Combining analytic methods to unlock sequential and temporal patterns of self-regulated learning

John Saint, Dragan Gaševic, Wannisa Matcha, Nora'Ayu Ahmad Uzir, Abelardo Pardo

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

11 Citations (Scopus)


The temporal and sequential nature of learning is receiving increasing focus in Learning Analytics circles. The desire to embed studies in recognised theories of self-regulated learning (SRL) has led researchers to conceptualise learning as a process that unfolds and changes over time. To that end, a body of research knowledge is growing which states that traditional frequency-based correlational studies are limited in narrative impact. To further explore this, we analysed trace data collected from online activities of a sample of 239 computer engineering undergraduate students enrolled on a course that followed a flipped class-room pedagogy. We employed SRL categorisation of micro-level processes based on a recognised model of learning, and then analysed the data using: 1) simple frequency measures; 2) epistemic network analysis; 3) temporal process mining; and 4) stochastic process mining. We found that a combination of analyses provided us with a richer insight into SRL behaviours than any one single method. We found that better performing learners employed more optimal behaviours in their navigation through the course's learning management system.

Original languageEnglish
Title of host publicationLAK 2020 Conference Proceedings
EditorsVitomir Kovanović, Maren Scheffel, Niels Pinkwart, Katrien Verbert
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages10
ISBN (Electronic)9781450377126
Publication statusPublished - 2020
EventInternational Conference on Learning Analytics and Knowledge 2020 - Frankfurt, Germany
Duration: 23 Mar 202027 Mar 2020
Conference number: 10th (Website) (Website)


ConferenceInternational Conference on Learning Analytics and Knowledge 2020
Abbreviated titleLAK 2020
Internet address


  • Epistemic Network Analysis
  • Learning Analytics
  • Micro-level Processes
  • Process Mining
  • Self-regulated Learning

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