关键词: 粗糙集, 条件信息熵, 学生评教, 评教指标权重, Kruskal最大树模糊聚类

Abstract: In order to obtain objective and reasonable results of students’ evaluation of teaching, this paper adopts an intelligent approach based on rough set theory. Rough set method can only process decision table containing given decision attribute. However, during the evaluation of teaching, the decision attribute is usually lost because of lacking objective scales in practical measurement. Pointing at this problem, decision attribute values are acquired from experts’ evaluation data set based on the method of Kruskal maximum tree fuzzy clustering, and the integrated decision table is designed by combining the students’ evaluation data set with the decision attribute data. Objective weight values of all evaluation indexes are obtained under the information entropy method based on rough set theory, and the evaluation is finished for the candidates. Example analysis and contrastive experiments with other existing evaluation methods are given to demonstrate the effectiveness and superiority of the proposed new method.

Key words: rough set theory, conditional information entropy, students’ evaluation of teaching, weight of evaluation index, Kruskal maximum tree fuzzy clustering