A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective

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dc.contributor.authorMatcha, W.
dc.contributor.authorUzir, N.A.
dc.contributor.authorGasevic, D.
dc.contributor.authorPardo, A.
dc.date.issued2020
dc.description.abstractThis 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 wellknown model of self-regulated learning proposed by Winne and Hadwin. The results show that existing LADs are rarely grounded in learning theory, cannot be suggested to support metacognition, do not offer any information about effective learning tactics and strategies, and 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). The MULAS consists of the four dimensions that are cyclically and recursively interconnected including: theory, design, feedback, and evaluation.
dc.description.statementofresponsibilityWannisa Matcha, Nora’ayu Ahmad Uzir, Dragan Ga sevic, and Abelardo Pardo
dc.identifier.citationIEEE Transactions on Learning Technologies, 2020; 13(2):226-245
dc.identifier.doi10.1109/TLT.2019.2916802
dc.identifier.issn1939-1382
dc.identifier.issn1939-1382
dc.identifier.orcidPardo, A. [0000-0002-6857-0582]
dc.identifier.urihttps://hdl.handle.net/2440/139786
dc.language.isoen
dc.publisherIEEE
dc.relation.granthttp://purl.org/au-research/grants/arc/DP190102286
dc.rights© 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
dc.source.urihttps://doi.org/10.1109/tlt.2019.2916802
dc.subjectDashboards; empirical research; feedback; information visualization; learning analytics; self-regulated learning
dc.titleA Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective
dc.typeJournal article
pubs.publication-statusPublished

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