Managing Cognitive Load in the Design of Assistive Technology for Those with Learning Disabilities

Managing Cognitive Load in the Design of Assistive Technology for Those with Learning Disabilities

Boaventura DaCosta (Solers Research Group, USA) and Soonhwa Seok (eLearning Design Lab, University of Kansas, USA)
DOI: 10.4018/978-1-61520-817-3.ch002
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Abstract

This is the second of three chapters serving as the introduction to this handbook which addresses the relationship between human cognition and assistive technologies and its design for individuals with cognitive disabilities. In this chapter the authors present strategies to manage cognitive load in the design of instructional materials for those with learning disabilities. The authors introduce cognitive load theory, which proposes a set of instructional principles grounded in human information processing research that can be leveraged in the creation of efficient and effective learning environments. They attempt to separate conjecture and speculation from empirically-based study and consolidate more than twenty-five years of research to highlight the best ways in which to increase learning. Altogether, the authors affirm the approach discussed in the last chapter—that technology for learning should be created with an understanding of design principles empirically supported by how the human mind works, particularly when it comes to the design of assistive technologies for individuals with learning disabilities.
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Introduction

Cognitive Load, Assistive Technology, and Those with Learning Disabilities

In the last chapter, we learned that human information processing is constrained in both capacity and duration. We explained how working memory, a system that temporarily stores and manages information for performing complex cognitive tasks, is a contradiction in terms. Its limitations cause it to be a bottleneck, restricted to seven (plus or minus two) chunks of information at any given time (Miller, 1956); yet, it is also the conduit for learning. This is a problem because the acquisition of new knowledge relies so heavily on the processing and storage capabilities of working memory (Low & Sweller, 2005; Sweller & Chandler, 1994). New information may potentially overload working memory capacity and subsequently encumber learning (Kalyuga, Chandler, & Sweller, 1999; Sweller, van Merrienboer, & Paas, 1998).

While we are all confronted by these information processing roadblocks, individuals with cognitive disabilities are at particular risk. There has been considerable research focused on working memory and children with learning disabilities (LDs). Generally speaking, research on the matter suggests that children with LDs have difficulty with working memory in areas such as reading and mathematics (e.g., Bull, Johnston, & Roy, 1999; de Jong, 1998; Hitch & McLean, 1991; Keeler & Swanson, 2001; McLean & Hitch, 1999; Passolunghi & Siegel, 2004). For example, those with reading disabilities are not poor readers, but have less working memory capacity than more skilled readers (Swanson & Siegel, 2001).

Fortunately, there has been considerable research in the study of cognitive load with regard to working memory. Even though some researchers have examined cognitive load under the premise of the working memory overload hypothesis (e.g., Niaz & Logie, 1993), the most predominant work on cognitive load can be attributed to cognitive load theory (CLT) (e.g., Chandler & Sweller, 1991; Kalyuga, Chandler, Tuovinen, & Sweller, 2001; Mousavi, Low, & Sweller, 1995; Sweller, 1999; Sweller et al., 1998)—a learning theory focused on the limitations of working memory during instruction.

This is the second of three chapters serving as the introduction to this handbook which addresses the relationship between human cognition and assistive technologies (ATs) and its design for individuals with cognitive disabilities. In this chapter we present strategies to manage cognitive load in the design of instructional materials for those with LDs. We introduce CLT, which proposes a set of instructional principles grounded in human information processing research that can be leveraged in the creation of efficient and effective instructional material. We attempt to separate conjecture and speculation from empirically-based study and consolidate more than twenty-five years of research to highlight the best ways in which to increase learning. This chapter also serves as scaffolding for the next chapter where we present the cognitive theory of multimedia learning (CTML), a learning theory which focuses on best practices in the use of visual and auditory information in multimedia-based instruction. Altogether, we affirm the approach discussed in the last chapter—that technology for learning should be created with an understanding of design principles empirically supported by how the human mind works, particularly when it comes to the design of ATs for individuals with LDs. Before we present these instructional principles, we begin this chapter with an in-depth discussion of CLT and its history.

Key Terms in this Chapter

Procedure Knowledge: “[K]nowledge underpinning performance of a task that is completed more or less the same way each time” (Clark et al., 2006, p. 168).

Cognitive Load Theory (CLT): A theory proposed by John Sweller and his colleagues focused on the limitations of working memory during instruction.

Completion Examples: A hybrid approach between worked examples and practice problems where some steps are provided as worked example and others are presented as practice problems.

Split-Attention Principle: An instructional principle proposing that if the instructional material is presented as a figure and text, split-attention can be circumvented by integrating the figure and text together (Sweller & Chandler, 1994).

Worked (Worked-out) Examples: A step-by-step example that demonstrates how a task is performed or how to solve a problem (Clark et al., 2006); the principle proposes learners learn more deeply when studying worked examples than studying practice problems (Sweller, 2005a).

Diverse Worked Examples: Varied worked examples and practice problems that help in the application of skills and knowledge to varied scenarios.

Modality Principle: An instructional principle proposing that presenting information in dual modalities spreads total induced load across the visual and auditory channels of working memory thereby reducing cognitive load (Low & Sweller, 2005; Sweller & Chandler, 1994; Sweller et al., 1998).

Cognitive Load: Refers to the amount of cognitive resources imposed on working memory.

Element Interactivity: Used to measure intrinsic load; think of an element as a single unit of information to be processed in working memory.

Intrinsic Load: One of three types of cognitive load that is caused by the natural complexity of the information that must be processed or the amount of element interactivity involved; this load is not under the control of the instructional designer.

German (Effective) Load: One of three types of cognitive load that can prove advantageous to learners in applying what they have learned to new tasks; it is caused by instructional design implementations that aid in meaningful learning and is under the control of the instructional designer.

Segmenting Principle: An instructional principle proposing that deeper learning can occur when a lesson is presented in learner-controlled segments rather than continuous units (Mayer, 2005a; Mayer & Moreno, 2003).

Pre-training Principle: An instructional principle proposing that learners learn more deeply when they are aware of names and behaviors of main concepts (Mayer, 2005a; Mayer & Moreno, 2003).

Process Knowledge: “[A] flow of events that summarize the operations of business, scientific, or mechanical systems” (Clark et al., 2006, p. 163).

Transfer of Learning: The ability to apply what has been learned to new settings or situations.

Dual-Coding Theory: The theory proposed by Allan Paivio that cognition is composed of verbal and non-verbal subsystems.

Far Transfer: Transfer of skills and knowledge that is applied under conditions of change; learners must be able to apply the skills and knowledge that they have learned to new situations.

Backwards Fading: A strategy in which worked examples become gradually replaced with practice problems in a lesson as the learner gains expertise in the subject matter (Clark et al., 2006).

Near Transfer: The transfer of skills and knowledge that are typically applied the same way each and every time the skills and knowledge are used.

Redundancy Principle: An instructional principle proposing that learners learn more deeply when identical information is not presented in more than one format (Mayer, 2005a).

Extraneous (Irrelevant Load) Load: One of three types of cognitive load that is caused in situations where instructional material is created using instructional design that ignores the limitations of working memory and consequently fails to focus working memory resources on schema construction and automation (Sweller, 2005a); this load is irrelevant to the learning goals at hand (Clark et al., 2006) and is considered to be under the control of the instructional designer (Pollock et al., 2002) and, consequently, is avoidable if proper instructional methods are applied.

Worked Example-Problem Pairs: The strategy of altering of worked example with similar practice problems (Clark et al., 2006).

Cognitive Theory of Multimedia Learning: A theory credited to Richard E. Mayer and his colleagues focused on best practices in the use of visual and auditory information in multimedia-based instruction.

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