The validity and utility of activity logs as a measure of student engagement
Abstract
Learning management system (LMS) web logs provide granular, near-real-time records of student behavior as learners interact with online course materials in digital learning environments. However, it remains unclear whether LMS activity indeed reflects behavioral properties of student engagement, and it also remains unclear how to deal with variability in LMS usage across a diversity of courses. In this study, we evaluate whether instructors' subjective ratings of their students' engagement are related to features of LMS activity for 9,021 students enrolled in 473 for-credit courses. We find that estimators derived from LMS web logs are closely related to instructor ratings of engagement, however, we also observe that there is not a single generic relationship between activity and engagement, and what constitutes the behavioral components of "engagement" will be contingent on course structure. However, for many of these courses, modeled engagement scores are comparable to instructors' ratings in their sensitivity for predicting academic performance. As long as they are tuned to the differences between courses, activity indices from LMS web logs can provide a valid and useful proxy measure of student engagement.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Learning management system time-on-task is a reasonable proxy for student engagement, especially in online classes, although such analytics are imperfect.
“Although data analytics provided by the learning management system are certainly imperfect (e.g., Kelley, 2021), they have been demonstrated to predict learning outcomes (Santos et al., 2021;Saqr et al., 2017) and are considered a reasonable proxy for student engagement, especially in online classes (Motz et al., 2019).”
From Lessons from the field: Isn't it just common sense? Naïve realism as a barrier to learning (2024) · cites this paper as Motz et al., 2019
Cites
Jingle, Jangle, and Conceptual Haziness: Evolution and Future Directions of the Engagement Construct
Reschly, A. L., & Christenson, S. L. (2012) · Handbook of Research on Student Engagement · doi:10.1007/978-1-4614-2018-7_1 · Cited by 980 · see citing works
Kim, J., LaRose, R., & Peng, W. (2009) · CyberPsychology & Behavior · doi:10.1089/cpb.2008.0327 · Cited by 781 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Can crowdsourcing platforms be useful for educational research?
Wang, K. D., Chen, Z., & Wieman, C. (2024) · Proceedings of the 14th Learning Analytics and Knowledge Conference · doi:10.1145/3636555.3636897 · Cited by 9 · see citing works
Lessons from the field: Isn't it just common sense? Naïve realism as a barrier to learning
Dyer, K. D. (2024) · Family Relations · doi:10.1111/fare.12967 · Cited by 2 · see citing works
- Authors
- Motz, B., Quick, J., Schroeder, N., Zook, J., & Gunkel, M.
- Affiliations
- Indiana University (US)
- Venue
- Proceedings of the 9th International Conference on Learning Analytics & Knowledge
- Publisher
- ACM
- Published
- 2019-03-04
- Pages
- 300-309
- OpenAlex
- W2915641965
- Cited by
- 47
- References
- 2
- Citations by year
- 2026: 2 · 2025: 9 · 2024: 8 · 2023: 7 · 2022: 4 · 2021: 14 · 2020: 3
- FWCI
- 6.08
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Learning Analytics and Knowledge (LAK) (publishedVersion)
- Funders
- University of Michigan
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Social Sciences › Education › Online and Blended Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- student engagement, online learning, course structure, student performance prediction, student behavior
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review