Analytics of learning strategies: the association with the personality traits

Wannisa Matcha, Dragan Gašević, Jelena Jovanović, Nora'Ayu Ahmad Uzir, Chris W Oliver, Andrew Murray, Danijela Gasevic

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

21 Citations (Scopus)


Studying online requires well-developed self-regulated learning skills to properly manage one's learning strategies. Learning analytics research has proposed novel methods for extracting theoretically meaningful learning strategies from trace data originating from formal learning settings (online, blended, or flipped classroom). Thus identified strategies proved to be associated with academic achievement. However, automated extraction of theoretically meaningful learning strategies from trace data in the context of massive open online courses (MOOCs) is still under-explored. Moreover, there is a lacuna in research on the relations between automatically detected strategies and the established psychological constructs. The paper reports on a study that (a) applied a state-of-the-art analytic method that combines process and sequence mining techniques to detect learning strategies from the trace data collected in a MOOC (N=1,397), and (b) explored associations of the detected strategies with academic performance and personality traits (Big Five). Four learning strategies detected with the adopted analytics method were shown to be theoretically interpretable as the wellknown approaches to learning. The results also revealed that the four detected learning strategies were predicted by conscientiousness, emotional instability, and agreeableness and were associated with academic performance. Implications for theoretical validity and practical application of analytics-detected learning strategies are also provided.

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


  • Approaches to learning
  • Learning analytics
  • Learning strategies
  • Personality traits

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