摘要： Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception. In this paper, four different classi?cation techniques—decision tree algorithms, support vector machines, arti?cial neural networks, and discriminant analysis—are used to build classi?er models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and speci?city performance metrics. Although all the classi?er models show comparably high classi?cation performances, C5.0 classi?er is the best with respect to accuracy, precision, and speci?city. In addition, an analysis of the variable importance for each classi?er model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The ?ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ?ndings may be used to improve the measurement instruments.