STUDENT DROPOUT PREDICTION USING MACHINE LEARNING - Osemwegie, Eric E.; Amadin, Frank I. (FUDMA JOURNAL OF SCIENCES, 2023)
In a higher education environment, we considered the likelihood of probable dropouts from a first-year undergraduate Computer Science program. In order to achieve this, data from five academic sessions were obtained from the Department of Computer Science, University of Benin, Nigeria. Out of nine hundred and forty seven (947) data obtained, only a total of nine hundred and six (906) was usable after cleaning and preprocessing. Six distinct classifiers including Naive Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Artificial Neural Networks (ANN) were modeled for the prediction of student success and dropouts. The performance six were stated to have performed on average at 90.4%, 98.9%, 98.5%, 97.4%, 96.0% and 97.3% respectively. Although there wasn’t much of a performance difference between the DT, SVM, and LR, the LR model was chosen for deployment since it performs better than the other two models in terms of F1_score and Recall.