A Study in Negotiation-based Peer Assessment: Natural Language Applied in Assessment Representation

Abstract

This study presents a conceptual framework for providing intelligent supports through agent negotiation, fuzzy constraints and natural language processing to enhance the effectiveness of peer assessment. By using fuzzy constraints, it not only provides a flexible marking scheme to deal with the imprecision and uncertainty for the representation of assessment. Additionally, a fuzzy constraint-based negotiation mechanism is employed to coordinate the cognitive differences between students. Through iterative agent negotiation, students can reconcile the differences and reach an agreement on the assessment results. Owing to the difficulty in reading fuzzy sets of assessment results we incorporate the technique of linguistic approximation to translate fuzzy sets into natural language to facilitate students to understand assessment feedback. Experimental results indicated that students were able to acquire more meaningful and readable feedback to reflect upon and revise their work.