Dynamic analysis of classroom engagement sentiment based on multilevel feature extraction and Transformer CNN-LSTM integrated model: personalized behavior prediction for non-English learners in a cross-modal adversarial learning framework
Bibliographic record
Abstract
The article proposes a novel cross-modal adversarial learning framework for analyzing the emotional dynamics of non-English learners during classroom engagement and predicting their individualized behaviors.The framework combines multilevel feature extraction and Transformer CNN-LSTM integrated model to handle multimodal data more efficiently and capture the complex relationship between emotions and behaviors.Low-level and high-level multilevel features are then extracted from the raw multimodal data.Meanwhile, Transformer is utilized to mine long-distance dependencies between multimodal data, CNN extracts local features, and LSTM is used to model dynamic changes in time series.In addition, the framework introduces adversarial training to learn shared features across modalities.Before 50 rounds of training, the CL-Transformer model loss function, emotion recognition accuracy, and behavior prediction accuracy converge, showing the fastest training speed and training results.The algorithm in this paper has more than 90% precision, recall, and F1 scores for emotion recognition and behavior prediction, and the recognition accuracy for different emotions is up to 0.96.In the fifth stage of the case study, the classroom emotion conversion rate and arousal is up to 0.66, and the model predicts that the probability of cell phone playing behavior is the highest for learners who are in angry moods, which is 64.7%.The learners' classroom emotional acceptance as well as behavioral integration have an impact on their classroom engagement.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".