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Process mining techniques for analysing patterns and strategies in students’ self-regulated learning

Metacognition and Learning volume 9, pages 161–185 (2014)Cite this article

Abstract

Referring to current research on self-regulated learning, we analyse individual regulation in terms of a set of specific sequences of regulatory activities. Successful students perform regulatory activities such as analysing, planning, monitoring and evaluating cognitive and motivational aspects during learning not only with a higher frequency than less successful learners, but also in a different order—or so we hypothesize. Whereas most research has concentrated on frequency analysis, so far, little is known about how students’ regulatory activities unfold over time. Thus, the aim of our approach is to also analyse the temporal order of spontaneous individual regulation activities. In this paper, we demonstrate how various methods developed in process mining research can be applied to identify process patterns in self-regulated learning events as captured in verbal protocols. We also show how theoretical SRL process models can be tested with process mining methods. Thinking aloud data from a study with 38 participants learning in a self-regulated manner from a hypermedia are used to illustrate the methodological points.

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Acknowledgments

We thank the German Research Foundation for funding (DFG: BA 2044/5-1).

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Authors and Affiliations

  1. Instructional Media, University of Würzburg, Oswald-Külpe-Weg 82, 97074, Würzburg, Germany

    Maria Bannert & Christoph Sonnenberg

  2. Centre for Research on Computer-Supported Learning and Cognition, University of Sydney, Sydney, Australia

    Peter Reimann

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  1. Maria Bannert
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Correspondence to Maria Bannert.

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Bannert, M., Reimann, P. & Sonnenberg, C. Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition Learning 9, 161–185 (2014). https://doi.org/10.1007/s11409-013-9107-6

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