A systematic review of empirical studies on learning analytics dashboards: a self-regulated learning perspective

Wannisa Matcha, Nora'ayu Ahmad Uzir, Dragan Gašević, Abelardo Pardo

Research output: Contribution to journalArticleResearchpeer-review


This paper presents a systematic literature review of learning analytics dashboards (LADs) research that reports empirical findings to assess the impact on learning and teaching. Several previous literature reviews identified self-regulated learning as a primary focus of LADs. However, there has been much less understanding how learning analytics are grounded in the literature on self-regulated learning and how self-regulated learning is supported. To address this limitation, this review analyzed the existing empirical studies on LADs based on the well-known model of self-regulated learning proposed by Winne and Hadwin. The results show that existing LADs i) are rarely grounded in learning theory; ii) cannot be suggested to support metacognition; iii) do not offer any information about effective learning tactics and strategies; and iv) have significant limitations in how their evaluation is conducted and reported. Based on the findings of the study and through the synthesis of the literature, the paper proposes that future research and development should not make any a priori design decisions about representation of data and analytic results in learning analytics systems such as LADs. To formalize this proposal, the paper defines the model for user-centered learning analytics systems (MULAS). MULAS consists of the four dimensions that are cyclically and recursively inter-connected including: theory, design, feedback, and evaluation.
Original languageEnglish
Pages (from-to)226 - 245
Number of pages20
JournalIEEE Transactions on Learning Technologies
Issue number2
Publication statusPublished - Apr 2020


  • learning analytics
  • Information visualisation
  • data science
  • dashboards

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