Bridging the STEM Divide: Addressing Educational Disparity in Indiana

This paper proposes and develops a robust, cross-benchmarked machine learning framework for predicting SAT benchmark percentages across unified school districts in Indiana. The study utilizes state-provided datasets from the Indiana Department of Education for 300+ districts and created indices representing early-stage ethnic and gender disparity, average early-stage performance, enrollment, and a variety of funding mechanisms. After dimensionality reduction through principal component analysis (captured 83.35% original variance) and quantile-binning the SAT label, random forest (RF) and XGBoost (XGB) ensemble learning models were trained independently, reaching 0.71 and 0.72 classification accuracy, respectively. A composite model was developed by blending probability distributions from RF and XGB, reaching an optimal accuracy of 0.73. Macro-averaged values for precision, recall, and F1 scores similarly increased to 0.74, 0.69, and 0.71, respectively, enhancing class balance overall. Subsequently, XGBoost gain calculations and Shapley Additive Explanations (SHAP) plots were discussed to examine specific feature relationships with SAT classes. Notable variables including ethnic disparity and enrollment were geospatially graphed to identify key geographical patterns for future mitigation planning. This study concluded by recommending baseline policies encompassing small-district funding prioritization, corporate partnerships, and early-stage intervention.