Metacognition and Learning

, Volume 9, Issue 2, pp 161–185

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

  • Maria Bannert
  • Peter Reimann
  • Christoph Sonnenberg
Article

DOI: 10.1007/s11409-013-9107-6

Cite this article as:
Bannert, M., Reimann, P. & Sonnenberg, C. Metacognition Learning (2014) 9: 161. doi:10.1007/s11409-013-9107-6

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.

Keywords

Self-regulated learning Temporal patterns in SRL Process mining Fuzzy Miner 

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Maria Bannert
    • 1
  • Peter Reimann
    • 2
  • Christoph Sonnenberg
    • 1
  1. 1.Instructional MediaUniversity of WürzburgWürzburgGermany
  2. 2.Centre for Research on Computer-Supported Learning and CognitionUniversity of SydneySydneyAustralia

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