Predicting Students at Risk of Dropout in Technical Course Using LMS Logs

• Published in: Electronics (Basel) Vol. 11; no. 3; p. 468
• Main Authors: Tamada, Mariela Mizota, Giusti, Rafael, Netto, José Francisco de Magalhães
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Algorithms; At risk students; Classification; Clustering; COVID-19; Data mining; Decision trees; Distance learning; Education; Educational materials; Learning management systems; Machine learning; Performance prediction; Prediction models; Research methodology; Secondary schools; Students; Teaching
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11030468

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.