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<p>Addressing the issues of a single form, insufficient targeting, a lack of synergy, and the inability to create a customized collaborative education process in the current college Civic Education curriculum setting, This paper develops a recommendation system for a college Civic Education curriculum based on enhanced collaborative filtering technology. It adopts an enhanced algorithm of cooperative filtering based on the combination, and by having to introduce a gradual forgetting curve relying on the time-sensitive change of user interest, it more effectively resolves. The standard algorithm is outdated, inefficient, and not very adaptable. A similar classification method for college Civics courses is then constructed after data mining is performed to filter out the data noise present in the data set. Via empirical validation, it is discovered that the optimization method obtains an average accuracy of 78.83% when the amount of cycles is 40. When the number of cycles is 40, the recall rate of the optimized algorithm is the highest, and the recall rate is 78%. In terms of recommendation popularity comparison, the optimization algorithm popularity starts to be higher than the traditional algorithm when the number of cycles exceeds 80. Therefore, the system has excellent practicality and resilience, and it operates steadily, which is of positive significance for creating an atmosphere of collaborative and win-win university thinking and political education for teachers and students with various forms and individual innovation.</p>
</abstract>

Addressing the issues of a single form, insufficient targeting, a lack of synergy, and the inability to create a customized collaborative education process in the current college Civic Education curriculum setting, This paper develops a recommendation system for a college Civic Education curriculum based on enhanced collaborative filtering technology. It adopts an enhanced algorithm of cooperative filtering based on the combination, and by having to introduce a gradual forgetting curve relying on the time-sensitive change of user interest, it more effectively resolves. The standard algorithm is outdated, inefficient, and not very adaptable. A similar classification method for college Civics courses is then constructed after data mining is performed to filter out the data noise present in the data set. Via empirical validation, it is discovered that the optimization method obtains an average accuracy of 78.83% when the amount of cycles is 40. When the number of cycles is 40, the recall rate of the optimized algorithm is the highest, and the recall rate is 78%. In terms of recommendation popularity comparison, the optimization algorithm popularity starts to be higher than the traditional algorithm when the number of cycles exceeds 80. Therefore, the system has excellent practicality and resilience, and it operates steadily, which is of positive significance for creating an atmosphere of collaborative and win-win university thinking and political education for teachers and students with various forms and individual innovation.

Exploration of the innovative development path of university civic education based on data mining algorithm

Applied Mathematics and Nonlinear Sciences

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

Addressing the issues of a single form, insufficient targeting, a lack of synergy, and the inability to create a customized collaborative education process in...

Exploration of the innovative development path of university civic education based on data mining algorithm

Publié en ligne: 02 oct. 2023

Pages: -

Reçu: 26 déc. 2022

Accepté: 12 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00454

Mots clés
Civic education, Collaborative filtering algorithm, Data mining, Gradual forgetting curve, Personalized course recommendation

© 2023 Weigang Luo, published by Sciendo

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

Exploration of the innovative development path of university civic education based on data mining algorithm

Publié en ligne: 02 oct. 2023

Pages: -

Reçu: 26 déc. 2022

Accepté: 12 avr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00454

Mots clésCivic education, Collaborative filtering algorithm, Data mining, Gradual forgetting curve, Personalized course recommendation

© 2023 Weigang Luo, published by Sciendo

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

Mots clés
Civic education, Collaborative filtering algorithm, Data mining, Gradual forgetting curve, Personalized course recommendation