Development of a Machine Learning Cumulative GPA Prediction Model using Explainable AI

Abstract

This study aims to develop an accurate, transparent, and interpretable model for predicting students’ Cumulative Grade Point Average (GPA) using an Educational Data Mining approach. The study adopts the Knowledge Discovery in Databases (KDD) framework, which includes data preprocessing, Z-transformation normalization, and feature selection. Three machine learning algorithms, namely Random Forest, XGBoost, and Support Vector Machine (SVM), are compared to determine the best-performing model. Model evaluation is conducted using a 10-fold cross-validation scheme with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics to ensure generalization capability. To address the black-box nature of machine learning models, Explainable Artificial Intelligence (XAI) techniques are applied using SHAP and LIME to provide both global and local interpretability of the predictions. The results indicate that XGBoost Regression achieves the best performance with the lowest error values. Previous GPA, attendance rate, and study duration are identified as the most influential predictors. The integration of XAI enables deeper insights for educators in supporting data-driven decision-making. Therefore, the proposed model has strong potential to be implemented as an early warning system for more effective and measurable academic interventions.