We propose a fair and accurate 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 to assess group work because students can observe the contributions of other students. However, mutual evaluation presents some potential problems to discuss such as irresponsible evaluations and collusion. Our proposed 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 since too many chances for evaluation burden students. We introduce the iterative method for alternately generating trust networks and calculating cluster-trust values, which represent similarity of evaluations in a cluster network. Using a multi-agent simulation, 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. Thus, our method can provide useful information for assessments to instructors and reduce free-riders’ incentives for cheating behaviors.
|ジャーナル||Transactions of the Japanese Society for Artificial Intelligence|
|出版ステータス||Published - 2016|
ASJC Scopus subject areas
- Artificial Intelligence