Abstract
One classical instructional effect of cognitive load theory (CLT) is the worked-example effect. Although the vast majority of studies have focused on well-structured and algorithmic sub-domains of mathematics or physics, more recent studies have also analyzed learning with examples from complex domains in which only heuristic solution strategies can be taught (e.g., troubleshooting, mathematical proving). Is learning by such examples also effective, and do the same instructional design principles apply? We discuss the main findings of an own research program and of related studies that addressed such questions. We found that CLT’s basic design principles also hold true for heuristic domains: Reduce extraneous load by employing examples, maximize germane load by fostering self-explanations, prevent cognitive overload by pretraining in the case of difficult learning materials, and by focusing attention on the most relevant aspects. Other typical CLT assumptions (e.g., better provide information for novice learners) were not confirmed in its generality. The present findings extend the applicability of CLT but also identify some potentially too simplistic assumptions.
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This research was partly funded by the Deutsche Forschungsgemeinschaft (DFG; Re 1040/4-1, 4-2, and 4-3).
This contribution reviews research on example-based learning in heuristic domains. Its main purpose is to propose five theses that are directly relevant for instructional design.
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Renkl, A., Hilbert, T. & Schworm, S. Example-Based Learning in Heuristic Domains: A Cognitive Load Theory Account. Educ Psychol Rev 21, 67–78 (2009). https://doi.org/10.1007/s10648-008-9093-4
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DOI: https://doi.org/10.1007/s10648-008-9093-4