Academic Journal
Enhancing Ideological and Political Education Through Semantic Understanding and Sentiment Analysis: A Data-Driven Adaptive Learning Framework.
| Title: | Enhancing Ideological and Political Education Through Semantic Understanding and Sentiment Analysis: A Data-Driven Adaptive Learning Framework. |
|---|---|
| Authors: | Miao, Xingguo1 (AUTHOR) miaoxingguo1983@163.com |
| Source: | International Journal of High Speed Electronics & Systems. Oct2026, Vol. 35 Issue 5, p1-32. 32p. |
| Subject Terms: | *Sentiment analysis, *Artificial intelligence, *Natural language processing, *Transformer models, Individualized instruction, Online education, Political science education, Instructional systems |
| Abstract: | The rapid development of online learning platforms has revolutionized Ideological and Political Education (IPE), offering unprecedented opportunities to personalize learning experiences and enhance student engagement. Traditional IPE methods often rely on static, instructor-led approaches that lack adaptability and real-time feedback. This study proposes an innovative computational framework that integrates semantic understanding, sentiment analysis, and adaptive learning mechanisms to address these limitations. Our model employs transformer-based architectures, such as BERT and GPT, to analyze textual interactions, identify sentiment trends, and provide educators with real-time insights into students' emotional and intellectual engagement. We incorporate explainable AI (XAI) techniques, including SHAP and LIME, to ensure model transparency and foster trust in educational applications. Empirical evaluations on multiple sentiment analysis datasets demonstrate that our approach significantly outperforms traditional models in sentiment classification accuracy, engagement prediction, and content adaptability. The proposed framework not only enhances the quality and effectiveness of ideological and political education but also provides a scalable solution for personalized learning in digital environments. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of High Speed Electronics & Systems is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Business Source Index |
| FullText | Text: Availability: 0 |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1142/S0129156425406783 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 32 StartPage: 1 Subjects: – SubjectFull: Sentiment analysis Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Natural language processing Type: general – SubjectFull: Transformer models Type: general – SubjectFull: Individualized instruction Type: general – SubjectFull: Online education Type: general – SubjectFull: Political science education Type: general – SubjectFull: Instructional systems Type: general Titles: – TitleFull: Enhancing Ideological and Political Education Through Semantic Understanding and Sentiment Analysis: A Data-Driven Adaptive Learning Framework. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Miao, Xingguo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: Oct2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 01291564 Numbering: – Type: volume Value: 35 – Type: issue Value: 5 Titles: – TitleFull: International Journal of High Speed Electronics & Systems Type: main |
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