Design and Application of Innovative Teaching Platform Based on Fog Computing
Jiejian Cai, Baoling Qin, Fenglin Zheng, Sina Li, Yunshi Luo, Jianwei Zhang
Available Online December 2018.
- https://doi.org/10.2991/iceiss-18.2018.56How to use a DOI?
- Cloud Computing; Fog Computing; Fog Model; Distributed Computing; Innovative Teaching Platform
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/iceiss-18.2018.56 ID - Cai2018/12 ER -