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Comparing Four Instructional Techniques for Promoting Robust Knowledge

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Abstract

Robust knowledge serves as a common instructional target in academic settings. Past research identifying characteristics of experts’ knowledge across many domains can help clarify the features of robust knowledge as well as ways of assessing it. We review the expertise literature and identify three key features of robust knowledge (deep, connected, and coherent) and four means of assessing these features (perception, memory, problem solving, and transfer). Focusing on the domains of math and science learning, we examine how four instructional techniques—practice, worked examples, analogical comparison, and self-explanation—can promote key features of robust knowledge and how those features can be assessed. We conclude by discussing the implications of this framework for theory and practice.

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Notes

  1. By contrast, Logan and colleagues have argued that practice enables learners to progress from an early phase characterized by reliance on general algorithms to a later phase characterized by retrieval of declarative memories of specific problems (Compton and Logan 1991; Logan 1988).

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Acknowledgments

This work was supported by Grant SBE0836012 from the National Science Foundation to the Pittsburgh Science of Learning Center (http://www.learnlab.org). We gratefully acknowledge Christian Schunn, Joel Chan, and Jooyoung Jang for their feedback on this work, Jose Mestre and Brian Ross for insights into the ideas discussed, and Daniel Robinson and three anonymous reviewers for their very helpful suggestions and comments on the article.

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Richey, J.E., Nokes-Malach, T.J. Comparing Four Instructional Techniques for Promoting Robust Knowledge. Educ Psychol Rev 27, 181–218 (2015). https://doi.org/10.1007/s10648-014-9268-0

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