Deep Learning-based Student Engagement Detection using CNN, Mobile Net, ResNet50, VGG16
Bibliographic record
Abstract
In large classrooms, tutors or student-driven learning systems may manage or summarize what is happening more easily via the detection and measurement of student attention. Today, numerous institutions can create functional systems for aiding teachers or tutoring systems alike. Deep Learning has advanced machine learning that has proved effective in automating pupil engagement tracking. The article discusses a possible solution to the problem of procuring large volumes of labeled data by suggesting a transfer learning technique for classroom-based student behavior recognition. Using several CNN architectures, MobileNet, custom CNN, ResNet50, and VGG16, this paper proposes a deep learning method for detecting student engagement. 5000 samples, equally divided between the "Engaged" and "Not Engaged" classes, were created from a lightweight dataset of 1000 images. To overcome small sample sizes and enhance generalization, we employed cross-validation and transfer learning. While ResNet50 obtained the highest raw accuracy but displayed overfitting, MobileNet provided the best trade-off between accuracy (92.5%) and efficiency among the tested models. Our results lend credence to the viability of automated classroom engagement monitoring through deep learning.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".