Data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers

Abstract

Professional development for college teachers is essential and improving teaching performance is a key aspect of this. To evaluate teaching performance in colleges and universities, we use student ratings for each lecturer and course. Investigating the characteristics of college teachers associated with teaching performance ratings can be valuable for students, teachers, universities, and higher education administrators. Previous studies have identified only some of the characteristics associated with teaching performance, and the data on ratings used in these studies was often collected from online rating/comment websites, which may not be reliable. This data-driven study aims to identify discriminative factors of teaching performance ratings for college teachers at a university in Guangdong, China. We collected comprehensive data on college teachers' characteristics and used data on teaching performance ratings obtained from the university's official channel. Our findings can inform policies and practices related to teaching evaluation and professional development in higher education.