Fuzzy Comprehensive Evaluation Model on University Teaching Quality

In view of fuzziness of university teaching quality evaluation, AHP-based multi-level fuzzy comprehensive evaluation model become the advantage model for university teaching quality assessment. However, membership conversion way at present has a problem of redundancy, that is, the redundant data which has no value to objective classification in memberships of indicators is used to calculate the objective membership. In order to obtain the membership conversion method which isn't affected by interference of redundant data, we use entropy-based data mining method and define the distinguished weight through mining the knowledge information of objective classification hidden in membership of indicators. In this way, we abstract the effective value by mining the redundancy. Then we can calculate the membership of the objective by comparable value which gets from the effective value. Thus, we construct an improved fuzzy comprehensive evaluation model for university teaching quality evaluation. We take assessment of architecture major in an university to identify the feasible and effective of this method.