Beyond busy work: rethinking the measurement of online student engagement
Bibliographic record
Abstract
To combat high failure and student drop-out rates, universities have developed strategies to monitor online student engagement through measurable activities. In this study, we explore if and how these monitoring activities accurately measure online engagement. We interviewed nine highly engaged online third-year students throughout a semester to find out more about what engagement meant for them and how they enacted it in the online space, both visibly and invisibly. According to students in this study, traditional measures of online engagement were not perceived as valuable to their learning. The students complained about the ‘busy work’ – tasks that kept them busy or that monitored their engagement through a metrics-based tool. The students reported a number of other activities that prompted their engagement in learning; many of these would not be picked up by the usual ways of measuring engagement. These findings invite educators to move away from having fixed ideas about where and how and when online students should be engaging. They invite critique of the superficial, descriptive, tick-the-box exercises that are usually designed to monitor engagement by computer rather than through human interaction. They offer educators an opportunity to explore other ways of understanding student engagement in the online space.
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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 | 0.038 | 0.163 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| 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".