| Summary: | This study proposes a machine learning (ML) framework to overcome emotion recognition challenges in classroom environments, where high facial expression similarity and complex postural backgrounds hinder the accurate analysis. First, the histogram of oriented gradients (HOGs) was used to extract seven facial expression features. Second, comparative analysis of six ML algorithms identified support vector machine (SVM) as the optimal classifier. Third, grid search with cross-validation enhanced SVM’s recognition performance by 13.9% accuracy, 11.3% precision, and 13.8% recall improvement. Fourth, students’ facial expressions were recognized using HOG and the optimized SVM during eight course tasks. A classroom teaching effect evaluation model was constructed to predict students’ learning concentration scores according to the positive degree of different facial expressions. Absolute error, relative error, scatter, and violin plots all demonstrate that the predicted concentration score is strongly linearly correlated with actual mission score and final grade; mean absolute errors were 1.95 and 3.3, while mean relative errors were 2.57 and 4.42%, respectively. This study provides a reliable new method for intervening in students’ learning concentration in advance and fostering the quality of classroom teaching.
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