Proceedings of the 2017 International Conference on Management, Education and Social Science (ICMESS 2017)

Research on Evaluation of Morality and Ability of Teachers in Universities Based on the Perspective of Data Mining

Authors
Li Tang, Caiyun Zhou, Li He, Shuhua Zhang
Corresponding Author
Li Tang
Available Online June 2017.
DOI
https://doi.org/10.2991/icmess-17.2017.41How to use a DOI?
Keywords
Support Vector Machine (SVM); Morality of Teachers; Ability of teachers; Data Mining
Abstract
The evaluation of professional morality and ability of teachers is an important part of management in the colleges and universities. Focusing on the evaluation of teachers in Chinese universities, this paper designs an evaluation model, and constructs an evaluation metrics from the perspective of data mining. Furthermore, a new evaluation method for teachers based on Support Vector Machine (SVM) is proposed. The prediction levels of the morality and ability of teachers will be given by SVM. Finally, it gives the relative policies to promote the morality and ability of teachers. It will efficiently improve the quality of teaching and bring benefit to the teaching reform.
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Proceedings
2017 International Conference on Management, Education and Social Science (ICMESS 2017)
Part of series
Advances in Social Science, Education and Humanities Research
Publication Date
June 2017
ISBN
978-94-6252-348-7
ISSN
2352-5398
DOI
https://doi.org/10.2991/icmess-17.2017.41How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Li Tang
AU  - Caiyun Zhou
AU  - Li He
AU  - Shuhua Zhang
PY  - 2017/06
DA  - 2017/06
TI  - Research on Evaluation of Morality and Ability of Teachers in Universities Based on the Perspective of Data Mining
BT  - 2017 International Conference on Management, Education and Social Science (ICMESS 2017)
PB  - Atlantis Press
SP  - 172
EP  - 175
SN  - 2352-5398
UR  - https://doi.org/10.2991/icmess-17.2017.41
DO  - https://doi.org/10.2991/icmess-17.2017.41
ID  - Tang2017/06
ER  -