Cognitive Issues in Tailoring Multimedia Learning Technology to the Human Mind

Cognitive Issues in Tailoring Multimedia Learning Technology to the Human Mind

Slava Kalyuga (The University of New South Wales, Australia)
DOI: 10.4018/978-1-60566-014-1.ch030
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In order to design effective and efficient multimedia applications, major characteristics of human cognition and its processing limitations should be taken into account. A general cognitive system that underlies human performance and learning is referred to as our cognitive architecture. Major features of this architecture will be described first. When technology is not tailored to these features, its users may experience cognitive overload. Major potential sources of cognitive load during multimedia learning and how we can measure levels of this load will be presented next. Some recently developed methods for managing cognitive overload when designing multimedia applications and building adaptive multimedia systems will be described in the last two sections, which will be followed by the conclusion.
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Human Cognitive Architecture

Existing theoretical models of human cognition and empirical evidence indicate several major characteristics that underline operation of this system in learning and performance (see Sweller, 2003; van Merriënboer & Sweller, 2005, for more detailed descriptions of these features). First of all, our cognitive system is knowledge-based. It includes a large store of organized information with effectively unlimited storage capacity and duration. This store of knowledge is called long-term memory (LTM). It contains a vast base of organized domain-specific knowledge structures that allow us to treat multiple elements of information as a single higher-level chunk. Such structures allow us to rapidly classify problem situations and retrieve appropriate procedures for handling these situations instead of employing inefficient search-based strategies.

Another key feature of our cognitive system is the mechanism that significantly limits the scope of immediate changes to this information store, thus preventing possibility of major disruptions. The concept of working memory (WM) is a currently accepted implementation of this mechanism. The essential common attribute of most existing models of WM (e.g., Baddeley, 1986; Cowan, 2001) is its severe limitations in capacity and duration when dealing with novel information. Working memory not only temporarily stores and transforms information that is in the focus of our attention, but also constructs and updates mental representations of a current situation or task. If more than a few novel elements of information are processed simultaneously in WM, its capacity may become overloaded. According to cognitive load theory, processing limitations of working memory and associated cognitive load represent a major factor influencing the effectiveness of learning (Sweller, van Merrienboer, & Paas, 1998).

WM capacity is distributed over partly independent auditory and visual modules. For example, Baddeley’s (1986) model includes the phonological loop that processes auditory information (verbal or written material in an auditory form), and the visuospatial sketchpad that deals with visual information such as diagrams and pictures. Therefore, limited WM capacity could be effectively expanded by using more than one sensory modality, and instructional materials with dual-mode presentation (e.g., a visual diagram accompanied by an auditory text) can be more efficient than equivalent single modality formats. The amount of information that can be processed using both auditory and visual channels might exceed the processing capacity of a single channel.

The next two important features of our cognitive architecture define the means by which we are able to acquire a huge knowledge base in LTM, considering very restrictive conditions of slow and incremental changes to this base. Firstly, most of this information is actively reconstructed or reorganized (within WM) information borrowed from other stores, that is, from knowledge bases of other people delivered through variety of media. Secondly, if such external stores of information are not available (including the cases when the information is truly new), the system has a default general problem-solving mechanism for the generation of new information, a random search followed by tests of effectiveness.

Key Terms in this Chapter

Cognitive Load Theory: An instructional theory describing implications of processing limitations of human cognitive architecture. The theory distinguishes between the essential and extraneous forms of cognitive load and suggests a variety of techniques for managing essential and reducing extraneous load in learning.

Cognitive Theory of Multimedia Learning: An instructional theory that applies principles of cognitive load theory to the design of learning environments that use mixed sensory modalities. The theory describes the processes of selecting, organizing, and integrating information from the separate verbal and pictorial channels, and suggests principles that enhance these processes.

Split-Attention Effect: A positive effect on learning by physically integrating separate sources of information in space or synchronizing them in time. The effect is due to eliminating or reducing cognitive overload caused by cross-referencing different representations.

Modality Effect: Improved learning that occurs when separate sources of nonredundant information are presented in alternate, auditory, or visual forms. The effect is explained by increased working memory capacity when using more than one modality.

Cognitive Architecture: A general cognitive system that underlies human performance and learning. The understanding of human cognition within a cognitive architecture requires knowledge of memory organization, forms of knowledge representation, mechanisms of problem solving, and the nature of human expertise.

Redundancy Effect: Improved learning of complex material by avoiding unnecessary duplication of the same information using different modes of presentation.

Expertise Reversal Effect: A reversal in the relative effectiveness of instructional methods as levels of learner knowledge in a domain change. For example, extensive instructional support could be beneficial for novices, but disadvantageous for more experienced learners.

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