Research on University Education Management Decision-making under the Combination of Experience and Data

DOI

10.2991/aemh-19.2019.7How to use a DOI?

Keywords

Education management, Big data, Mapping-inversion, Data-experience fusion, Decision optimization

Abstract

To achieve the credible purpose of education and teaching management, it is necessary to conduct education management big data mining from the perspective of multidimensional and complex. From the perspective of the actual needs of college education management, this paper introduces the significance of education management big data mining, puts forward the educational data mapping and experience inversion learning mechanism, and discusses the establishment of education management model based on big data mining. Firstly, the differences between educational data mining and traditional data analysis are analyzed. Secondly, how to find education management and decision-making knowledge from educational data is proposed. Finally, an educational management model based on the integration of experience and data is constructed. Research shows that university education and teaching big data mining is reflected in the multi-dimensional and complex characteristics of information. Moreover, education management decision-making based on fusion analysis of data-experience is a trusted decision-making, and it is also a meaningful study of future education management in colleges and universities.

Copyright

© 2019, 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/).

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TY  - CONF
AU  - Lei Zhang
PY  - 2019/10
DA  - 2019/10
TI  - Research on University Education Management Decision-making under the Combination of Experience and Data
BT  - Proceedings of the 2019 International Conference on Advanced Education, Management and Humanities (AEMH 2019)
PB  - Atlantis Press
SP  - 32
EP  - 36
SN  - 2352-5398
UR  - https://doi.org/10.2991/aemh-19.2019.7
DO  - 10.2991/aemh-19.2019.7
ID  - Zhang2019/10
ER  -