Feature Engineering on LMS Data to Optimize Student Performance Prediction

• URL: http://arxiv.org/abs/2504.02916v1
• Date: Thu, 03 Apr 2025 13:11:42 GMT
• Title: Feature Engineering on LMS Data to Optimize Student Performance Prediction
• Authors: Keith Hubbard, Sheilla Amponsah,
• Abstract summary: We examine key considerations for engineering features from a regional comprehensive university’s learning management system (LMS) grade and login data.<n>We compare numerous engineered features and approaches to utilizing those features for machine learning.<n>We conclude with a summary of the implications of including these features into more comprehensive student performance models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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