Feature Engineering on LMS Data to Optimize Student Performance Prediction

Keith Hubbard, Sheilla Amponsah
4/3/2025
cs.CYcs.LG

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Abstract

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.

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Cite this paper

@article{hubbard2025feature,
  title  = {Feature Engineering on LMS Data to Optimize Student Performance Prediction},
  author = {Keith Hubbard and Sheilla Amponsah},
  year   = {2025},
  eprint = {2504.02916},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.02916v1}
}

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