<span>Predicting learning status of informatics students at Dong Thap University based on machine learning models</span>

Abstract

For effective student support, it is essential to forecast student academic performance status. This study explores student academic performance and applies four machine learning (ML) models to predict student learning alert status. The research collects input data to build a model including parameters such as entrance grades, information about accommodation, learning environment, and first-semester result from undergraduates of the Faculty of Mathematics and Computer Education, Dong Thap University. The effective ML techniques such as Logistic regression (LR), Support vector machine (SVM), Decision trees (DTs), and Random Forest (RF) are suggested to predict student learning alert status. The outcomes are evaluated on metrics like Accuracy, Precision, Recall, and F1 score. The results show that the forecasting ability of logistic regression models outperforms in classifying student performance compared to other methods by yielding optimal classification results like high accuracy and Sensitivity followed by RF, SVM, and DTs.