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Educational Data Mining and Learning Analytics: Applications to Constructionist Research

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Abstract

Constructionism can be a powerful framework for teaching complex content to novices. At the core of constructionism is the suggestion that by enabling learners to build creative artifacts that require complex content to function, those learners will have opportunities to learn this content in contextualized, personally meaningful ways. In this paper, we investigate the relevance of a set of approaches broadly called “educational data mining” or “learning analytics” (henceforth, EDM) to help provide a basis for quantitative research on constructionist learning which does not abandon the richness seen as essential by many researchers in that paradigm. We suggest that EDM may have the potential to support research that is meaningful and useful both to researchers working actively in the constructionist tradition but also to wider communities. Finally, we explore potential collaborations between researchers in the EDM and constructionist traditions; such collaborations have the potential to enhance the ability of constructionist researchers to make rich inferences about learning and learners, while providing EDM researchers with many interesting new research questions and challenges.

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Notes

  1. For more information, consult Witten et al. (2011).

  2. For detailed descriptions of these algorithms and their application, consult Witten et al. (2011).

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Acknowledgments

Berland would like to thank the Complex Play Lab for help with this work, Don Davis for editorial help, and National Science Foundation Awards #SMA-1338508 and #EEC-1331655. Baker would like to thank support from the Bill and Melinda Gates Foundation, Award #OPP1048577, and from the National Science Foundation through the Pittsburgh Science of Learning Center, Award #SBE-0836012. Blikstein would like to thank the National Science Foundation through the CAREER Award #1055130, the AT&T Foundation, and the Lemann Foundation.

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Authors and Affiliations

  1. Department of Curriculum and Instruction, University of Wisconsin–Madison, Madison, WI, USA

    Matthew Berland

  2. Teacher’s College, Columbia University, New York, NY, USA

    Ryan S. Baker

  3. Graduate School of Education, Stanford University, Stanford, CA, USA

    Paulo Blikstein

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Correspondence to Matthew Berland.

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Berland, M., Baker, R.S. & Blikstein, P. Educational Data Mining and Learning Analytics: Applications to Constructionist Research. Tech Know Learn 19, 205–220 (2014). https://doi.org/10.1007/s10758-014-9223-7

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