Dropout prediction in e-learning courses through the combination of machine learning techniques

Authors:
Ioanna Lykourentzou;Ioannis Giannoukos;Vassilis Nikolopoulos;George Mpardis;Vassili Loumos
Affiliations:
School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece;School of Electrical and Computer Engineering, National Technical University of Athens, Zographou Campus, 15773 Athens, Greece
Venue:
Computers & Education
Year:
2009

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Abstract

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.