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Application of Data Analysis Technology Based on Apriori Algorithm in Ideological and political Education

Published: 30 May 2024 Publication History
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  • Abstract

    Information technology and its application are hot topics in the current major application fields. Firstly, this paper explains the connotation and function of data analysis. Secondly, this paper examines the crucial role of utilizing data analysis technology in ideological and political education, enhances the effectiveness of ideological and political education, and improves the scientific nature of ideological and political education. Thirdly, the fundamental process and model of data analysis technology embedded in ideological and political teaching education are clarified from seven key aspects, including a clear definition of the purpose of data analysis, methods of data collection, processing, and other related aspects. Finally, some practical methods are proposed, including utilizing data to enhance the precision of ideological and political teaching, employing information technology to promote the innovation of ideological and political education, and employing intelligence to improve the humanization of ideological and political teaching. These methods offer an effective reference for the further reform and development of ideological and political courses in the context of the rapid development of the internet in the future.

    References

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    Hain, D., Jurowetzki, R., Lee, S., Zhou, Y. 2023. Machine Learning and Artificial Intelligence for Science, Technology, Innovation Mapping and Forecasting: Review, Synthesis, and Applications. Scientometrics: An International Journal for All Quantitative Aspects of the Science of Science Policy,128(3):1465-1472.
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    1. Application of Data Analysis Technology Based on Apriori Algorithm in Ideological and political Education

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      ICIEAI '23: Proceedings of the 2023 International Conference on Information Education and Artificial Intelligence
      December 2023
      1132 pages
      ISBN:9798400716157
      DOI:10.1145/3660043
      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 the author(s) 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].

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      Association for Computing Machinery

      New York, NY, United States

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      Published: 30 May 2024

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