Ensemble-Learning Techniques for Predicting Student Performance on Video-Based Learning

The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era has promoted the rise of the big data era in educational data. This situation has created an opportunity for an educator to utilize the available data from MOOCs to facilitate student learning and performance. Therefore, this research study aims to introduce three types of ensemble learning methods, which are stacking, boosting, and bagging, to predict student performance. These techniques combine the advantage of feature selection method and Synthetic Minority Oversampling Technique (SMOTE) algorithm as a method to balance the number of output features to build the ensemble learning model. As a result, the proposed AdaBoost type ensemble classifier has shown the highest prediction accuracy of more than 90% and Area Under the Curve (AUC) of approximately 0.90. Results by AdaBoost classifier have outperformed other ensemble classifiers, stacking and bagging as well as base classifiers.