Understanding the disparities in Mathematics performance: An interpretability-based examination

Abstract

Problem:Educational disparities in Mathematics performance are a persistent challenge. This study aims to unravel the complex factors contributing to these disparities among students internationally, with a focus on the interpretability of the contributing factors. Methodology:Utilizing data from the Programme for International Student Assessment (PISA), we conducted rigorous preprocessing and variable selection to prepare for applying binary classification interpretability models. These models were trained using the Stratified K-Fold technique to ensure balanced representation and assessed using six key metrics. Solution:By applying interpretability models such as Shapley Additive Explanations (SHAP) analysis, we identified critical factors impacting student performance, including reading accessibility, critical thinking skills, gender, and geographical location. Results:Our findings reveal significant disparities linked to resource availability, with students from lower socioeconomic backgrounds possessing fewer books and demonstrating lower performance in Mathematics. The geographical analysis highlighted regional educational disparities, with certain areas consistently underperforming in PISA assessments. Gender also emerged as a determinant, with females contributing differently to performance levels across the spectrum. Conclusion:The study provides insights into the multifaceted determinants of student Mathematics performance and suggests potential avenues for future research to explore global interpretability models and further investigate the socioeconomic, cultural, and educational factors at play.