Using Process Mining (PM) and Epistemic Network Analysis (ENA) for Comparing Processes of Collaborative Problem Regulation

  • Nadine MelznerEmail author
  • Martin Greisel
  • Markus Dresel
  • Ingo Kollar
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1112)


Learning Sciences research often concerns the analysis of data from individual or collaborative learning processes. For the analysis of such data, various methods have been proposed, including Process Mining (PM) and Epistemic Network Analysis (ENA). Both methods have advantages and disadvantages when analyzing learning processes. We argue that a concerted use of both techniques may provide valuable information that would be obscured when using only one of these methods. We demonstrate this by applying PM and ENA on data from a study that investigated how students regulate collaborative learning when faced with either motivational or comprehension-related problems. While PM showed that collaborative learners are more incoherent (i.e. more heterogeneous in their chosen activities) when regulating motivational problems than comprehension-related problems at the beginning, ENA revealed that in later stages of their learning process, they focus on fewer activities when being confronted with motivational than with comprehension-related problems. Thus, a combination of the two approaches seems to be warranted.


Epistemic network analysis Process mining Self-regulation Collaborative learning Co-regulation Shared regulation 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universität AugsburgAugsburgGermany

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