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Cited by 76Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive LearningEducational Technology and E-Learning

A Systematic Review of Learning Analytics: Incorporated Instructional Interventions on Learning Management Systems

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Pan, Zilong, Biegley, Lauren, Taylor, Allen, Zheng, Hua·2024·Journal of Learning Analytics
DOI 10.18608/jla.2023.8093
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Abstract

The learning management system (LMS) is widely used in educational settings to support teaching and learning practices. The usage log data, generated by both learners and instructors, enables the development and implementation of learning analytics (LA) interventions aimed at facilitating teaching and learning activities. To examine the current status of the development and empirical impacts of learning analytics–incorporated interventions within LMSs on improving teaching and learning practices, we conducted a systematic review that examined 27 articles published from 2012 through 2023. The outcomes of this review provided valuable insights into the design and development of learning analytics–incorporated interventions implemented on LMSs and empirical evidence of the impacts of these interventions, along with implications to inform future design and applications.

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  • Reliance on static questionnaires has been increasingly questioned, as self-reported learning styles may not accurately reflect learners' actual study behaviors in digital environments.

    “However, the reliance on static questionnaires has been increasingly questioned, as selfreported learning styles may not accurately reflect learners' actual study behaviors in digital environments.”

    From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [8]

    Section 4.1.1.2 describes studies using surveys alongside LMS trace data, and Section 4.2.3 describes measuring engagement through LMS interactions. Neither passage assesses learning-style questionnaires or says self-reported learning styles fail to reflect actual study behavior. Searches for those terms and for questionnaire accuracy found no passage addressing the claim. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

  • Discrepancies between self-reported and behavior-derived learning styles were explicitly identified, highlighting the fluid and context-dependent nature of learner preferences.

    “Importantly, discrepancies between self-reported and behavior-derived learning styles were explicitly identified, highlighting the fluid and context-dependent nature of learner preferences [8][9][10][11].”

    From AI for All: Adaptive, Accessible, and Inclusive Learning Experiences in the Age of Intelligent LMSs (2026) · cites this paper as [8]

    Section 4.2.3 (Behavioral Outcomes; page unavailable) discusses LMS engagement and interactions, not discrepancies between self-reported and behavior-derived learning styles. Searches for learning styles, self-reports, observed behavior, and learner preferences found no passage addressing that comparison or the claimed fluidity of preferences. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

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  82. Zheng, L., Niu, J., & Zhong, L. (2022). Effects of a learning analytics-based real-time feedback approach on knowledge elaboration, knowledge convergence, interactive relationships and group performance in CSCL. British Journal of Educational Technology, 53(1), 130-149. https://doi.org/10.1111/bjet.13156 · DOI

Cited by

Sorted by citations

Authors
Pan, Z., Biegley, L., Taylor, A., & Zheng, H.
Affiliations
  • Lehigh University (US)
  • Charles R. Drew University of Medicine and Science (US)
ORCID
Venue
Journal of Learning Analytics
Publisher
Society for Learning Analytics Research
Published
2024-05-22
Volume
11
Issue
2
Pages
52-72
ISSN
1929-7750 (ISSN-L)
DOI
10.18608/jla.2023.8093
OpenAlex
W4398248030
Cited by
76
References
0
Citations by year
2026: 55 · 2025: 18 · 2024: 3
FWCI
5.74
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Learning Analytics (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
  • Physical Sciences › Computer Science › Information Systems › Educational Technology and E-Learning
Keywords
learning analytics, virtual learning environments, teaching and learning practices, intervention design

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