An Empirical Study on Student Evaluations of Teaching Based on Data Mining

Abstract

Under the influence of big data, many fields have undergone tremendous changes. In the field of education, the data still contains a wealth of practical value, but the data mining and knowledge discovery is not enough, especially in the application of student evaluations of teaching (SET). In study, the K-means algorithm is used to cluster the data of three main teaching evaluation indexes (TEI) including individual background, course content, teaching method into high satisfaction degree (HSD), middle satisfaction degree (MSD), and low satisfaction degree (LSD). The logistic regression results showed that gender was a significant factor in students' evaluation of teachers and that there were potential connections between teaching evaluation and teachers' gender, age, and teaching content. In addition, the research shows that the effect of satisfaction degree on students' academic achievement is limited. The findings from this empirical study present a better understanding of reform of SET in higher education.