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

<p>This paper proposes new methods and approaches to online ideological and political education for college students. Firstly, a network ideological and political intelligence platform is constructed based on the functional stratification characteristics of big data. Then, the ideological and political contents are clustered and filtered, and the contents are divided into several teaching fields according to the nature of various events. Based on this, the “incentive compatibility” mechanism based on “interest cluster” analysis is used to cluster and model users interested in the topic, to recommend relevant online content to similar groups. Finally, we analyzed the problems of online ideological and political education in colleges and universities in China and proposed countermeasures. Only 52.3% of colleges and universities have substantial recognition of the construction work results. 75% of the first batch of pilot colleges and universities have funding sources with the Ministry of Education and the schools themselves, 45% of colleges and universities have special funds of over 1 million, and the remaining 55% have special funds below 200,000. For further improvement, college students’ online ideological and political construction needs to be paid attention to and carried out.</p>
</abstract>

This paper proposes new methods and approaches to online ideological and political education for college students. Firstly, a network ideological and political intelligence platform is constructed based on the functional stratification characteristics of big data. Then, the ideological and political contents are clustered and filtered, and the contents are divided into several teaching fields according to the nature of various events. Based on this, the “incentive compatibility” mechanism based on “interest cluster” analysis is used to cluster and model users interested in the topic, to recommend relevant online content to similar groups. Finally, we analyzed the problems of online ideological and political education in colleges and universities in China and proposed countermeasures. Only 52.3% of colleges and universities have substantial recognition of the construction work results. 75% of the first batch of pilot colleges and universities have funding sources with the Ministry of Education and the schools themselves, 45% of colleges and universities have special funds of over 1 million, and the remaining 55% have special funds below 200,000. For further improvement, college students’ online ideological and political construction needs to be paid attention to and carried out.

Combining big data with innovative education on online ideology and politics for college students

Applied Mathematics and Nonlinear Sciences

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

This paper proposes new methods and approaches to online ideological and political education for college students. Firstly, a network ideological and political...

Combining big data with innovative education on online ideology and politics for college students

Data publikacji: 09 wrz 2023

Zakres stron: -

Otrzymano: 18 lis 2022

Przyjęty: 20 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00312

Słowa kluczowe
Big data, Online ideology and politics, Incentive compatibility, Cluster screening, Interest clusters

© 2023 Qi Fan, published by Sciendo

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

Combining big data with innovative education on online ideology and politics for college students

Data publikacji: 09 wrz 2023

Zakres stron: -

Otrzymano: 18 lis 2022

Przyjęty: 20 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00312

Słowa kluczoweBig data, Online ideology and politics, Incentive compatibility, Cluster screening, Interest clusters

© 2023 Qi Fan, published by Sciendo

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

Słowa kluczowe
Big data, Online ideology and politics, Incentive compatibility, Cluster screening, Interest clusters