在线体育教学中的情感生成方法基于教师与学生互动的数据挖掘
Yanwei Zhao1, Xiangyun Kong2, Wei Zheng3
1Langfang Normal University, LangFang, China.
PeerJ. Computer science
|January 23, 2024
概括
这项研究介绍了在线体育教育的情感生成模型,增强了教师与学生的互动和学习成果. 该模型通过数据挖掘和先进的人工智能技术改善了情感话语.
科学领域:
- 教育技术的教育技术
- 人工智能的人工智能
- 情感计算是一种情感计算.
背景情况:
- 在线教育,特别是虚拟体育教育,由于缺乏直接的教师与学生的情感互动而扎.
- 现有的情感生成模型需要针对特定的教育环境和群体进行改进.
- 在线学习中的教师-学生情感动态研究不足.
研究的目的:
- 开发和评估在线体育教育的情感生成模型.
- 在虚拟学习环境中改进情感话语并增强学习成果.
- 在教育环境中解决当前情感生成模型的局限性.
主要方法:
- 教师与学生互动的数据挖掘用于产生情绪.
- 多模式对话文本情感识别模型.
- 使用联合解码的话题扩展情感对话生成模型.
- 长期短期记忆神经网络用于模拟情绪波动.
主要成果:
- 拟议的模型在损失和混值方面表现出优于传统的端到端模型的优势.
- 在情绪预测方面取得了84.4%的准确率.
- 在对话中嵌入情感线索显著增加了响应生成.
结论:
- 开发的情感生成模型有效地提高了在线体育教育中的情感话语和学习效率.
- 该模型提供了一种新的方法来改进虚拟教育环境的情感生成.
- 研究结果表明,将情绪智能嵌入人工智能可以改善在线学习体验.
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