An Empirical Study on Student Evaluations of Teaching Based on Data Mining
Wei Zhang 1
,  
Shiming Qin 1  
,  
Hanjun Jin 1
,  
Jing Deng 1
,  
 
 
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1
Central China Normal University, China
2
Nanyang Technological University, Singapore
CORRESPONDING AUTHOR
Shiming Qin   

National Engineering Research Center for E-Learning, Central China Normal University, Wuhan 430079, China
Online publish date: 2017-08-23
Publish date: 2017-08-23
 
EURASIA J. Math., Sci Tech. Ed 2017;13(8):5837–5845
KEYWORDS
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.
 
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