College English Teaching and Evaluation Research Based on Machine Learning

ABSTRACT Scientific prediction and evaluation of teaching activities are critical for higher vocational colleges to optimize teaching strategies. However, existing college English teaching evaluation studies lack systematic integration of machine learning models, leading to low prediction accuracy. To address this issue, this study proposes a linear weighted fusion model of Logistic Regression (LR) and Gradient Boosting Decision Tree (GBDT) for English exam pass rate prediction. Based on 14,000 teaching evaluation samples, the model is optimized through SMOTE sampling, L1 regularization feature selection, and weight tuning (optimal weight α = 0.4 for LR). Experimental results show that the fusion model achieves an AUC value of 0.632, which is 5.2–7.2 percentage points higher than single models, with a generalization error rate of only 12%. Further feature importance analysis reveals that question‐type scores, assignment completion rate, and English training frequency are the top three key factors affecting pass rates, while test format and question‐type categories have non‐significant impacts. This study provides a data‐driven solution for college English teaching evaluation, offering actionable insights for personalized instruction and filling the gap of insufficient data support in traditional evaluation methods.