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Example-based learning: exploring the use of matrices and problem variability

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Abstract

The purpose of the study was to investigate the efficacy of using faded worked examples presented in matrices with problem structure variability to enhance learners’ ability to recognize the underlying structure of the problems. Specifically, this study compared the effects of matrix-format versus linear-format faded worked examples combined with equivalent problem structure versus contrast problem structure on learning. A total of 113 undergraduate students recruited from campus were randomly assigned to one of the four experimental conditions formed by a 2 × 2 factorial design. The results revealed three significant interactions on accuracy of anticipations, near transfer and medium transfer, suggesting that matrices foster learning when they contain contrast-structure problems but not with equivalent-structure problems.

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Acknowledgments

This research was partially supported by Shanghai Pujiang Program (13PJC031), Shanghai Planning Office of Philosophy and Social Science (2014JJY001), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, China.

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Authors and Affiliations

  1. Arizona State University, Tempe, USA

    Mary A. Hancock-Niemic & Robert K. Atkinson

  2. Key Laboratory of Brain Functional Genomics (MOE and STCSM), The School of Psychology and Cognitive Science, East China Normal University, Shanghai, 200062, China

    Lijia Lin

  3. University of Freiburg, Freiburg Im Breisgau, Germany

    Alexander Renkl & Joerg Wittwer

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  1. Mary A. Hancock-Niemic
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Correspondence to Lijia Lin.

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Hancock-Niemic, M.A., Lin, L., Atkinson, R.K. et al. Example-based learning: exploring the use of matrices and problem variability. Education Tech Research Dev 64, 115–136 (2016). https://doi.org/10.1007/s11423-015-9403-8

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