A Deep Reinforcement Learning Based Emotional State Analysis Method for Online Learning
Why this work is in the frame
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Bibliographic record
Abstract
With the development of artificial intelligence technology, the basic judgment of students' learning state can be realized through the comprehensive analysis of students' face, expression, behavior posture and other multi-modal data. However, due to the lack of end-to-end recognition model and complete data sets, it is impossible to achieve accurate analysis of learning status. In this paper, based on deep reinforcement learning, an online learning state analysis method based on affective computing is proposed. On the basis of student identity recognition, face recognition is carried out through an unsupervised expression recognition model based on Siam-RCNN, and then 3D CNNs is used to recognize the feature data set for timing extraction. The state of collaborative awareness learning is analyzed by using HMM model. After verification, the accuracy of emotional state recognition can reach 98.88%, which is in the leading level in the industry.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it