doi: 10.18178/ijiet.2025.15.2.2243
Enhancing Student Performance Prediction in e-Learning Ecosystems Using Machine Learning Techniques
Fatima Ezzahraa EL Habti1,*
,
Mustafa Hiri1 ,
Mohamed Chrayah2 ,
Abdelhamid Bouzidi1 ,
Noura Aknin1
- 1TIMS Laboratory, Faculty of sciences Tetouan, Abdelmalek Essaadi University Morocco, Morocco
- 2TIMS Laboratory, ENSA Tetouan, Abdelmalek Essaadi University Morocco, Morocco
- Manuscript receivedSeptember 28, 2024
- revisedNovember 1, 2024
- acceptedNovember 22, 2024
- publishedFebruary 14, 2025
Abstract
Accurately predicting student performance in e-learning environments is a significant challenge that is essential for personalizing education and enhancing learning outcomes. This study examines the effectiveness of machine learning techniques in forecasting learner success within e-learning ecosystems, using the Open University Learning Analytics Dataset (OULAD). We conducted a comparative analysis of four machine learning algorithms—Random Forest, Logistic Regression, Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA)—employing comprehensive data pre-processing and feature engineering methods. The Random Forest algorithm outperformed the others, achieving a 91% accuracy rate in classifying student outcomes into “Distinction,” “Pass,” and “Fail” categories. Despite this high accuracy, differentiating between certain performance classes, especially “Distinction” and “Fail,” remained challenging, highlighting the complexity of student performance metrics in online learning contexts. These results demonstrate the potential of machine learning, particularly the Random Forest algorithm, as a valuable tool for enhancing predictive analytics in e-learning systems. The study contributes to the optimization of educational technologies by indicating that refined predictive models can lead to more effective, data-driven interventions and personalized learning pathways.
Keywords
- machine learning
- predictive analysis
- e-learning
- student performance
- learning analytics
- random forest algorithm
How to Cite
Fatima Ezzahraa EL Habti, Mustafa Hiri, Mohamed Chrayah, Abdelhamid Bouzidi, and Noura Aknin, "Enhancing Student Performance Prediction in e-Learning Ecosystems Using Machine Learning Techniques," International Journal of Information and Education Technology, vol. 15, no. 2, pp. 301-311, 2025. https://doi.org/10.18178/ijiet.2025.15.2.2243
Copyright & License
Copyright © 2025 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).