Proceedings of the 2020 International Conference on Social Science, Economics and Education Research (SSEER 2020)

Deep Mining of Teaching Data Under Online Teaching Quality Control

Authors
Ying Gao, Wendong Wang
Corresponding Author
Wendong Wang
Available Online 1 August 2020.
DOI
10.2991/assehr.k.200801.005How to use a DOI?
Keywords
online teaching, platform, teaching data
Abstract

In response to the national call for “no extension of education, no suspension of classes”, online teaching in our school is fully launched as scheduled. In order to ensure the quality of undergraduate online classroom teaching, from the point of view of teaching quality monitoring, through investigation, taking the teaching data of Yan’an University as an example, taking a small part to see the whole, this paper analyzes the existing problems in online teaching in colleges and universities in China at present, and puts forward some improvement methods and suggestions.

Copyright
© 2020, 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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Volume Title
Proceedings of the 2020 International Conference on Social Science, Economics and Education Research (SSEER 2020)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
1 August 2020
ISBN
10.2991/assehr.k.200801.005
ISSN
2352-5398
DOI
10.2991/assehr.k.200801.005How to use a DOI?
Copyright
© 2020, 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  - Ying Gao
AU  - Wendong Wang
PY  - 2020
DA  - 2020/08/01
TI  - Deep Mining of Teaching Data Under Online Teaching Quality Control
BT  - Proceedings of the 2020 International Conference on Social Science, Economics and Education Research (SSEER 2020)
PB  - Atlantis Press
SP  - 24
EP  - 27
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200801.005
DO  - 10.2991/assehr.k.200801.005
ID  - Gao2020
ER  -