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In this chapter, I propose three distinct purposes of multiple representations and suggest that these lead to different design principles and learning activities. Multiple representations can play a complementary role when learners exploit differences in their form and content by switching between and selecting the appropriate representation for the task at hand. Constraining benefits are achieved when learners can profit from the support of a familiar representation to understand a new and complex representation. Finally, if learners abstract across multiple representations, they can construct a deeper understanding of the nature of the representations and the domain they represent. This chapter updates a review of studies that have used multiple representations for these purposes and identifies some of the circumstances that influence the effectiveness of using multiple representations in these ways.
Multiple representations in multimedia learning play a complementary role when learners exploit differences in computational properties or information by switching between representations and selecting the appropriate representation for the task at hand. The functional taxonomy that serves as the basis of this chapter is one proposed by the author as part of the DeFT framework for learning with multiple representations. It suggests that there are three main functions that multiple representations play when supporting learning, namely complementary, constraining, and constructing functions. Complementary multiple representations support learning by taking advantage of the differences between representations. In summary, there is evidence that providing (or asking learners to generate) multiple representations that learners must systematically relate to one another can indeed help learners come to a deeper understanding of phenomena under investigation. The chapter also discusses implications for cognitive theory and instructional design.
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