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EAI International Conference, BigIoT-EDU

BigIoT-EDU 2021: Application of Big Data, Blockchain, and Internet of Things for Education Informatization pp 69–78Cite as

Construction of Management Model of Vocational Education Based on Data Analysis

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Abstract

Due to the strong self-organization and self-adaptive ability of DEEP LEARNING network, this paper constructs a prediction model of management deep learning level of vocational education based on DEEP LEARNING network, using nsse-china 2013 questionnaire as the source of data, taking the five comparable indicators between vocational colleges as the input of the network, and the management level of vocational education as the output of the network, and simulating in MATLAB. The experimental results show that the prediction model overcomes the complexity and subjectivity of the traditional evaluation of deep learning vocational education management, has the characteristics of fast convergence speed and high prediction accuracy, and has good applicability.

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Author information

Authors and Affiliations

  1. School of Civil Engineering and Architecture, Chongqing Vocational College of Transportation, Chongqing, 402247, China

    Yunli Dao

Authors
  1. Yunli Dao
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Editor information

Editors and Affiliations

  1. Abdul Wali Khan University Mardan, Mardan, Pakistan

    Mian Ahmad Jan

  2. Abdul Wali Khan University Mardan, Mardan, Pakistan

    Fazlullah Khan

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Dao, Y. (2021). Construction of Management Model of Vocational Education Based on Data Analysis. In: Jan, M.A., Khan, F. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 392. Springer, Cham. https://doi.org/10.1007/978-3-030-87903-7_11

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  • DOI: https://doi.org/10.1007/978-3-030-87903-7_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87902-0

  • Online ISBN: 978-3-030-87903-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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