<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Do a good job in the way of college students’ network political innovation based on big data thinking, so that it can play a stronger advantage and energy in college education. Carrying out innovative exploration of college students’ curriculum education based on big data thinking can continuously deepen the theoretical research significance of Internet political education. It can also put forward suggestions for better practice of network ideological and political education in colleges and universities, which has the dual significance of theoretical construction and practical guidance. Therefore, the MCA-sampling model is designed in this paper. According to the calculation of the sampling model, the opportunity for online ideological and political data literacy is 11%, and the challenge is 89%. This is because any flaws at any level will bring a severe test to the calculation of the effectiveness of online political teaching methods for students, which greatly increases the difficulty of online political educators. Through the horizontal comparison, it can be seen that the acceptance theory focusing on “receiver-centered” is the most innovative. Its innovativeness is 83%. The probability of timely method innovation is as high as 89%. The most unstable aspect of innovation probability is the root cause. Its probability of innovation is at least 24%.</p>
</abstract>

Do a good job in the way of college students’ network political innovation based on big data thinking, so that it can play a stronger advantage and energy in college education. Carrying out innovative exploration of college students’ curriculum education based on big data thinking can continuously deepen the theoretical research significance of Internet political education. It can also put forward suggestions for better practice of network ideological and political education in colleges and universities, which has the dual significance of theoretical construction and practical guidance. Therefore, the MCA-sampling model is designed in this paper. According to the calculation of the sampling model, the opportunity for online ideological and political data literacy is 11%, and the challenge is 89%. This is because any flaws at any level will bring a severe test to the calculation of the effectiveness of online political teaching methods for students, which greatly increases the difficulty of online political educators. Through the horizontal comparison, it can be seen that the acceptance theory focusing on “receiver-centered” is the most innovative. Its innovativeness is 83%. The probability of timely method innovation is as high as 89%. The most unstable aspect of innovation probability is the root cause. Its probability of innovation is at least 24%.

Combining big data for college students’ network ideological and political innovation education

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Do a good job in the way of college students’ network political innovation based on big data thinking, so that it can play a stronger advantage and energy in...

Combining big data for college students’ network ideological and political innovation education

Publié en ligne: 08 mai 2023

Pages: -

Reçu: 09 mai 2022

Accepté: 03 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00241

Mots clés
Big Data, College Students, Education Field, Monte Carlo Algorithm, MCA-Sampling Model

© 2023 Ruijun Ban, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Combining big data for college students’ network ideological and political innovation education

Publié en ligne: 08 mai 2023

Pages: -

Reçu: 09 mai 2022

Accepté: 03 nov. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00241

Mots clésBig Data, College Students, Education Field, Monte Carlo Algorithm, MCA-Sampling Model

© 2023 Ruijun Ban, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Mots clés
Big Data, College Students, Education Field, Monte Carlo Algorithm, MCA-Sampling Model