Fair assessment of group work by mutual evaluation based on trust network

We propose a method for fair and accurate assessment of group work based on trust networks generated by mutual evaluations. Group work is often used for educational activities in universities since it is an effective way to acquire useful knowledge in a number of practical subjects. One drawback is the difficulty of deciding on final marks. Some students may work quite hard whereas others may rarely participate in the group work, but it is almost impossible for professors/instructors to identify contributions of individual students in detail. In contrast, students in the same group are obvious choices for appropriate evaluators of other members since they have first-hand knowledge of the collaborative work. However, some students may be irresponsible for their ratings and submit disputable evaluations, resulting in inaccurate marks. We introduce a simple mutual evaluation method and generate trust networks expressing the distances between evaluations in this paper. After that, disputable evaluations are excluded and students are marked again. We also examine a grouping strategy to detect irresponsible students more accurately. We demonstrate the effectiveness and limitations of our method using multi-agent simulation. Results show that our method can help with the marking of individual students in a group work.