An Intelligent Mechanism for Adaptive Peer User Modeling in Web-Based Environments

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 method was tested on educational e-learning data and the preliminary results that it yielded are promising.