Optimizing Student Performance Prediction: A Comparative Analysis of Regression Algorithms and Feature Selection Techniques on LMS Log Data

This study investigates the predictive power of Learning Management System (LMS) log data for student performance in higher education. Analyzing interactions from 114 students in a sports pedagogy course, we compared linear regression (LR), random forest regression (LFR), and support vector regression (SVR), each paired with mutual information (MI) and backward elimination (BE) feature selection. Results show LMS log data alone can effectively predict final grades, with SVR+BE performing best (R 2 = 0.81, MAE = 4.54). Feature selection, particularly BE, consistently improved model performance across all algorithms. Key findings include: LMS interactions strongly predict academic performance; SVR outperforms other algorithms in capturing complex educational data relationships; and BE's superiority highlights the importance of feature interactions. This research advances educational data mining (EDM) by identifying optimal modeling approaches for LMS data, contributing to the development of early warning systems in online and blended learning environments.