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 contribution

Abstract / Description of output

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 well-known 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 languageUndefined/Unknown
Title of host publicationProceedings of the Tenth International Conference on Learning Analytics & Knowledge
Place of PublicationNew York, NY, USA
PublisherACM Association for Computing Machinery
ISBN (Print)9781450377126
Publication statusPublished - 22 Mar 2020

Publication series

NameLAK ’20
PublisherAssociation for Computing Machinery

Keywords / Materials (for Non-textual outputs)

  • approaches to learning
  • learning analytics
  • learning strategies
  • personality traits

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