Abstract
One of the critical aspects in completing study in a virtual learning environment (VLE) is the student behavior when interacting with the system. However, in real cases, most of the student behavior data have imbalanced label distribution. This imbalanced dataset affects the model performance of machine learning algorithms significantly. This study attempts to examine several resampling methods such as random undersampling (RUS), oversampling with synthetic minority oversampling technique (SMOTE), and hybrid sampling (SMOTEENN) to resolve the imbalanced data issue. Several machine learning (ML) classifiers are employed to evaluate the efficiency of the resampling methods, including Naïve Bayes (NB), Logistic Regression (LR), and Random Forest (RF). The experiment results indicate that the performance of classifiers is improved utilizing more balanced dataset. Furthermore, the Random Forest classifier has accomplished the best result among all other models while using SMOTEENN as a resampling approach.
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Acknowledgements
This work was also supported in part by the Ministry of Science and Technology, Taiwan, under Grant both No. MOST 109-2221-E-468-009-MY2 and No. MOST 110-2218-E-468-001-MBK. This work was also supported in part by Ministry of Education under Grant No. I109MD040. This work was also supported in part by Asia University Hospital under Grant No. 10951020.
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Chen, HC. et al. (2022). Learning Performance Prediction with Imbalanced Virtual Learning Environment Students’ Interactions Data. In: Barolli, L., Yim, K., Chen, HC. (eds) Innovative Mobile and Internet Services in Ubiquitous Computing. IMIS 2021. Lecture Notes in Networks and Systems, vol 279. Springer, Cham. https://doi.org/10.1007/978-3-030-79728-7_33
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