The combination application of Civic Education and Big Data Internet Technology based on the background of college education reform

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Abstract

To explore the combined application of Civic Education and big data Internet technology in the context of college education reform. In this paper, based on big data Internet technology, a mining analysis algorithm-K-means clustering algorithm is proposed, and after combining Civic Education and K-means algorithm, the ideological evaluation indexes of Civic Education application, i.e., Civic Education course construction basis, Civic Education course structure configuration, Civic Education course implementation process and Civic Education course gaining Effectiveness of four indicators examples as experiments, as a way to verify the data mining analysis ability of K-means clustering algorithm. The experimental results show that for the four index instances of Civic Education and K-means clustering algorithm, the C-level and above evaluation of Civic Education curriculum construction foundation is 75.42%, the C-level and above evaluation of Civic Education curriculum structure configuration is 81.78%, the C-level and above evaluation of Civic Education curriculum implementation process is 76.78%, and the C-level and above evaluation of Civic Education curriculum obtaining effect is 92.68%, among which A-level evaluation even reached 48.89%. This shows that the combination of Civic Education and Big Data Internet technology can better explore the Civic elements in Civic Education; moreover, it can provide guiding reform direction for Civic Education and add bricks to promote the Civic thoughts of college students. This also expands the application field of big data Internet technology so that more research fields can enjoy the dividends brought by technology development.

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APA

Qiao, Y. (2024). The combination application of Civic Education and Big Data Internet Technology based on the background of college education reform. Applied Mathematics and Nonlinear Sciences, 9(1). https://doi.org/10.2478/amns.2023.2.00168

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