An adaptive mechanism for author-reviewer matching in online peer assessment

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

dc.contributor.author	Giannoukos, I	en
dc.contributor.author	Lykourentzou, I	en
dc.contributor.author	Mpardis, G	en
dc.contributor.author	Nikolopoulos, V	en
dc.contributor.author	Loumos, V	en
dc.contributor.author	Kayafas, E	en
dc.date.accessioned	2014-03-01T01:32:37Z
dc.date.available	2014-03-01T01:32:37Z
dc.date.issued	2010	en
dc.identifier.issn	1860949X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20196
dc.subject	Machine learning	en
dc.subject	Peer assessment	en
dc.subject	User matching	en
dc.title	An adaptive mechanism for author-reviewer matching in online peer assessment	en
heal.type	journalArticle	en
heal.identifier.primary	10.1007/978-3-642-11684-1_7	en
heal.identifier.secondary	http://dx.doi.org/10.1007/978-3-642-11684-1_7	en
heal.publicationDate	2010	en
heal.abstract	Peer assessment techniques are an effective means to take advantage of the knowledge that exists in web-based peer environments. Through these techniques, participants act both as authors and reviewers over each other's work. However, as web-based cooperating environments continuously grow in popularity, there is a need to develop intelligent mechanisms that will retrieve the optimal group of reviewers to comment on the work of each author, with a view to increasing the usefulness that these comments will have on the author's final result. This paper introduces a novel technique that incorporates feed forward neural networks to determine the optimal reviewers for a specific author during a peer assessment procedure. The proposed method seeks to match author to reviewer profiles based on feedback regarding the usefulness of reviewer comments as it was perceived by the author. The proposed mechanism is expected to improve the peer assessment procedure, by making it adaptive to individual user characteristics, increasing the quality of the projects of a group overall and speeding up the peer assessment procedure. The method was tested on educational data derived from an e-learning course and the preliminary results that it yielded are promising. © 2010 Springer-Verlag Berlin Heidelberg.	en
heal.journalName	Studies in Computational Intelligence	en
dc.identifier.doi	10.1007/978-3-642-11684-1_7	en
dc.identifier.volume	279	en
dc.identifier.spage	109	en
dc.identifier.epage	126	en