Journal
COMPUTERS & EDUCATION
Volume 53, Issue 3, Pages 950-965Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compedu.2009.05.010
Keywords
Distance education and telelearning; E-learning; Machine learning; Dropout prediction
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In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to accurately classify some e-learning students, whereas another may succeed, three decision schemes, which combine in different ways the results of the three machine learning techniques, were also tested. The method was examined in terms of overall accuracy, sensitivity and precision and its results were found to be significantly better than those reported in relevant literature. (C) 2009 Elsevier Ltd. All rights reserved.
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