From study tactics to learning strategies: an analytical method for extracting interpretable representations

Ed Fincham, Dragan Gasevic, Jelena Jovanovic, Abelardo Pardo

    Research output: Contribution to journalArticleResearchpeer-review

    52 Citations (Scopus)


    Research into self-regulated learning has traditionally relied upon self-reported data. While there is a rich body of literature that has extracted invaluable information from such sources, it suffers from a number of shortcomings. For instance, it has been shown that surveys often provide insight into students' perceptions about learning rather than how students actually employ study tactics and learning strategies. Accordingly, recent research has sought to assess students' learning strategies and, by extension, their self-regulated learning via trace data collected from digital learning environments. A number of studies have amply demonstrated the ability of educational data mining and learning analytics methods to identify patterns indicative of learning strategies within trace log data. However, many of these methods are limited in their ability to describe and interpret differences between extracted latent representations at varying levels of granularity (for instance, in terms of the underlying data of student actions and behaviour). To address this limitation, the present study proposes a new methodology whereby interpretable representations of student's self-regulating behaviour are derived at two theoretically inspired levels: that of learning strategies, and the study tactics that compose them.

    Original languageEnglish
    Pages (from-to)59-72
    Number of pages14
    JournalIEEE Transactions on Learning Technologies
    Issue number1
    Publication statusPublished - 2019


    • Data mining
    • Education
    • Electronic mail
    • Hidden Markov models
    • Informatics
    • Task analysis
    • Tools

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