Enhancing Teaching Quality via Machine Learning: Early Detection of At-Risk Students Using Smooth Target Encoding and Calibrated Probability Outputs

Students' family backgrounds and behaviors can affect their learning difficulties, but teachers managing large classes often cannot monitor each student's complex situation. Machine learning offers a solution by identifying anomalies in student performance data, enabling early detection of at-risk students. This study proposed a method that preprocessed data and applied smooth target encoding with a global mean prior and smoothing to address high-dimensional sparsity and small-sample category noise. Fitting encoding was performed on the training set to prevent data leakage into features. After training, temperature scaling calibration on the validation set produced probability outputs suitable for threshold-sensitive decision-making. Experimental results showed 92.31% accuracy, 95.24% F1 score, and 97.97% receiver operating characteristic–area under curve, demonstrating strong predictive performance for final student grades. This approach provided robust technical support for teachers to enhance teaching quality.