Detecting learning strategies through process mining

John Saint, Dragan Gašević, Abelardo Pardo

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

    11 Citations (Scopus)


    The recent focus on learning analytics to analyse temporal dimensions of learning holds a strong promise to provide insights into latent constructs such as learning strategy, self-regulated learning, and metacognition. There is, however, a limited amount of research in temporally-focused process mining in educational settings. Building on a growing body of research around event-based data analysis, we explore the use of process mining techniques to identify strategic and tactical learner behaviours. We analyse trace data collected in online activities of a sample of nearly 300 computer engineering undergraduate students enrolled in a course that followed a flipped classroom pedagogy. Using a process mining approach based on first order Markov models in combination with unsupervised machine learning methods, we performed intra- and inter-strategy analysis. We found that certain temporal activity traits relate to performance in the summative assessments attached to the course, mediated by strategy type. Results show that more strategically minded activity, embodying learner self-regulation, generally proves to be more successful than less disciplined reactive behaviours.

    Original languageEnglish
    Title of host publicationLifelong Technology-Enhanced Learning
    Subtitle of host publication13th European Conference on Technology Enhanced Learning, EC-TEL 2018, Leeds, UK, September 3–5, 2018 Proceedings
    EditorsViktoria Pammer-Schindler, Mar Perez-Sanagustin, Hendrik Drachsler, Raymond Elferink, Maren Scheffel
    Place of PublicationCham Switzerland
    Number of pages14
    ISBN (Electronic)9783319985725
    ISBN (Print)9783319985718
    Publication statusPublished - 2018
    EventEuropean Conference on Technology Enhanced Learning (EC-TEL) 2018 - Leeds, United Kingdom
    Duration: 3 Sep 20185 Sep 2018
    Conference number: 13th (Proceedings)

    Publication series

    NameLecture Notes in Computer Science
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    ConferenceEuropean Conference on Technology Enhanced Learning (EC-TEL) 2018
    Abbreviated titleEC-TEL 2018
    Country/TerritoryUnited Kingdom
    Internet address


    • First order Markov models
    • Learning analytics
    • Process mining
    • Self-regulated learning
    • Temporal dynamics

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