智能深度学习模型用于推意识形态和政治音乐教育资源
1School of Music and Dance, Hunan City University, Yiyang City, 410030, Hunan Province, China. 35066798@qq.com.
Scientific reports
|October 17, 2025
概括
本研究介绍了一种智能深度学习模型,使用红色音乐建议来增强意识形态和政治教育 (IPE). 该模型提高了推准确性和对数字IPE资源的情感参与.
科学领域:
- 数字教育 数字教育
- 人工智能的人工智能
- 音乐信息检索 音乐信息检索
背景情况:
- 传统的意识形态和政治教育 (IPE) 交付面临着精度低,情感参与有限的挑战.
- 数字化转型需要创新的方法来增强IPE资源交付和学习者互动.
- 整合红色音乐等文化元素可以丰富教育经验.
研究的目的:
- 开发一个智能深度学习模型,用于推与红色音乐集成的IPE资源.
- 为了解决传统的IPE资源交付精度和情感参与的局限性.
- 为红色文化的数字遗产创造一个以技术为导向的范式.
主要方法:
- 利用多式特征提取来融合红色音乐的情感内容 (通过STFT和注意力机制) 与歌词语义.
- 使用动态认知诊断和基于变压器的时间序列建模构建学习者个人资料.
- 通过异质信息网络和层次强化学习生成精确的资源建议.
主要成果:
- 拟议的模型显著优于比较方法,推准确度提高了23%至35%.
- 教育相关性增加了多达29%,情感共增长了27%.
- 在大学课程中的试点测试证实了提高学生参与度.
结论:
- 该研究提出了一种新的,以技术为导向的数字IPE方法,通过红色音乐增强资源交付和情感联系.
- 智能推模型为教育中的文化遗产提供了一个范式.
- 未来的工作可以扩大多式联运数据集成,并探索个性化IPE的跨文化应用.
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