What are the indicators of Student Engagement in Learning Management Systems? A Systematized Review of the Literature
Bibliographic record
Abstract
Student engagement has an important role in academic achievement in all learning contexts, including e-learning environments. The extent of monitoring and promoting student engagement in e-learning affects the quality of education and is a determining factor for ensuring student’s success. Log data of students’ activities recorded in a learning management system (LMS) can be used to measure their level of engagement in the online teaching–learning process. No previous studies have been found stating a consistent and systematically raised list of LMS-based student engagement indicators, so this systematized review aimed to fulfill this gap. The authors performed an advanced search in the PubMed, Ovid, Google Scholar, Scopus, Web of Science, ProQuest, Emerald, and ERIC databases to retrieve relevant original peer-reviewed articles published until the end of June 2021. Reviewing the 32 included articles resulted in 27 indicators that were categorized into three themes and six categories as follows: (a) log-in and usage (referring to LMS, access to course material), (b) student performance (assignments, assessments), and (c) communication (messaging, forum participation). Among the categories, access to course material and messaging were the most and the least mentioned, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".