Enhancing Emotion Recognition in College Students' Online Learning: A Research on Integrating Feature Fusion and Attention Mechanisms
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Bibliographic record
Abstract
Aiming at key challenges in emotional recognition for college students in online learning, including the lack of experimental data sets, the imperfect emotion classification system, and the poor robustness of emotion recognition algorithms, this paper constructs an emotion recognition model for college students' online learning based on feature fusion and attention mechanism.First, based on face recognition technology, the Convolutional Neural Network (CNN) deep features, Histogram of Oriented Gradients (HOG) texture features, and Scale-Invariant Feature Transform (SIFT) features of images are extracted and fused, because three feature extraction algorithms have different feature extraction capabilities and have certain robustness to changes in image lighting, rotation, scale, and other factors, fusion can make feature vectors more comprehensive and rich in information, thereby improving the accuracy and robustness of detection and recognition; second, a ResNet network is constructed to complete the basic classification of learning expressions and verify the experimental results; In order to enhance the ability of deep learning methods to learn discriminative features from noisy signals and further improve classification accuracy, then, by combining the channel attention mechanism and soft thresholding to improve the ResNet network, a Deep Residual Shrinkage Network (DRSN) is constructed to achieve emotion recognition of college students' online learning; finally, through a horizontal comparison experiment of multiple different network structures, the effectiveness of the soft threshold attention mechanism is verified.This method obtains more complete facial expression features for emotion classification through feature fusion, and combines the channel attention mechanism module.The recognition accuracy of DRSN network is 84.12%, which is about 4.17% higher than the original ResNet network (79.95%).The application of this research can assess the concentration of college students in online learning and measure the degree of emotional engagement of college students.Through the analysis of model result data, teachers can understand the course content that college students are interested in, adjust teaching plans according to the learning status of students, help realize personalized teaching, and consolidate teaching results.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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