A Two-Stage Multi-Agent Based Assessment Approach to Enhance Students’ Learning Motivation Through Negotiated Skills Assessment

Abstract

In this paper we present an Agent-based evaluation approach in a context of Multi-agent simulation learning systems. Our evaluation model is based on a two stage assessment approach: (1) a Distributed skill evaluation combining agents and fuzzy sets theory; and (2) a Negotiation based evaluation of students’ performance during a training simulation or a Problem-solving process in a Computer-assisted learning system. This paper highlights how this approach deals with the problem of subjective evaluation of students, and shows the impact of Negotiated skills evaluation on reducing the students’ rate of dropout. This approach can also compensate the absence of human expert for assessing training results. Applied to training in plant protection, experiments' results showed first the fuzzy sets based assessment to be similar to the domain expert’s assessment and second the negotiated skills assessment to be effective in assessing students’ abilities and sustaining students’ motivation to continue learning. This evaluation approach allows us to address the problem of subjective assessment and overcome some difficulties encountered in traditional measurement models.