Abstract—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.
Index Terms—AdaBoosting, ensemble learning, educational data mining, SMOTE algorithm.
Chin-Wei Teoh, Sin-Ban Ho, and Khairi Shazwan Dollmat are with Faculty of Computing and Informatics, Multimedia University, 63100 Cyberjaya, Malaysia (e-mail: email@example.com, firstname.lastname@example.org, email@example.com).
Chuie-Hong Tan is with Faculty of Management, Multimedia University, 63100 Cyberjaya, Malaysia (e-mail: firstname.lastname@example.org).
Cite: Chin-Wei Teoh, Sin-Ban Ho, Khairi Shazwan Dollmat, and Chuie-Hong Tan, "Ensemble-Learning Techniques for Predicting Student Performance on Video-Based Learning," International Journal of Information and Education Technology vol. 12, no. 8, pp. 741-745, 2022.Copyright © 2022 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).