skip to main content
10.1145/3453187.3453340acmotherconferencesArticle/Chapter ViewAbstractPublication PagesebimcsConference Proceedingsconference-collections
research-article

Research on Smart Education Service Platform Based on Big Data

Authors Info & Claims
Published:24 March 2021Publication History

ABSTRACT

The big data technology can be applied to build the education service platforms and construct the big data analysis and application system as well as the multi-dimensional perception system. The big data analysis assists in the teaching process and breaks the temporal and spatial restrictions of educational resources, to realize the diversification of educational resources and improve the effectiveness of teaching feedback. This paper proposes a smart education service platform based on big data, which can promote the organic integration of educational communication, educational research, learning activities, teaching affairs administration, and information infrastructures. At the same time, the platform provides smarter, more efficient, and accurate services for teaching.

References

  1. ZHANG Sheng, ZHAO Jue: Practice and Exploration of Visual Design of Wisdom Education Management System-Taking Hunan University of Technology and Business for Example[J] Modern Educational Technology. 2020(8):80--85.Google ScholarGoogle Scholar
  2. SHI Wanli, ZHANG Yuhui: Intelligent education platform design based on big data analysis technology [J] Modern Electronics Technique. May 2020, Vol 43 No 9:150--153.Google ScholarGoogle Scholar
  3. Montgomery D C, Peck E A, Vining G G. Introduction to linear regression analysis[M]. John Wiley & Sons, 2012.Google ScholarGoogle Scholar
  4. Hosmer Jr D W, Lemeshow S, Sturdivant R X. Applied logistic regression[M]. John Wiley & Sons, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  5. Aggarwal C C, Zhai C X. A survey of text classification algorithms[M]//Mining text data. Springer, Boston, MA, 2012: 163--222.Google ScholarGoogle Scholar
  6. Yu S S, Chu S W, Wang C M, et al. Two improved k-means algorithms[J]. Applied Soft Computing, 2018, 68: 747--755.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Abdel-Basset M, Mohamed M, Smarandache F, et al. Neutrosophic association rule mining algorithm for big data analysis[J]. Symmetry, 2018, 10(4): 106.Google ScholarGoogle ScholarCross RefCross Ref
  8. Silge J, Robinson D. Text mining with R: A tidy approach[M]. "O'Reilly Media, Inc.", 2017.Google ScholarGoogle Scholar
  9. Liu Q, Xiang B, Yuan N J, et al. An influence propagation view of pagerank[J]. ACM Transactions on Knowledge Discovery from Data (TKDD), 2017, 11(3): 1--30.Google ScholarGoogle Scholar
  10. Zhong Shaochun: How Artificial Intelligence Supports Educational Revolution [J] China Educational Technology. 2020(3):17--24.Google ScholarGoogle Scholar
  11. CUI Ya-qiang, GAN Qi-hong, WANG Chun-yan: Thoughts on the Construction and Operation Mechanism of Smart Teaching Environment in Colleges and Universities-Taking Sichuan University as an Example [J] Modern Educational Technology. 2020(3):95--100.Google ScholarGoogle Scholar
  12. LIU Bang-qi: The Development Form and Practice Path of Intelligent Education-Talking about the Relationship between Intelligent Education and Smart Education [J] Modern Educational Technology. 2019(10):20--27.Google ScholarGoogle Scholar
  13. Alexander B, Ashford-Rowe K, Barajas-Murphy N, et al. EDUCAUSE horizon report: 2019 higher education edition[R]. Educause, 2019:1--42.Google ScholarGoogle Scholar
  14. Becker S A, Brown M, Dahlstrom E, et al. NMC horizon report: 2018 higher education edition[R]. Louisville, CO:Educause, 2018:1--54.Google ScholarGoogle Scholar
  15. Becker S A, Cummins M, Davis A, et al. NMC horizon report: 2017 higher education edition[R]. The New Media Consortium, 2017:1--54.Google ScholarGoogle Scholar
  16. YU Shengquan, WANG Qi: Analysis of Collaborative Path Development of "AI+Teachers" [J] E-education Research. 2019(4):23--29.Google ScholarGoogle Scholar
  17. ZHONG Shaochun, TANG Yewei: Research on the Orientation and Route of Educational Innovative Development in the Age of Artificial Intelligence [J] E-education Research. 2018(10):15--20.Google ScholarGoogle Scholar

Index Terms

  1. Research on Smart Education Service Platform Based on Big Data

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      EBIMCS '20: Proceedings of the 2020 3rd International Conference on E-Business, Information Management and Computer Science
      December 2020
      718 pages
      ISBN:9781450389099
      DOI:10.1145/3453187

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 24 March 2021

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article
      • Research
      • Refereed limited

      Acceptance Rates

      EBIMCS '20 Paper Acceptance Rate112of566submissions,20%Overall Acceptance Rate143of708submissions,20%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader