Lightweight Model for Emotion Detection from Facial Expression in Online Learning
Bibliographic record
Abstract
Detecting educational emotion of students is important as this plays a vital role in their learning process. Generally, in a regular classroom, instructors can observe the emotion of the students by their facial expressions. In an online learning platform, it is quite challenging. Deep learning architectures are found to be efficient in detecting emotion from facial expressions. However, these architectures are very deep in nature and computationally expensive, which are not suitable to deploy on students’ edge devices. In this study, we propose a deep learning architecture based on MobileNet, which is lightweight in nature and suitable to deploy in edge devices. We performed a comparative analysis of the proposed architecture with some other state-of-the-art deep learning architectures using a dataset called "Spontaneous Facial Expression Database for Academic Emotion Inference in Online Learning (OL-SFED)" which was developed using an online learning platform. From the comparison, we found that the proposed architecture showed competitive performance in terms of accuracy with the state-of-the-art architectures while using a significantly less number of parameters than the others.
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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".