Investigating Student's Temporal Behaviors and Evaluation of Faculty through Educational Data Mining (Research Note)

Educational data mining (EDM) extracts implicit and interesting patterns from large data collections to provide a more effective learning environment. Introducing EDM concepts and techniques, this paper aims to discover existing behavioral pattern of students in course selection and faculty evaluation as well as educators policies in grading. This study has been carried out to determine the correlation between Student Evaluation of Teachers (SET) ratings and their gained scores in University of Tehran, department of Information Technology engineering.Dividing students based on their grades, a weak direct relationship has been demonstrated among weak and good students (0.086, 0.108) meaning that the more students’ grades were, the higher teacher evaluation scores were observed. Insufficient students’ awareness of SET importance and the existence inappropriate questions beyond students’ knowledge in the questionnaire may cause these results. Recognizing effective factors in SET ratings can reveal the strengths and weaknesses of this kind of faculty evaluation and provide the possibility for better planning and obtaining authentic results.