Early and dynamic student achievement prediction in E-learning courses using neural networks

Lykourentzou, I; Giannoukos, I; Mpardis, G; Nikolopoulos, V; Loumos, V

dc.contributor.author	Lykourentzou, I	en
dc.contributor.author	Giannoukos, I	en
dc.contributor.author	Mpardis, G	en
dc.contributor.author	Nikolopoulos, V	en
dc.contributor.author	Loumos, V	en
dc.date.accessioned	2014-03-01T01:30:15Z
dc.date.available	2014-03-01T01:30:15Z
dc.date.issued	2009	en
dc.identifier.issn	1532-2882	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/19519
dc.subject	Student Achievement	en
dc.subject	Neural Network	en
dc.subject.classification	Computer Science, Information Systems	en
dc.subject.classification	Information Science & Library Science	en
dc.subject.other	E-learning	en
dc.subject.other	Feedforward neural networks	en
dc.subject.other	Internet	en
dc.subject.other	Multimedia systems	en
dc.subject.other	Statistical tests	en
dc.subject.other	Students	en
dc.subject.other	Accurate predictions	en
dc.subject.other	Educational services	en
dc.subject.other	Input datum	en
dc.subject.other	Neural-network	en
dc.subject.other	Prediction methods	en
dc.subject.other	Student achievements	en
dc.subject.other	Virtual groups	en
dc.subject.other	Teaching	en
dc.title	Early and dynamic student achievement prediction in E-learning courses using neural networks	en
heal.type	journalArticle	en
heal.identifier.primary	10.1002/asi.20970	en
heal.identifier.secondary	http://dx.doi.org/10.1002/asi.20970	en
heal.language	English	en
heal.publicationDate	2009	en
heal.abstract	The increasing popularity of e-learning has created a need for accurate student achievement prediction mechanisms, allowing instructors to improve the efficiency of their courses by addressing specific needs of their students at an early stage. In this paper, a student achievement prediction method applied to a 10-week introductory level e-learning course is presented. The proposed method uses multiple feed-forward neural networks to dynamically predict students' final achievement and to cluster them in two virtual groups, according to their performance. Multiple-choice test grades were used as the input data set of the networks. This form of test was preferred for its objectivity. Results showed that accurate prediction is possible at an early stage, more specifically at the third week of the 10-week course. In addition, when students were clustered, low misplacement rates demonstrated the adequacy of the approach. The results of the proposed method were compared against those of linear regression and the neural-network approach was found to be more effective in all prediction stages. The proposed methodology is expected to support instructors in providing better educational services as well as customized assistance according to students' predicted level of performance.	en
heal.publisher	JOHN WILEY & SONS INC	en
heal.journalName	Journal of the American Society for Information Science and Technology	en
dc.identifier.doi	10.1002/asi.20970	en
dc.identifier.isi	ISI:000263136200013	en
dc.identifier.volume	60	en
dc.identifier.issue	2	en
dc.identifier.spage	372	en
dc.identifier.epage	380	en