A Predictive Model and Performance Evaluation in Mathematics for Primary Education

Abstract

This study investigates the application of predictive modelling to assess and forecast students’ academic performance in primary mathematics education. Four regression techniques, Linear Regression, Decision Tree Regression, Random Forest Regression, and K-Nearest Neighbours Regression, were implemented and comparatively evaluated. Model performance was measured using Mean Squared Error (MSE) as the primary metric. Results indicate that Linear Regression achieved the lowest MSE (1.33), establishing a strong predictive baseline. Although Decision Tree Regression effectively captured non-linear patterns, it yielded a substantially higher MSE (62.38), highlighting the risk of overfitting. Random Forest Regression improved generalization by aggregating multiple decision trees, achieving an MSE of 25.21. Meanwhile, K-Nearest Neighbours Regression provided localized predictive accuracy with a competitive MSE of 19.29. Collectively, these findings contribute to the growing body of research on data-driven approaches in education, providing practical insights for educators and policymakers to leverage predictive analytics and enhance learning outcomes in primary mathematics.