Prediction of student academic performance using machine learning algorithms

Abstract

Educational data mining (EDM) can be used to identify students’ activities, progress, achievements, and overall success in learning. EDM has become very popular in recent years as a convergence of learning, analysis, visualization, and recommendation which makes the learning process persistent and visible. In this paper, an EDM approach was conducted in order to classify and predict student performance with machine learning techniques. Based on the history educational dataset collected in Learning Management System (LMS) and Educational Management System (EMS), a model for the classification of student performance was conducted. A model is trained and evaluated on data from four different courses. Machine learning algorithms such as Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), and Support Vector Machine (SVM) are analyzed. Support Vector Machine (SVM) classifier was finally selected for model training and evaluation. Although the proposed model gave quite good results, there is room for improvement in future work, which is discussed in the paper.