Study on the construction of learners’ emotion perception model and its improvement of behavioral patterns in artificial intelligence-assisted education based on deep learning
Bibliographic record
Abstract
With the wide application of deep learning in the field of education, student emotion perception has become one of the research hotspots. The study recognizes learners’ facial expressions by face detection algorithm after collecting learners’ data and preprocessing. Algorithms such as convolutional neural network and ConvLSTM are used to recognize learners’ emotions, and learners’ emotions are constructed to be modeled. Evaluate the learner emotion performance of this paper’s model and compare it with other emotion recognition models. The model of this paper is used for practical research to collect students’ emotions in six classes, and statistics and analysis are performed. Finally, by studying the relationship between students’ emotions and behaviors, targeted suggestions for improving students’ behaviors are proposed. The accuracy of this paper’s model in recognizing student emotions on the RAF-DB dataset and classroom dataset is 90.32% and 97.65%, respectively, which is much higher than that of other pre-trained models. The recognition accuracy of this paper’s model for eight types of student emotions is between [0.93, 0.98]. In the statistics of classroom students’ emotions, the main emotions of students in session 1 were concentration, in session 2 were surprise and concentration, in sessions 3, 4, and 6 were surprise and delight, and in session 5 were concentration and disappointment. Focus was significantly positively correlated with “serious attendance”, “thinking”, “answering questions”, “discussing” and “doing tests”, tiredness was significantly positively correlated with “answering questions”, “reviewing” and “deserting”, boredom was significantly positively correlated with “answering questions”, “doing quizzes”, “reviewing” and “desertion”, doubts were significantly positively correlated with “discussing”, “doing quizzes” and “reviewing”, distraction was significantly positively correlated with “reviewing” and “desertion”, happiness was significantly positively correlated with “discussion”, and disappointment was significantly positively correlated with “desertion”.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".