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European Journal of Psychology of Education

, Volume 28, Issue 4, pp 1237–1263 | Cite as

Coordinating principles and examples through analogy and self-explanation

  • Timothy J. Nokes-Malach
  • Kurt VanLehn
  • Daniel M. Belenky
  • Max Lichtenstein
  • Gregory Cox
Article

Abstract

Research on expertise suggests that a critical aspect of expert understanding is knowledge of the relations between domain principles and problem features. We investigated two instructional pathways hypothesized to facilitate students’ learning of these relations when studying worked examples. The first path is through self-explaining how worked examples instantiate domain principles and the second is through analogical comparison of worked examples. We compared both of these pathways to a third instructional path where students read worked examples and solved practice problems. Students in an introductory physics class were randomly assigned to one of three worked example conditions (reading, self-explanation, or analogy) when learning about rotational kinematics and then completed a set of problem solving and conceptual tests that measured near, intermediate, and far transfer. Students in the reading and self-explanation groups performed better than the analogy group on near transfer problems solved during the learning activities. However, this problem solving advantage was short lived as all three groups performed similarly on two intermediate transfer problems given at test. On the far transfer test, the self-explanation and analogy groups performed better than the reading group. These results are consistent with the idea that self-explanation and analogical comparison can facilitate conceptual learning without decrements to problem solving skills relative to a more traditional type of instruction in a classroom setting.

Keywords

Analogy Explanation Generalization Instruction Learning Principles Problem solving Knowledge transfer Worked examples 

Notes

Acknowledgments

This work was supported by Grant SBE0354420 from the National Science Foundation, Pittsburgh Science of Learning Center (http://www.learnlab.org). No endorsement should be inferred. We thank members of Cognitive Science Learning Laboratory, Sarah Nokes-Malach, and two anonymous reviewers for their many helpful comments and suggestions on the paper.

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Copyright information

© Instituto Superior de Psicologia Aplicada, Lisboa, Portugal and Springer Science+Business Media Dordrecht 2012

Authors and Affiliations

  • Timothy J. Nokes-Malach
    • 1
  • Kurt VanLehn
    • 2
  • Daniel M. Belenky
    • 1
  • Max Lichtenstein
    • 1
  • Gregory Cox
    • 3
  1. 1.Department of Psychology, Learning Research and Development CenterUniversity of PittsburghPittsburghUSA
  2. 2.School of Computing, Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA
  3. 3.Department of Psychological and Brain SciencesIndiana UniversityBloomingtonUSA

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