Predicting at-risk university students in a virtual learning environment via a machine learning algorithm

Kwok Tai CHUI, Dennis Chun Lok FUNG, Miltiadis D. LYTRAS, Tin Miu LAM

Research output: Contribution to journalArticlespeer-review

89 Citations (Scopus)

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 languageEnglish
Article number105584
JournalComputers in Human Behavior
Volume107
Early online dateApr 2020
DOIs
Publication statusPublished - 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.2018.06.032

Keywords

  • Academic performance
  • At-risk students
  • Event prediction
  • Higher education
  • Machine learning
  • Virtual learning environments

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