A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment





machine learning, weka, predictive model, ensemble, student performance prediction, classification algorithm, virtual learning


Virtual Learning Environments (VLE), such as Moodle and Blackboard, store vast data to help identify students' performance and engagement. As a result, researchers have been focusing their efforts on assisting educational institutions in providing machine learning models to predict at-risk students and improve their performance. However, it requires an efficient approach to construct a model that can ultimately provide accurate predictions. Consequently, this study proposes a hybrid machine learning framework to predict students' performance using eight classification algorithms and three ensemble methods (Bagging, Boosting, Voting) to determine the best-performing predictive model. In addition, this study used filter-based and wrapper-based feature selection techniques to select the best features of the dataset related to students' performance. The obtained results reveal that the ensemble methods recorded higher predictive accuracy when compared to single classifiers. Furthermore, the accuracy of the models improved due to the feature selection techniques utilized in this study.

Author Biography

Edmund Evangelista, Zayed University

Dr. Edmund Evangelista is an Assistant Professor at the College of Technological Innovation at Zayed University (Abu Dhabi Campus). He received his Ph.D. in Information Technology from Saint Paul University Philippines. His primary field of research is Machine Learning and Software Engineering. Before joining Zayed University, he has over eighteen years of experience working in the IT Industry and IT Academia in countries like Oman, Kuwait, United Arab Emirates, and the Philippines. He has held positions as Team Leader, Software Engineer, and Web/Moodle Developer within the IT industry.




How to Cite

Evangelista, E. (2021). A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment. International Journal of Emerging Technologies in Learning (iJET), 16(24), pp. 255–272. https://doi.org/10.3991/ijet.v16i24.26151