech processing. Transfer of learning is also very useful in teaching students to use higher cognitive thinking by applying their background knowledge to new situations.

Dr. John Sweller

Further research by Dr. John Sweller into cognitive load theory has focused the discovering universal principles that can apply to human learning. The main goal of his research has been to discover the most efficient methods for instruction to make sure that the working memories of learners are not overloaded. When the working memories of learners are not overloaded, learners may more effectively develop schema in their long term memories. When new schema are developed during instruction, learning has occurred.

Figure 1: The human brain learning efficiently.

Under ideal learning conditions, the working memory of learners receives information both visually and audibly in appropriate sized chunks. The brain can then build upon the current schema in long term memory (Figure 1).

Learning Efficiency

Most recently, Dr. Sweller has summarized the findings from his research into cognitive load theory by developing them into the theory of learning efficiency. According to the the theory, there are three types of cognitive load. These are intrinsic load, germane load, and extraneous load.

• Intrinsic Load: This term refers to the inherent difficulty of the content being learned. The intrinsic load is built into the content and cannot be changed. Rather, content must be broken up into smaller chunks to avoid overloading working memory.

• Germane Load: This term refers to the relevant information that can be processed in working memory to assist with learning the instructional content.
• Extraneous Load: This term refers to irrelevant and unnecessary information that fills up working memory. It prevents learning from happening efficiently.

According to these principles, learning is efficient when:

• extraneous load is reduced,
• germane load is increased, and
• intrinsic load is managed with the appropriate techniques.

Furthermore, learning is inefficient if:

• extraneous load is high,
• germane load is low, and
• intrinsic load is not managed with appropriate techniques.

Therefore, failure to manage cognitive load properly results in cognitive overload and inefficient learning.

All individuals have the ability to develop mental discipline and the skill of mindfulness, the two go hand in hand. Mental discipline is huge in shaping what people do, say, think and feel. It's critical in terms of the processing of information and involves the ability to recognize and respond appropriately to new things and information people come across, or have recently been taught. Mindfulness is important to the process of learning in many aspects. Being mindful means to be present with and engaged in whatever you are doing at a specific moment in time. Being mindful can aid in helping us to more critically think, feel and understand the new information we are in the process of absorbing. The formal discipline approach seeks to develop causation between the advancement of the mind by exercising it through exposure to abstract school subjects such as science, language and mathematics. With student's repetitive exposure to these particular subjects, some scholars feel that the acquisition of knowledge pertaining to science, language and math is of "secondary importance", and believe that the strengthening and further development of the mind that this curriculum provides holds far greater significance to the progressing learner in the long haul.

The working memory model was proposed by Baddeley & Hitch (1974) as an alternative to the multi-store model of memory. It has been developed to directly challenge the concept of a single unitary store for short-term memories. The working memory model is based upon the findings of the dual-task study and suggests that there are four separate components to our working memory (STM). 

The most important component is the central executive; it is involved in problem solving/decision-making. It also controls attention and plays a major role in planning and synthesizing information, not only from the subsidiary systems but also from LTM. It is flexible and can process information from any modality, although it does have a limited storage capacity and so can attend to a limited number if things at one time.  

 Another part of the working memory model is the phonological loop, it stores a limited number of speech-based sounds for brief periods. It is thought to consist of two components - the phonological store (inner ear) that allows acoustically coded items to be stored for a brief period and the articulatory control process (the inner voice) that allows sub-vocal repetition of the items stored in the phonological store.

 Another important component is the visuo-spatial scratch pad; it stores visual and spatial information and can be thought of as an inner eye. It is responsible for setting up and manipulating mental images. Like the phonological loop, it has limited capacity but the limits of the two systems are independent. In other words, it is possible, for example, to rehearse a set of digits in the phonological loop while simultaneously making decisions about the spatial layout of a set of letters in the visual spatial scratchpad.  

References

1. Taylor, E.W. (2008). Transformative learning theory. New Directions for Adult and Continuing Education. Jossey-Bass. pp. 5–15.
2. Larsen-Freeman, D. (2013). Transfer of learning transformed.Language Learning: A Journal of Research in Language Studies, 63:S1. Retrieved fromhttp://onlinelibrary.wiley.com/doi/10.1111/j.1467-9922.2012.00740.x/abstract