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传统课堂教学分析,多以时间取样的手工编码为主,存在过度依赖专家、分析效率低、难以规模化服务等难题。基于人工智能技术的课堂教学分析要突破困境,需经历从全人工、弱人工智能、强人工智能发展到人机协同的进化路径;需建立由多源数据支持的教学案例库、文本视频为主的分析维度集、教学事件与时间取样相结合的多元分析方法而形成的分析框架;在实践层面,以计算机视觉为主的课堂行为分析,以自然语言理解和大数据为主的教学事件分析和评语分析等,可成为基于人工智能技术的课堂教学分析突破口,以期逐步达成人机协同、规模化、高效率开展课堂教学分析的目标。
Abstract：Traditional classroom teaching analysis based on manual coding has problems such as excessive dependence on experts, low analysis efficiency, and difficulty in large-scale services. In order to break through the predicament of artificial intelligencebased classroom teaching analysis, it is necessary to complete the process from total artificial, artificial narrow intelligence, artificial general intelligence to human-machine collaboration, build databases supported by teaching cases base with multi-source data and analysis dimensions mainly based on text and video, and create an analysis framework formed by a multi-analysis method combining teaching events and time sampling. From the perspective of practice, classroom behavior analysis based on computer vision, teaching event analysis and comment analysis based on natural language understanding and big data will become breakthroughs in classroom teaching analysis based on artificial intelligence, which aims to achieve the goal of human-machine collaborative, large-scale and efficient classroom teaching analysis gradually.
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基本信息:
DOI：
中图分类号:G434
引用信息:
[1]孙众,吕恺悦,骆力明等.基于人工智能的课堂教学分析[J].中国电化教育,2020,No.405(10):15-23.
基金信息:
国家自然科学基金项目“基于人工智能的课堂教学交互分析关键技术研究”(项目编号：61977048)阶段性研究成果