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Predicting Student Engagement in Virtual Learning Environments Using ML Approaches with Data Balancing Techniques

In: Navigating Economic Uncertainty - Vol. 2

Author

Listed:
  • Lediana Shala Riza

    (South East European University)

  • Lejla Abazi Bexheti

    (South East European University)

Abstract

The objective of this work is to develop an improved machine learning (ML) model to predict low-engagement students in virtual learning environments (VLEs). This model will address classification performance issues on imbalanced class data in a dataset of VLE students. To enhance the classification capabilities of the used data mining methods, this study employs the synthetic minority oversampling technique (SMOTE) approach. The study utilizes many predictive models, including logistic regression, decision tree, K-nearest neighbor, Naïve Bayes classifier, support vector machines, and XGBoost. In this work, we looked at the effects of data resampling by employing the SMOTE data balancing technique. When classifying class data from an unbalanced dataset, the ML classification algorithms are expected to perform more accurately, precisely, and sensitively when the class balancing techniques are applied.

Suggested Citation

  • Lediana Shala Riza & Lejla Abazi Bexheti, 2025. "Predicting Student Engagement in Virtual Learning Environments Using ML Approaches with Data Balancing Techniques," Springer Proceedings in Business and Economics, in: Hyrije Abazi-Alili & Abdylmenaf Bexheti & Veland Ramadani & Carmem Leal & Carlos Peixeira Marques (ed.), Navigating Economic Uncertainty - Vol. 2, pages 257-272, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-73510-3_16
    DOI: 10.1007/978-3-031-73510-3_16
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