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Extraneous overload occurs when essential cognitive processing (required to understand the essential material in a multimedia message) and extraneous cognitive processing (required to process extraneous material or to overcome confusing layout in a multimedia message) exceed the learner's cognitive capacity. According to the cognitive theory of multimedia learning, the five ways to handle an extraneous overload situation are to: eliminate extraneous material (coherence principle), insert signals emphasizing the essential material (signaling principle), eliminate redundant printed text (redundancy principle), place printed text next to corresponding parts of graphics (spatial contiguity principle), and eliminate the need to hold essential material in working memory for long periods of time (temporal contiguity principle). The research reviewed in this chapter shows that instructional designers should be sensitive to the limitations of working memory by being careful about the amount and layout of information that is presented to learners.
One of the classic instructional effects associated with the Cognitive Load Theory (CLT) is the worked-example effect in cognitive skill acquisition (see Chapters 2 and 3, this volume; Paas & van Gog, 2006). Worked-out examples consist of a problem formulation, solution steps, and the final solution itself. They are commonplace in the instructional material pertaining to well-structured domains such as mathematics or physics (see Figure 5.1 for an exemplary worked-out example). When CLT researchers discuss “learning from worked-out examples,” they typically mean that after the introduction of one or more domain principles (e.g., mathematical theorem, physics law), learners should be presented with several examples rather than a single example, as it is commonly the case. Despite this emphasis on learning from examples, researchers working in this area acknowledge the importance of requiring learners to solve problems later on in cognitive skill acquisition so that they can reach proficiency in the domain they are studying.
In this chapter, we elaborate the theoretical assumptions and empirical findings involving the studying of worked-out examples and learning by problem solving in different phases of cognitive skill acquisition. Rather than summarizing the extensive literature on example-based learning and its implications for instructional design (for overviews, see Atkinson, Derry, Renkl, & Wortham, 2000, and Renkl, 2005), we instead focus on addressing the issues of: (a) when it is best to study worked-out solutions, (b) when it is best to solve problems, and (c) how the transition between these two learning methods should be structured.
People gain a deep understanding when they receive worked-out examples in initial cognitive skill acquisition. This is, however, only true when the following guidelines are considered: prompt or train self-explaining examples (guideline of self-explanation elicitation); provide principle-based, minimalist, and example-related instructional explanations as help (help guideline); design examples so that the relations between different representations can be easily detected (easy-mapping guideline); make salient the examples' structural features that are relevant for selecting the correct solution procedure (structure-emphasizing guideline); and facilitate the isolation of meaningful building blocks in worked-out procedures (meaningful building-blocks guideline). Furthermore, series of examples with successively faded worked-out steps should be employed in order to structure the transition from example study to problem solving in later phases of skill acquisition.
Learning by doing and learning by solving complex problems are methods of learning that are en vogue and frequently propagated in the literature on learning and instruction in general as well as on multimedia learning. However, often learners have a very restricted understanding of the domain when they try to solve the first problems. In this case, they typically rely on general, domain-unspecific problem-solving heuristics such as means-ends analysis. This actually leads to the correct answer in many cases. However, does such striving for the right answer lead to a profound understanding of the domain? Would it not be better for the students to begin solving problems after they have already gained some significant understanding of the domain?
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