The Application and Optimization of Deep Learning in Recognizing Student Learning Emotions
Bibliographic record
Abstract
With the widespread application of deep learning technology across various fields, its potential value in educational technology, particularly in recognizing student learning emotions, has begun to gain attention.The real-time and accurate identification of learning emotions is crucial for facilitating personalized teaching and enhancing learning efficiency.This paper focuses on the automatic recognition of student learning emotions based on deep learning technology, aiming to improve the accuracy and practicality of recognition by optimizing the preprocessing of facial expression images and the temporal expression recognition model.The research starts with facial detection using Haar-like features and the Adaboost cascade method, followed by normalization of the detected facial images in scale, angle, and grayscale to enhance the system's robustness to facial image variations.Subsequently, a temporal expression recognition model based on a multi-attention fusion network is proposed.This model utilizes both shallow and deep features of deep learning, along with the prior knowledge of Facial Action Coding System (FACS), to capture the dynamic changes in facial expressions more intricately.Finally, by introducing three different attention mechanisms, this study significantly improved the efficiency and accuracy of emotion feature recognition in sequential data.The findings of this paper not only advance the technology of learning emotion recognition but also provide valuable insights for educational practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".