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

Lediana Shala Riza () and Lejla Abazi Bexheti ()
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Lediana Shala Riza: South East European University
Lejla Abazi Bexheti: South East European University

A chapter in Navigating Economic Uncertainty - Vol. 2, 2025, pp 257-272 from Springer

Abstract: 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.

Keywords: Student; Engagement; Prediction; VLE; ML; Class imbalance (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-73510-3_16

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DOI: 10.1007/978-3-031-73510-3_16

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