Cited by 47Online Learning and AnalyticsOnline and Blended LearningIntelligent Tutoring Systems and Adaptive Learning

The validity and utility of activity logs as a measure of student engagement

Motz, Benjamin, Quick, Joshua, Schroeder, Noah, Zook, Jordon, Gunkel, Matthew·2019·Proceedings of the 9th International Conference on Learning Analytics & Knowledge

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.

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Crossref · 10.1145/3303772.3303789

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  • 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).”

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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

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