SEDM: A Multi-Modal Deep Learning Approach for Detecting Student Engagement
Bibliographic record
Abstract
This paper introduces SEDM, a Student Engagement Detection Model that leverages computer vision and deep learning to monitor student engagement in both classroom and online environments. SEDM combines YOLOv9 for face detection, HopeNet for head pose estimation, and DeepFace for emotion analysis, and it uses temporal smoothing and engagement buffer to minimize noise. A peer-discussion mode is created to ensure collaborative interactions are not misclassified as disengagement. SEDM was evaluated in both classroom and online settings, achieving accuracies of $98.6 \%$ and $97.1 \%$ respectively, which outperform or match existing models that rely only on head pose detection. SEDM’s precise measurement of student engagement enables educators to identify drop-offs and refine their teaching strategies accordingly. The model is adaptable to various classroom settings and provides a scalable, data-driven approach to improving student learning experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".