Using machine learning to predict student mathematics performance in six East Asian countries: evidence from PISA 2022.

Introduction: In the contemporary era of artificial intelligence and rapidly evolving knowledge systems, mathematics performance has become a critical competency for younger generations. Although the mathematical achievement of students in East Asian countries has attracted increasing scholarly attention, studies employing machine learning techniques to examine the combined determinants of their success remain limited. Methods: This study evaluates six machine learning models-Random Forest, LightGBM, XGBoost, AdaBoost, Elastic Net, and Linear Regression to identify the most accurate algorithm for predicting mathematics performance among students from six high-performing East Asian countries/economies participating in the Programme for International Student Assessment (PISA) 2022. A sample of 26,969 fifteen-year-old students was analyzed. Following model selection, a post hoc feature selection procedure was applied, retaining the 24 most influential predictors from an initial set of 62 variables to ensure analytical parsimony while preserving model performance. SHapley Additive exPlanations (SHAP) values and SHAP interaction analyses were employed to quantify the magnitude, direction, and heterogeneity of each predictor's contribution at the individual level, including nonlinear relationships. Results: XGBoost emerged as the optimal model, demonstrating superior predictive accuracy ( Discussion and conclusion: These findings underscore the central role of student-proximal determinants in mathematics achievement within Confucian Heritage Culture educational contexts. Interpreted through the lens of self-determination theory, the results carry important implications for educational policy and practice, particularly in prioritizing self-efficacy development, optimizing instructional time, and promoting equitable learning environments. The study also contributes theoretically and offers directions for future research.