Predicting Students at Risk of Dropout in Technical Course Using LMS Logs
Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that corre...
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Veröffentlicht in: | Electronics (Basel) 2022-02, Vol.11 (3), p.468 |
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Sprache: | eng |
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Zusammenfassung: | Educational data mining is a process that aims at discovering patterns that provide insight into teaching and learning processes. This work uses Machine Learning techniques to create a student performance prediction model, using academic data and records from a Learning Management System, that correlates with success or failure in completing the course. Six algorithms were employed, with models trained at three different stages of their two-year course completion. We tested the models with records of 394 students from 3 courses. Random Forest provided the best results with 84.47% on the F1 score in our experiments, followed by Decision Tree obtaining similar results in the first subjects. We also employ clustering techniques and find different behavior groups with a strong correlation to performance. This work contributes to predicting students at risk of dropping out, offers insight into understanding student behavior, and provides a support mechanism for academic managers to take corrective and preventive actions on this problem. |
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ISSN: | 2079-9292 2079-9292 |
DOI: | 10.3390/electronics11030468 |