Abstract
A university education is widely considered essential to social advancement. Ensuring students pass their courses and graduate on time have thus become issues of concern. This paper proposes a reduced training vector-based support vector machine (RTV-SVM) capable of predicting at-risk and marginal students. It also removes redundant training vectors to reduce the training time and support vectors. To examine the effectiveness of the proposed RTV-SVM, 32,593 university students on seven courses were chosen for performance evaluation. Analysis reveals that the RTV-SVM achieved a training vector reduction of at least 59.7% without altering the margin or accuracy of the classifier. Moreover, the results showed the proposed method to be capable of achieving overall accuracy of 92.2–93.8% and 91.3–93.5% in predicting at-risk and marginal students, respectively. Copyright © 2018 Elsevier Ltd. All rights reserved.
Original language | English |
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Article number | 105584 |
Journal | Computers in Human Behavior |
Volume | 107 |
Early online date | Apr 2020 |
DOIs | |
Publication status | Published - Jun 2020 |
Citation
Chui, K. T., Fung, D. C. L., Lytras, M. D., & Lam, T. M. (2020). Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Computers in Human Behavior, 107. Retrieved from https://doi.org/10.1016/j.chb.2018.06.032Keywords
- Academic performance
- At-risk students
- Event prediction
- Higher education
- Machine learning
- Virtual learning environments