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.
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Giannoukos, I., Lykourentzou, I., Mpardis, G., Nikolopoulos, V., Loumos, V., Kayafas, E. (2010). An Adaptive Mechanism for Author-Reviewer Matching in Online Peer Assessment. In: Wallace, M., Anagnostopoulos, I.E., Mylonas, P., Bielikova, M. (eds) Semantics in Adaptive and Personalized Services. Studies in Computational Intelligence, vol 279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11684-1_7
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DOI: https://doi.org/10.1007/978-3-642-11684-1_7
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