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

<p>In the era of big data, different countries and regions use big data in the field of education. This paper constructs a personalized teaching model for Civics based on big data text-mining technology. Combined with the DIKW pyramid composition structure analysis, it shows how the data can be used to guide the process of teaching decisions. The frequency of Civics keywords in the teaching classroom and the key teaching content in the Civics classroom are determined using the word frequency analysis method. Similarity and KNN calculations are used to classify the students’ civics knowledge level, and personalized teaching plans are developed for their level of knowledge. The application of personalized teaching in Civics is practiced from the aspects of tracking students’ knowledge status and enhancing personalized teaching effects. The results show that the average final grade of class A in the experimental group is 102.37, and the average grade of class B in the control group is 97.37 in the final exam. Class A students have an average grade that is 5 points higher than that of class B. The average grade of students in class A is 5 points higher than that of class B. The two groups are tested using independent t-tests. The results of the two groups were subjected to independent t-test, F=7.047, p=0.00, 8&lt;0.05, variance is not uniform. The t-test results show Sig=0.048&lt;0.05, which indicates that there is a significant difference between the final grades of the experimental and control groups.</p>
</abstract>

In the era of big data, different countries and regions use big data in the field of education. This paper constructs a personalized teaching model for Civics based on big data text-mining technology. Combined with the DIKW pyramid composition structure analysis, it shows how the data can be used to guide the process of teaching decisions. The frequency of Civics keywords in the teaching classroom and the key teaching content in the Civics classroom are determined using the word frequency analysis method. Similarity and KNN calculations are used to classify the students’ civics knowledge level, and personalized teaching plans are developed for their level of knowledge. The application of personalized teaching in Civics is practiced from the aspects of tracking students’ knowledge status and enhancing personalized teaching effects. The results show that the average final grade of class A in the experimental group is 102.37, and the average grade of class B in the control group is 97.37 in the final exam. Class A students have an average grade that is 5 points higher than that of class B. The average grade of students in class A is 5 points higher than that of class B. The two groups are tested using independent t-tests. The results of the two groups were subjected to independent t-test, F=7.047, p=0.00, 8<0.05, variance is not uniform. The t-test results show Sig=0.048<0.05, which indicates that there is a significant difference between the final grades of the experimental and control groups.

Innovation of Civic Education Path in Colleges and Universities under the Integration of Big Data Technology

Applied Mathematics and Nonlinear Sciences

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

In the era of big data, different countries and regions use big data in the field of education. This paper constructs a personalized teaching model for Civics...

Innovation of Civic Education Path in Colleges and Universities under the Integration of Big Data Technology

Online veröffentlicht: 20. Dez. 2023

Seitenbereich: -

Eingereicht: 16. Feb. 2023

Akzeptiert: 30. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01577

Schlüsselwörter
Text mining technology, Word frequency analysis, Similarity, KNN algorithm, Civic education

© 2023 Xinjie Chen et al., published by Sciendo

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

Innovation of Civic Education Path in Colleges and Universities under the Integration of Big Data Technology

Online veröffentlicht: 20. Dez. 2023

Seitenbereich: -

Eingereicht: 16. Feb. 2023

Akzeptiert: 30. Juni 2023

DOI: https://doi.org/10.2478/amns.2023.2.01577

SchlüsselwörterText mining technology, Word frequency analysis, Similarity, KNN algorithm, Civic education

© 2023 Xinjie Chen et al., published by Sciendo

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

Schlüsselwörter
Text mining technology, Word frequency analysis, Similarity, KNN algorithm, Civic education