Beyond binary outcomes: Evaluating and mitigating bias in national standardized test score prediction

Predictive analytics in education holds promise for supporting student learning but poses risks of bias potentially disadvantaging certain groups based on demographic attributes such as gender. Prior work on fairness in educational prediction has largely focused on binary outcomes in higher education, leaving continuous predictions, such as standardized test scores, comparatively understudied, especially in early schooling where consequences can be long-lasting. Despite a few fairness metrics being available for continuous outcomes, they often rely on mean differences, obscuring distributional disparities that may disadvantage specific students. Even fewer studies have attempted to address bias in continuous predictions. This study addresses these gaps by introducing a fairness evaluation framework grounded in statistical distance measures with two adapted metrics, AreaPDF and AreaCDF, that capture group disparities across the full distribution of continuous predicted outcomes. We evaluate these metrics in the context of predicting national standardized test scores in early childhood education and further develop five reweighting-based data-balancing methods to mitigate bias in continuous prediction tasks. Empirical analyses show that the proposed metrics reveal biases overlooked by traditional measures and that the reweighting strategies substantially improve fairness, underscoring the importance of aligning debiasing methods with demographic and score-distribution characteristics.