A Comparative Approach for Facial Expression Recognition in Higher Education Using Hybrid-Deep Learning from Students' Facial Images
Bibliographic record
Abstract
Online education has become increasingly common due to the Covid-19 pandemic.A key difference between online and face-to-face education is that instructors often cannot see their students' facial expressions online.This is problematic because facial expressions can help an instructor gauge engagement and understanding.Therefore, it would be useful to find a mechanism whereby students' facial expressions during an online lecture could be monitored.This information can be used as feedback for teachers to change a particular teaching method or maintain another method.This research presents a system that can automatically distinguish students' facial expressions.These comprise eight expressions (anger, attention, disgust, fear, happiness, neutrality, sadness, and surprise).The data for this research was collected from pictures of 70 university students' facial expressions.The data included 6720 images of students' faces distributed equally among the eight expressions mentioned above, that is, 840 images for each category.In this paper, pre-trained deep learning networks (AlexNet, MobileNetV2, GoogleNet, ResNet18, ResNet50, and VGG16) with transfer learning (TL) and K-fold validation (KFCV) were used for recognizing the facial expressions of students.The experiments were conducted using MATLAB 2021a and the best results were recorded by ResNet18 for F1-score and for AUC curve 99%, and 100% respectively.
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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 | 0.000 | 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 | 0.000 | 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) | 0.001 | 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".