一个多模式数据集,用于在线课堂中分析教师行为
Chenglei Huang1, Jia Zhu2, Yilong Ji3
1Zhejiang Key Laboratory of Intelligent Education Technology and Application, Zhejiang Normal University, Jinhua, 321004, China.
Scientific data
|July 2, 2025
概括
研究人员创建了MM-TBA,这是一个新的多模式数据集,用于分析教师在真实课堂上的行为. 这个数据集解决了真实的教学数据的稀缺问题,促进了教育科学和人工智能的研究.
科学领域:
- 教育科学教育科学教育科学
- 认知科学 认知科学
- 人工智能的人工智能
- 教育技术的教育技术
背景情况:
- 教师行为分析对于提高教学质量和教育发展至关重要.
- 现有的数据集缺乏真实性,使用在线视频不反映真实课堂动态.
- 对于线下教师行为分析的综合,多模式数据集存在重大差距.
研究的目的:
- 介绍MM-TBA,一个新的多模式数据集,用于在真实的线下课堂环境中分析教师行为.
- 通过提供复杂,现实世界的教学场景来克服现有数据集的局限性.
- 促进对教师行为,认知科学和教育技术的科学研究.
主要方法:
- 收集了来自300多名实习教师在线课堂环境中的4839个教学视频.
- 手动过了大约32,000秒的镜头,捕捉了各种教学活动.
- 开发了专门的子数据集:教学行动检测,评估报告和教学设计.
主要成果:
- MM-TBA数据集为分析教师行为提供了丰富的多模式资源.
- 包括时间动作检测,讲座评估和教学设计组件.
- 在现实的教育环境中研究细微的教师行为提供了基础.
结论:
- MM-TBA解决了在教师行为研究中对真实数据集的关键需求.
- 该数据集预计将促进教育科学,认知科学和教育中的AI应用.
- 它是结合人工智能和教育技术的跨学科研究的宝贵工具.
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