Qiang Yin

Predicting student academic performance using TabPFN and SHAP: An interpretable machine learning approach

Predicting student academic performance is of significant importance for enabling timely educational interventions and improving educational outcomes in secondary education. However, existing predictive models in educational data mining often face the dual challenges of limited interpretability and suboptimal performan...