Fair assessment of group work by mutual evaluation with irresponsible and collusive students using trust networks

We propose a fair peer assessment method for group work using a multi-agent trust network. Although group work is an effective educational method, accurately assessing individual students is not easy. Mutual evaluation is often used for such assessment, but often presents some potential problems such as irresponsible evaluations and collusion. Our method identifies and excludes such cheating and unfair ratings on the basis of trust networks that are often used to evaluate sellers in e-market places by using customers’ ratings. We assume a group-work course in a semester in which students mutually evaluate other group members a few (three to five) times. We introduce the iterative method for alternately generating trust networks using cluster-trust values, which represent similarity of evaluations in a cluster network. We experimentally show that our method can find the irresponsible students and collusive groups and considerably improve accuracy of final marks with only a few chances for mutual evaluations, and thereby, can provide useful information for assessments to instructors.