Student evaluations to measure the teaching effectiveness of instructor's are very frequently applied in higher education for many years. This study investigates the factors associated with the assessment of instructors teaching performance using two different data mining techniques; stepwise regression and decision trees. The data collected anonymously from students' evaluations of Management Information Systems department at Bogazici University. Additionally, variables related to other instructor and course characteristics are also included in the study. The results show that, a factor summarizing the instructor related questions in the evaluation form, the employment status of the instructor, the workload of the course, the attendance of the students, and the percentage of the students filling the form are significant dimensions of instructor's teaching performance.
The aim of the paper is to present the characteristics of certain dynamic programming strategies on the decision tree hidden behind the optimizing problems and thus to offer such a clear tool for their study and classification which can help in the comprehension of the essence of this programming technique.