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Innovation of Ideological and Political Education Model in the Context of Big Data

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1282))

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Abstract

Times are developing, technology is innovating, and big data thinking and methods promote the progress of human civilization, triggering all-round deep thinking and change in society. In the age of Internet communication, ideological and political education is currently confronting new chances and threats. It must advance with the times, reflect the times, grasp the laws, and be creative. Combined with the analysis of the innovation of ideological and political education course mode for college students in the Age of Big Data, the paper proposes cognition, work and assessment approaches in favor of curricular innovation based on cloud media technology, agent technology and Internet of Things technology, and comes up with a model to innovate student ideology education course.

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Author information

Authors and Affiliations

  1. School of Management Science and Engineering, Shandong Technology and Business University, Shandong, China

    Xiangzhen Pan

Authors
  1. Xiangzhen Pan
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Correspondence to Xiangzhen Pan .

Editor information

Editors and Affiliations

  1. David Goldman Informatics Centre, University of Sunderland, Sunderland, UK

    John MacIntyre

  2. University of Shanghai for Science and Technology, Shanghai, China

    Jinghua Zhao

  3. Shenzhen University, Shenzen, Guangdong, China

    Xiaomeng Ma

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Pan, X. (2021). Innovation of Ideological and Political Education Model in the Context of Big Data. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_47

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