Machine Learning Predictions of Student Outcomes: The Role of Educational Structure and Social Stressors in Czech Municipalities

Persistent disparities in student learning outcomes across Czech municipalities highlight the challenge of ensuring equitable access to quality education. These disparities are not only associated with demographic and economic conditions but also with the responsibility of municipalities and institutions to address structural inequalities. This study applies machine learning and SHAP analysis to predict student learning outcomes across municipalities with extended jurisdiction (MEJs), using demographic, economic, social, and housing indicators. Results highlight the dominant role of educational structure, with the share of people without secondary education and the proportion of younger adults holding college degrees emerging as the most influential predictors. Social and housing stressors, including parental executions, poverty destabilization, and housing allowances, further moderate outcomes, revealing nonlinear threshold effects that refine the explanatory narrative. The combined model achieved an R² of 0.629, confirming that while demographic and educational indicators explain most of the variance, contextual vulnerabilities add interpretive richness by identifying vulnerable subgroups. These findings underscore the dual influence of structural educational attainment and social stressors on student performance, while emphasizing educational responsibility as a key dimension in promoting equity and sustainable development.