Using process mining to analyse self-regulated learning: a systematic analysis of four algorithms

John Saint, Yizhou Fan, Shaveen Singh, Dragan Gasevic, Abelardo Pardo

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

Abstract

The conceptualisation of self-regulated learning (SRL) as a process that unfolds over time has influenced the way in which researchers approach analysis. This gave rise to the use of process mining in contemporary SRL research to analyse data about temporal and sequential relations of processes that occur in SRL. However, little attention has been paid to the choice and combinations of process mining algorithms to achieve the nuanced needs of SRL research. We present a study that 1) analysed four process mining algorithms that are most commonly used in the SRL literature - Inductive Miner, Heuristics Miner, Fuzzy Miner, and pMineR; and 2) examined how the metrics produced by the four algorithms complement each. The study looked at micro-level processes that were extracted from trace data collected in an undergraduate course (N=726). The study found that Fuzzy Miner and pMineR offered better insights into SRL than the other two algorithms. The study also found that a combination of metrics produced by several algorithms improved interpretation of temporal and sequential relations between SRL processes. Thus, it is recommended that future studies of SRL combine the use of process mining algorithms and work on new tools and algorithms specifically created for SRL research.

Original languageEnglish
Title of host publicationLAK21 Conference Proceedings - The Impact we Make: The contributions of learning analytics to learning
Subtitle of host publicationThe Eleventh International Conference on Learning Analytics & Knowledge
EditorsMaren Scheffel, Nia Dowell, Srecko Joksimovic, George Siemens
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages333-343
Number of pages11
ISBN (Electronic)9781450389358
DOIs
Publication statusPublished - 2021
EventInternational Learning Analytics & Knowledge Conference 2021 - Online, Irvine, United States of America
Duration: 12 Apr 202116 Apr 2021
Conference number: 11th
https://www.solaresearch.org/events/lak/lak21/
https://dl.acm.org/doi/proceedings/10.1145/3448139 (Proceedings)

Conference

ConferenceInternational Learning Analytics & Knowledge Conference 2021
Abbreviated titleLAK 2021
CountryUnited States of America
CityIrvine
Period12/04/2116/04/21
Internet address

Keywords

  • Learning analytics
  • Micro-level process analysis
  • Process mining
  • Self-regulated learning

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