Emotion Recognition in Learning Scenes Supported by Smart Classroom and Its Application
Bibliographic record
Abstract
Emotion recognition technology is one of the important applications of artificial intelligence and machine learning in the field of education.By recognizing the emotions of students in learning scenes, teachers can better understand the learning status of students and provide them with personalized learning resources and help.Current emotion recognition methods are mainly based on static facial emotions, neglecting the temporal features of facial emotions, which may lead to inaccurate recognition results.In order to overcome these challenges, this study conducts research on emotion recognition and its application in learning scenes supported by smart classrooms.The Transformer encoder is used to extract the temporal features of student facial emotions based on learning scenes, i.e., the selfattention module of the encoder is used to extract the temporal features of facial emotions in learning scenes.Residual attention networks, Transformers, and non-local neural networks are used to extract facial emotion features from different perspectives and levels.The combination of Vision-Transformer (ViT) and NetVLAD enables the model to learn the features of data from multiple perspectives, thereby improving the generalization ability of the model.The experimental results verify the effectiveness of the constructed 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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".