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

Junxiao Liu; Tianyu Gao; Shu Liang; Kecheng Chen; Junwei Zeng; Fengshi Jing; Jiandong Zhou

doi:10.1109/ICEIT57125.2023.10107897

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

Junxiao Liu (Co-first Author)
, Tianyu Gao (Co-first Author)
, Shu Liang
, Kecheng Chen
, Junwei Zeng
, Fengshi Jing^*
, Jiandong Zhou^*

^*Corresponding author for this work

Department of Data Science

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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. © 2023 IEEE.

Original language	English
Title of host publication	2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT)
Publisher	IEEE
Pages	223-227
ISBN (Electronic)	9781665475518, 978-1-6654-7550-1
ISBN (Print)	978-1-6654-7552-5
DOIs	https://doi.org/10.1109/ICEIT57125.2023.10107897
Publication status	Published - 2023
Event	12th IEEE International Conference on Educational and Information Technology (ICEIT 2023) - Chongqing, China Duration: 16 Mar 2023 → 18 Mar 2023

Publication series

Name	IEEE International Conference on Educational and Information Technology, ICEIT

Conference

Conference	12th IEEE International Conference on Educational and Information Technology (ICEIT 2023)
Place	China
City	Chongqing
Period	16/03/23 → 18/03/23

Funding

Funding: This study was partially supported by a Youth Issues "Research on Policies to Enhance Sense of the Chinese colleges' teachers Well-being, Achievement and Honor in the New Era", Grant No. CIA180274, National Social Science Foundation of China. This study was also partially supported Young College Teachers’ Teaching Ability Promotion Program of School of Physical Education, Jinan University.

Research Keywords

computational education
data science in education
teachers' professional development
teaching performance
teaching ratings

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10.1109/ICEIT57125.2023.10107897

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Liu, J., Gao, T., Liang, S., Chen, K., Zeng, J., Jing, F., & Zhou, J. (2023). Data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers. In 2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT) (pp. 223-227). (IEEE International Conference on Educational and Information Technology, ICEIT ). IEEE. https://doi.org/10.1109/ICEIT57125.2023.10107897

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title = "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. {\textcopyright} 2023 IEEE.",

keywords = "computational education, data science in education, teachers' professional development, teaching performance, teaching ratings",

author = "Junxiao Liu and Tianyu Gao and Shu Liang and Kecheng Chen and Junwei Zeng and Fengshi Jing and Jiandong Zhou",

year = "2023",

doi = "10.1109/ICEIT57125.2023.10107897",

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publisher = "IEEE",

pages = "223--227",

booktitle = "2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT)",

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note = "12th IEEE International Conference on Educational and Information Technology (ICEIT 2023) ; Conference date: 16-03-2023 Through 18-03-2023",

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Liu, J, Gao, T, Liang, S, Chen, K, Zeng, J, Jing, F & Zhou, J 2023, Data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers. in 2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT). IEEE International Conference on Educational and Information Technology, ICEIT , IEEE, pp. 223-227, 12th IEEE International Conference on Educational and Information Technology (ICEIT 2023), Chongqing, China, 16/03/23. https://doi.org/10.1109/ICEIT57125.2023.10107897

Data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers. / Liu, Junxiao (Co-first Author); Gao, Tianyu (Co-first Author); Liang, Shu et al.
2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT). IEEE, 2023. p. 223-227 (IEEE International Conference on Educational and Information Technology, ICEIT ).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers

AU - Liu, Junxiao

AU - Gao, Tianyu

AU - Liang, Shu

AU - Chen, Kecheng

AU - Zeng, Junwei

AU - Jing, Fengshi

AU - Zhou, Jiandong

PY - 2023

Y1 - 2023

N2 - 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. © 2023 IEEE.

AB - 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. © 2023 IEEE.

KW - computational education

KW - data science in education

KW - teachers' professional development

KW - teaching performance

KW - teaching ratings

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M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 978-1-6654-7552-5

T3 - IEEE International Conference on Educational and Information Technology, ICEIT

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PB - IEEE

T2 - 12th IEEE International Conference on Educational and Information Technology (ICEIT 2023)

Y2 - 16 March 2023 through 18 March 2023

ER -

Liu J, Gao T, Liang S, Chen K, Zeng J, Jing F et al. Data-Driven Discriminable Factors Analytics of Teaching Performance Ratings for College Teachers. In 2023 IEEE 12th International Conference on Educational and Information Technology (ICEIT). IEEE. 2023. p. 223-227. (IEEE International Conference on Educational and Information Technology, ICEIT ). Epub 2023 Apr 28. doi: 10.1109/ICEIT57125.2023.10107897

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

Abstract

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Research Keywords

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