Classroom Video Image Emotion Analysis Method for Online Teaching Quality Evaluation
Bibliographic record
Abstract
Classroom emotion is an important dimension to evaluate teaching effect, and the application of image processing to online teaching emotion analysis has become an inevitable trend of development. Aiming at the problems of low accuracy of expression recognition, unclear emotion scheme for online teaching evaluation, and low applicability of expression recognition model in existing methods, this paper conducts a research on classroom video image emotion analysis method for online teaching quality evaluation. First, the classroom video image emotion analysis task is divided into facial expression recognition task and facial feature point location task, and multi-task learning is carried out to achieve real-time switching between the two tasks for different types of input. The tag attention mechanism is proposed to deeply mine the key areas of the face in the classroom video image, so as to maintain the compactness of the distribution of the center sample of the classroom video image and its neighborhood samples in the feature space. Finally, based on the expression activity of teachers and students in the online classroom, the online classroom teaching emotion is analyzed, and the online teaching quality is evaluated from the side. The experimental results verify the validity of the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".