Design and Application of Innovative Teaching Platform Based on Fog Computing

DOI

10.2991/iceiss-18.2018.56How to use a DOI?

Keywords

Cloud Computing; Fog Computing; Fog Model; Distributed Computing; Innovative Teaching Platform

Abstract

Fog computing is a new type of calculation model in recent years, which shares part of the pressure of cloud computing storage, calculation and processing. Compared with the traditional teaching methods, this paper proposes to use fog computing and big data technology features to build an innovative teaching platform system and the fog model based on fog computing, which can reduce network broadband pressure, reduce the burden on cloud servers, improve comprehensive computing ability, and reduce delay. In addition, it can enhance the exchange of information between teachers and students, guarantee the full range of service levels between teachers and students, and mainly solve the problem of network congestion caused by big data teaching resources. The platform design mainly realizes the functions of resource sharing, teacher-student interaction, effect feedback, intelligent management, etc. Besides, it also can intelligently analyze student behavior, intelligently predict students' favorite courses recommend relevant courses for students to choose, and reform the teaching methods and management modes to improve the interaction between teaching and learning, and improve students' enthusiasm and efficiency in learning.

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/).

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TY  - CONF
AU  - Jiejian Cai
AU  - Baoling Qin
AU  - Fenglin Zheng
AU  - Sina Li
AU  - Yunshi Luo
AU  - Jianwei Zhang
PY  - 2018/12
DA  - 2018/12
TI  - Design and Application of Innovative Teaching Platform Based on Fog Computing
BT  - Proceedings of the 2018 2nd International Conference on Education Innovation and Social Science (ICEISS 2018)
PB  - Atlantis Press
SP  - 227
EP  - 231
SN  - 2352-5398
UR  - https://doi.org/10.2991/iceiss-18.2018.56
DO  - 10.2991/iceiss-18.2018.56
ID  - Cai2018/12
ER  -