Teaching Quality Monitoring of Higher Education Based on Data Mining
- DOI
- 10.2991/saeme-18.2018.93How to use a DOI?
- Keywords
- Higher Education, teaching quality, data mining, quality monitoring
- Abstract
In order to improve the teaching quality of higher education and improve the quality assurance system of higher education, based on the Internet + and under the background of big data, data mining technology is applied and a method for monitoring the teaching quality of higher vocational education is established. A variety of methods are used to clean up the monitoring data, get high-quality monitoring data, and construct the content system of school teaching quality monitoring. Then, according to the mode of "teaching input - teaching process - learning result", the data items which have high correlation with the quality of teaching are used as the core index of teaching quality, and the quality monitoring model of higher education is constructed from the core index of teaching quality. Moreover, the quality monitoring index of higher education is constructed in accordance with the weight of various data. Finally, a normal monitoring system for teaching quality of higher education is realized. The research shows that the teaching quality monitoring system proposed can effectively monitor the quality of teaching. The results can be used to amend the existing teaching quality evaluation indicators, and improve and enrich the five-in-one national higher education quality assurance system
- Copyright
- © 2018, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Xiaobing Jiang PY - 2018/06 DA - 2018/06 TI - Teaching Quality Monitoring of Higher Education Based on Data Mining BT - Proceedings of the 2018 International Conference on Sports, Arts, Education and Management Engineering (SAEME 2018) PB - Atlantis Press SP - 498 EP - 505 SN - 2352-5398 UR - https://doi.org/10.2991/saeme-18.2018.93 DO - 10.2991/saeme-18.2018.93 ID - Jiang2018/06 ER -