Lightweight and Interpretable Detection of Affective Engagement for Online Learners
Bibliographic record
Abstract
Offering online learners with a personalized, ethical, and privacy-protecting pedagogical companion that can detect their affective engagement and that can run on the devices they use to learn (edge devices) requires not only generating lightweight models but also interpretable, trustworthy, and generalizable ones. SqueezeNet is a lightweight deep neural network architecture, which is more popular for the edge devices. This paper showcases how SqueezeNet appears not to provide as meaningful explanations of its predictions of academic emotions as those of a lightweight traditional CNN model. More specifically, the winning CNN model, consisting of 473,317 parameters, delivers a predictive accuracy of 99.4% on the testing set, while maintaining an identical level of descriptive accuracy. In contrast, the best SqueezeNet model comprises 141,301 parameters (3x less) and has a predictive accuracy of 93.7% and a dropping descriptive accuracy of 84.0%. In brief, the SqueezeNet model does not maintain a similarly high descriptive accuracy and does not effectively identify facial features corresponding to the confusion, distraction, enjoyment, neutrality, and fatigue emotions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".