Predicting Academic Performance of Students Using Machine Learning Techniques

Abstract

With the advancement of information technologies in recent years, it has become common practice to analyze educational data gathered from a variety of sources. Analyzing these education data helps in improving the education system by identifying factors that affect students' progress and assessing their performance in school. Therefore, studies on predicting students' academic performance with high accuracy and extracting meaningful models from vast volumes of education data remain of great importance for researchers. In this study, the academic performance of students is predicted using random forest, decision tree, support vector machines, XGBoost, and logistic regression machine learning algorithms with data from Portuguese schools. In order to increase the prediction performance of the developed model, the imbalance in the dataset is eliminated with the SMOTE technique, and the most important features affecting the performance of the students are selected by using the Recursive Feature Elimination method. The results show that the XGBoost algorithm outperforms the research in the literature with an accuracy value of 97,2%.