Design Factors for Effective Educational Animations and Simulations

knowledge, and depictive representations are best for drawing inferences because they are informationally complete. Any choice of representations in visual learning materials must take into account not only the function of the representation, but also the prior knowledge of the learners who will be using the materials. There is evidence that the cognitive load associated with depictive, iconic representations versus descriptive, symbolic representations depends in part on the prior knowledge of the learner (Lee et al., 2006; Plass et al., 2007). Descriptive, symbolic representations are informationally dense and require a larger amount of prior knowledge to interpret. However, once interpretation of symbolic representations becomes automatized, then a great deal of information can be conveyed in a very economical fashion. Depictive, iconic Monday, October 27, 2008 32 representations convey less information, but rely far less on prior knowledge for interpretation, making them more transparent. This suggests that depictive representations are best suited for novice or low-prior knowledge learners. Our ongoing research supports this claim (Plass et al., 2007), but further research is needed. Another growing area that needs additional research involves the link between visual representations and emotions in learning. The amygdala plays a central role in both emotion and memory (LeDoux, 2003), and it has been suggested that positive emotions facilitate working memory processes such as are required for creative problem solving, and help long-term memory and retrieval as well (Isen, Daubman, & Nowicki, 1987; Isen & Patrick, 1983). Though previous research on affect in learning has found that removing interesting but irrelevant materials increases learning (Mayer, 2001), recent research suggests that visual materials can be designed to induce a positive affect in learners and improve learning outcomes (Norman, 2003; Um, Song, & Plass, 2007). More research is needed on the role of positive affect on cognitive load and learning. The research reviewed in this paper point to a few additional issues that warrant future investigation. A pattern of findings has emerged that calls into question the additive nature of cognitive load induced by elements of a visual representation. For textual representations, adding more descriptive information has traditionally been associated with increased mental effort in processing the information. However, for visual representations, adding depictive information may in some cases reduce cognitive load by reducing the need to interpret the descriptive representations of text or making a visual representation easier to interpret. Further research is needed to identify the conditions in which adding visual materials (even redundant materials) results in a reduction of cognitive load. Another issue that emerges is the need for better grounding of cognitive load theory in basic research on brain functions. The concept of the “visual channel” is too vague, with recent neuroscience research on visual perception suggesting that visual cognitive load is more localized in specific functional areas (e.g., the “what” versus “where” pathways). Similarly, the overlap of visual and verbal processing needs further research. A related issue involves the influence of top-down processes on perception. If a learner’s prior knowledge affects how he or she actually perceives a situation, then this suggests that the well known “expertise reversal effect” may also be having an effect at a very basic perceptual level. Future Research on the Design of Animations and Simulations for Education A striking impression from the review of research on cognitive load in learning from animations and simulations was that researchers seemed to treat all dynamic representations as if they were alike. Yet there are many different types and categories of animations and simulations, and it has Monday, October 27, 2008 33 become clear that future research on the effectiveness of these materials should move on from asking questions about animations and simulations in general to basing these questions on an appropriate typology of dynamic visual representations. These typologies could be based on the type of representation used (e.g., graphs, maps, networks, diagrams, etc.), the level of abstraction (e.g., line drawing, schematic cartoon, photo-realistic, etc.) and the type of content that is represented. Another issue that requires more systematic investigation is the impact of interactivity on cognitive load. Here, again, a typology of levels of interactivity would be helpful in order to conceptualize future research on learners’ control of the materials, manipulation of the educational content, and related system feedback. Although research has provided insights into the effect of different types of interactivity on learning outcomes, the cognitive load implications of different types and levels of interactivity need to be better understood. A final issue warranting future research is concerned with the design of multiple simulations or animations. Initial research indicates that learning is facilitated when simulations become progressively more difficult and complex, and students progressively more knowledgeable about the simulation content (De Jong & Joolingen, 1998; Rieber & Parmley, 1995; Rieber, 2005). This finding mirrors research by Renkl, Atkinson, Maier, & Staley (2002) on fading from completely worked-out examples to problem-based learning, which showed that learners’ increasing expertise was able to compensate for the increased demands of problem solving on learners’ cognitive capacity (Renkl, 2005). Future research should investigate cognitive-load related questions of visual aspects of model progression, e.g., focusing on the development of a visual language for the representation of science content in computer simulations. 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