Proceedings of the 2016 3rd International Conference on Management, Education Technology and Sports Science (METSS 2016)

Research on the application of data mining technology in the teaching quality evaluation in Colleges

Authors
Huan-Bin Wang, Ma Tao, Wen-Jie Liu
Corresponding Author
Huan-Bin Wang
Available Online November 2016.
DOI
https://doi.org/10.2991/metss-16.2016.48How to use a DOI?
Keywords
data mining, quality evaluation, teaching management.
Abstract
To improve the teaching quality and the quality of talents training is the basic goal of the reform of higher education and teaching.Scientific evaluation and monitoring is an important means to achieve this goal.The application of data mining technology in the teaching quality evaluation system has become the focus of the research on teaching quality evaluation in Colleges.Firstly, this paper analyzes the advantages of data mining technology in teaching quality evaluation. The concept and concrete steps of data mining are studied. Finally, the data extraction process and the composition of the data mining module in the teaching quality evaluation data mining are analyzed.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 3rd International Conference on Management, Education Technology and Sports Science (METSS 2016)
Part of series
Advances in Economics, Business and Management Research
Publication Date
November 2016
ISBN
978-94-6252-248-0
ISSN
2352-5428
DOI
https://doi.org/10.2991/metss-16.2016.48How 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  - Huan-Bin Wang
AU  - Ma Tao
AU  - Wen-Jie Liu
PY  - 2016/11
DA  - 2016/11
TI  - Research on the application of data mining technology in the teaching quality evaluation in Colleges
BT  - 2016 3rd International Conference on Management, Education Technology and Sports Science (METSS 2016)
PB  - Atlantis Press
SN  - 2352-5428
UR  - https://doi.org/10.2991/metss-16.2016.48
DO  - https://doi.org/10.2991/metss-16.2016.48
ID  - Wang2016/11
ER  -