Proceedings of the 5th International Symposium on Social Science (ISSS 2019)

Research on the Influence of Learning Analysis Technology on Teaching Mode Under the Background of Big Data

Authors
Ming Yang
Corresponding Author
Ming Yang
Available Online 18 March 2020.
DOI
10.2991/assehr.k.200312.064How to use a DOI?
Keywords
big data, learning analysis technology, teaching mode
Abstract

With the development of information technology, big data analysis applied in the field of education has become the development trend of teaching today. Learning analysis technology, as an emerging technology, has changed the empirical mode of traditional teaching. It can not only provide students with a high-quality, personalized learning experience, but also improve the teaching methods of educators, and improve the teaching process through information and data analysis. Based on the background of big data, this article explains how to improve the learning analysis technology based on the new situation, reform the teaching model, and highlight the significance of big data, to better serve students and promote the reform and development of the teaching model.

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 5th International Symposium on Social Science (ISSS 2019)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
18 March 2020
ISBN
978-94-6252-930-4
ISSN
2352-5398
DOI
10.2991/assehr.k.200312.064How 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  - Ming Yang
PY  - 2020
DA  - 2020/03/18
TI  - Research on the Influence of Learning Analysis Technology on Teaching Mode Under the Background of Big Data
BT  - Proceedings of the 5th International Symposium on Social Science (ISSS 2019)
PB  - Atlantis Press
SP  - 353
EP  - 356
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.200312.064
DO  - 10.2991/assehr.k.200312.064
ID  - Yang2020
ER  -