A classification model for predicting course outcomes using ensemble methods

Abstract

Educational data mining has sparked a lot of attention in latest years. Many machine learning methods have been suggested to discover hidden information from educational data. The extracted knowledge assists institutions in enhancing the effectiveness of teaching tactics and the quality of education. As a result, it improves students' performance and educational outputs overall. In this paper, a classification model was built to classify students' grades in a specific course into different categories (binary and multi-level classification tasks). The dataset contains features related to academic and non-academic information. The models were built using a variety of machine learning algorithms: decision tree (J48), support vector machine (SVM), and k-nearest neighbor (K-NN). Furthermore, ensemble methods (bagging, boosting, random subspace, and random forest) which combined multiple decision tree classifiers were implemented to improve the models' performance. The data set was modified under two stages: features selection method and data augmentation using a method called synthetic minority over sampling technique (SMOTE). Based on the results of the experiments, it is possible to predict the students' performance successfully by using machine learning algorithms and ensemble methods. Random subspace obtained the best accuracy at two-level classification task with modified data with 91.20%. At the three-level classification task, the best accuracy was obtained by random forest with 87.18%.