Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

Autor/inn/en	Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili
Titel	Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques
Quelle	In: Computers & Education, 53 (2009) 3, S.950-965 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0360-1315
DOI	10.1016/j.compedu.2009.05.010
Schlagwörter	Dropouts; Prediction; Teaching Methods; Distance Education; Probability; Evaluation Methods; Dropout Characteristics; Computer Uses in Education; Educational Technology; Classification + Suchen Sie Ihr Suchwort? Drop-out; Drop-outs; Dropout; Early leavers; Schulversagen; Vorhersage; Teaching method; Lehrmethode; Unterrichtsmethode; Distance study; Distance learning; Fernunterricht; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Computernutzung; Unterrichtsmedien; Classification system; Klassifikation; Klassifikationssystem
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. (Contains 2 tables and 11 figures.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10