Learner-facing learning analytic – Feedback and motivation: A critique

Anelika Maag, Chandana Withana, Srijana Budhathoki, Abeer Alsadoon, Trung Hung VO

Research output: Contribution to journalReview articlepeer-review

6 Citations (Scopus)


Data analysis to guide the design and deployment of learning experiences has been in use in educational institutions for decades. While this has made it possible to predict retention, flags students at risk and more clearly assesses performance across a range of indicators, few benefits visible to students have resulted. Recent attempts to design learner-facing analytics seem to also have met with indifferent results as they appear to have neglected to focus on student personality and motivation or to train students appropriately to decipher LA-based feedback. The aim of this study is to investigate why current Learning Analytics (LA) systems have so little impact on student motivation. A further goal has been to scrutinize the latest research for potential grounding in theoretical concepts generally – but with emphasis on motivation. Results show that, with few exceptions, neither instructor- nor learner-facing LA currently in use at universities take into consideration student personalities as neither are grounded in appropriate theory. This study contributes to the field of LA and Motivation by providing a clear (if bleak) picture of the lack of focus on student personality and motivation in terms of LA feedback. It is, therefore, timely that to remind the research community that an understanding of the learner's personality, attributes, and sources of motivation is central to the effectiveness of any feedback design. We see this paper as a starting point for a much-needed deeper discussion.

Original languageEnglish
Article number101764
Number of pages10
JournalLearning and Motivation
Publication statusPublished - Feb 2022


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