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

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.