基于深度学习的以学生为中心的人工智能在线音乐学习平台的建设
Ruiqing Xia1, Jiayin Li2, Haiying Li3,4
1Department of Education, Tianjin Normal University, Tianjin, 300387, China.
Scientific reports
|May 3, 2025
概括
本研究介绍了在线音乐平台的学生学习兴趣演变 (CRM-SLIE) 课程推模型. 该模型准确地预测了不断变化的学生兴趣,增强了个性化的课程建议.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 音乐教育 音乐教育教育
背景情况:
- 在线音乐学习平台需要有效的以学生为中心的课程建议.
- 现有的模型很难适应学生的动态学习兴趣和复杂的课程互动.
研究的目的:
- 为学生学习兴趣演变 (CRM-SLIE) 提出和评估课程推模型.
- 为了提高在线音乐学习平台上的课程建议的准确性和适应性.
主要方法:
- 在CRM-SLIE模型中,集成了注意力机制和Gated Recurrent Unit (GRU).
- 它包含一个项目交叉模块,以捕捉不断变化的学生兴趣和课程间的关系.
- 模型性能在各种嵌入维度和学生行为序列长度上进行了评估.
主要成果:
- CRM-SLIE表现出色,在嵌入维度为64和序列长度为20时,曲线下面积 (AUC) 达到0.872,达到最高.
- 该模型实现了0.364的最大召回率,超过了比较型号.
- 废弃实验证实了位置编码和项目交叉策略的重大影响.
结论:
- CRM-SLIE模型表现出强大的适应性,稳定性和在线音乐课程推的实用价值.
- 它有效地捕捉到动态的学生兴趣和复杂的课程关系,使个性化的建议.
- 该模型通过提供准确和相关的课程建议,显著改善了学习体验.
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