Nearly every educational institution uses a learning management system (LMS), often producing terabytes of data generated by thousands of people. We examine LMS grade and login data from a regional comprehensive university, specifically documenting key considerations for engineering features from these data when trying to predict student performance. We specifically document changes to LMS data patterns since Covid-19, which are critical for data scientists to account for when using historic data. We compare numerous engineered features and approaches to utilizing those features for machine learning. We finish with a summary of the implications of including these features into more comprehensive student performance models.
@article{arxiv.2504.02916,
title = {Feature Engineering on LMS Data to Optimize Student Performance Prediction},
author = {Keith Hubbard and Sheilla Amponsah},
journal= {arXiv preprint arXiv:2504.02916},
year = {2025}
}