Abstract
Student evaluations to measure the teaching effectiveness of instructor's are very fre- quently 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 tech- niques; 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.
Highlights
In recent years, there has been a widespread interest in the student evaluations of teaching (SET) to measure teaching effectiveness of instructors’ and quality of teaching
The relationship between student evaluations and the teaching performance of instructors has been debated for many years
This result is not peculiar to the Management Information Systems (MIS) education since the same evaluation form is used in all departments of Bogazici University
Summary
There has been a widespread interest in the student evaluations of teaching (SET) to measure teaching effectiveness of instructors’ and quality of teaching. Universities and colleges used the result of evaluations to monitor teaching quality and to help teachers improve their teaching effectiveness. In recent years SET results are utilized for other purposes by providing information to different parties in educational institutions (Kulik, 2001). Administrators use ratings in hiring new instructors, in promotion and tenure decisions, in selecting faculty and graduate students for teaching awards, and in assigning teachers to courses. Instructors use SET results to improve their teaching effectiveness and in monitoring the performance of their graduate student assistants. Students use the ratings in selecting courses and selecting teachers for awards
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