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

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.

Keywords

• lstm
• bilstm-crf
• entity-relationship extraction
• knowledge fusion storage
• civics education reform
• 62-07