Author First name, Last name, Institution

Edmund Evangelista, Zayed University

ORCID Identifiers

0000-0002-8050-1360

Document Type

Article

Source of Publication

International Journal of Emerging Technologies in Learning (iJET)

Publication Date

12-21-2021

Abstract

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.

Publisher

International Association of Online Engineering (IAOE)

Volume

16

Issue

24

Disciplines

Computer Sciences | Education

Keywords

Machine learning, Weka, Predictive model, Ensemble, Student performance prediction, Classification algorithm, Virtual learning

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed in Scopus

no

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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