ABSTRACT
ABSTRACT: One of the challenges of educational organization improvement is to manage talented teachers effectively, i.e. to assign the right teacher for given courses. For providing scientific performance evaluation, some factors related to a teacher, such as age, experience, professional, etc. usually play important roles. Therefore, it is crucial to find the underlying associations between teachers’ personal information and their working behaviors. In this paper, data mining technique is employed to improve the faculty evaluation. An algorithm called Quick Apriori, which is based on the classicApriori algorithm, is proposed. The results not only provide the decision rules related to personal characteristics with working performance, but also show that the obtained rules are consonant with existing experience.
