Source of Publication
International Journal of Emerging Technologies in Learning (iJET)
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.
International Association of Online Engineering (IAOE)
Computer Sciences | Education
Machine learning, Weka, Predictive model, Ensemble, Student performance prediction, Classification algorithm, Virtual learning
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Evangelista, Edmund, "A Hybrid Machine Learning Framework for Predicting Students’ Performance in Virtual Learning Environment" (2021). All Works. 4749.
Indexed in Scopus
Open Access Type
Gold: This publication is openly available in an open access journal/series