Feature Engineering on LMS Data to Optimize Student Performance Prediction
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866909563807072256 |
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| author | Hubbard, Keith Amponsah, Sheilla |
| author_facet | Hubbard, Keith Amponsah, Sheilla |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02916 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feature Engineering on LMS Data to Optimize Student Performance Prediction Hubbard, Keith Amponsah, Sheilla Computers and Society Machine Learning 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. |
| title | Feature Engineering on LMS Data to Optimize Student Performance Prediction |
| topic | Computers and Society Machine Learning |
| url | https://arxiv.org/abs/2504.02916 |