Robust Multimodal Engagement Recognition Using Emotion–Gaze–Pose Fusion with Lightweight LLM Temporal Reasoning
Bibliographic record
Abstract
This work presents a lightweight multimodal framework for real-time student engagement recognition using emotion, gaze, and pose fusion enhanced with Gemini-3-based temporal reasoning. The system integrates facial emotion recognition, gaze estimation, head-pose analysis, and action detection (hand raise, looking left/right/down, listening) to generate interpretable behavioral summaries over 3–5 second windows. These summaries are processed by an LLM to infer engagement states: Engaged, Distracted, Confused, and Bored. The paper includes methodology, dataset statistics, ablation studies, baseline comparisons, and a complete code implementation. This Zenodo upload contains the full Overleaf LaTeX source, diagrams, images, and figures needed to reproduce the PDF.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.004 |
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; both teacher heads agree on what is shown here.
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".