Comparative Evaluation of Modular Deep Learning Pipelines for Student Engagement Detection
Bibliographic record
Abstract
Student engagement detection is crucial in e-learning environments where educators have limited direct visual feedback of learner attentiveness. This paper proposes a modular three-stage pipeline for automated engagement detection, consisting of face detection, emotion recognition, and head pose estimation. We implement and compare alternative models for the latter two stages (DeepFace vs. HSE for emotion, and Hopenet vs. ShuffleNet for pose) and demonstrate that the proposed pipeline using YOLOv8 + HSE + Hopenet achieves superior performance. In evaluations on the public DAiSEE dataset, our best pipeline reaches 90.25% binary classification accuracy with an F1-score of 94.85%, outperforming existing approaches to binary engagement detection on this benchmark. We further show via ablation studies that the choice of emotion recognition model has the greatest impact on accuracy, while the head pose estimator provides complementary improvements. By comparing several pipeline configurations, we provide insights into the modular pipeline's adaptability and performance.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".