Abstract
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.
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Melzner, N., Greisel, M., Dresel, M., Kollar, I. (2019). Using Process Mining (PM) and Epistemic Network Analysis (ENA) for Comparing Processes of Collaborative Problem Regulation. In: Eagan, B., Misfeldt, M., Siebert-Evenstone, A. (eds) Advances in Quantitative Ethnography. ICQE 2019. Communications in Computer and Information Science, vol 1112. Springer, Cham. https://doi.org/10.1007/978-3-030-33232-7_13
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DOI: https://doi.org/10.1007/978-3-030-33232-7_13
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