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

<p>One of the important directions for the reform of ideology and politics is the integration of big data with ideological and political education. As a type of big data technology, knowledge graphs can display a clear knowledge architecture, thereby optimizing the educational impact of courses in ideology and politics. In this paper, we first enhance the CRF model for processing sequence annotation, which is based on BiLSTM. We then propose features for judging entity relationships. Next, we use the classification model to classify the relationships within it. Finally, we integrate and store the knowledge of both civics and political science to create a comprehensive civics and political science knowledge graph. We found that the experimental class, applying knowledge mapping for Civics education, increased its total Civics score by 5.88 points and outperformed the control group in the knowledge memorization and application tests by 6.33 and 3.54 points, respectively. The experimental class’s post-test score for deep teaching interest reached 22.38, surpassing the control group’s evaluation, and the surface orientation decreased by 1.91 points. Research has demonstrated the benefits of using knowledge mapping in teaching to enhance students’ civics performance and learning interest.</p>
</abstract>

One of the important directions for the reform of ideology and politics is the integration of big data with ideological and political education. As a type of big data technology, knowledge graphs can display a clear knowledge architecture, thereby optimizing the educational impact of courses in ideology and politics. In this paper, we first enhance the CRF model for processing sequence annotation, which is based on BiLSTM. We then propose features for judging entity relationships. Next, we use the classification model to classify the relationships within it. Finally, we integrate and store the knowledge of both civics and political science to create a comprehensive civics and political science knowledge graph. We found that the experimental class, applying knowledge mapping for Civics education, increased its total Civics score by 5.88 points and outperformed the control group in the knowledge memorization and application tests by 6.33 and 3.54 points, respectively. The experimental class’s post-test score for deep teaching interest reached 22.38, surpassing the control group’s evaluation, and the surface orientation decreased by 1.91 points. Research has demonstrated the benefits of using knowledge mapping in teaching to enhance students’ civics performance and learning interest.

Research on the Reform of Ideological and Political Education in Colleges and Universities Based on the Background of Big Data

Applied Mathematics and Nonlinear Sciences

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

One of the important directions for the reform of ideology and politics is the integration of big data with ideological and political education. As a type of...

Research on the Reform of Ideological and Political Education in Colleges and Universities Based on the Background of Big Data

Published Online: Aug 05, 2024

Page range: -

Received: Mar 28, 2024

Accepted: Jun 26, 2024

DOI: https://doi.org/10.2478/amns-2024-2086

Keywords
LSTM, BiLSTM-CRF, Entity-relationship extraction, Knowledge fusion storage, Civics education reform

© 2024 Chaofan Jiang, published by Sciendo

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

Research on the Reform of Ideological and Political Education in Colleges and Universities Based on the Background of Big Data

Published Online: Aug 05, 2024

Page range: -

Received: Mar 28, 2024

Accepted: Jun 26, 2024

DOI: https://doi.org/10.2478/amns-2024-2086

KeywordsLSTM, BiLSTM-CRF, Entity-relationship extraction, Knowledge fusion storage, Civics education reform

© 2024 Chaofan Jiang, published by Sciendo

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

Keywords
LSTM, BiLSTM-CRF, Entity-relationship extraction, Knowledge fusion storage, Civics education reform