广西师范大学学报(哲学社会科学版) ›› 2026, Vol. 62 ›› Issue (4): 76-86.doi: 10.16088/j.issn.1001-6597.2026.04.008

• 教育科学 • 上一篇    下一篇

多模态课堂视频分析技术生态:分析要素、潜力空间与风险规避

屈曼祺1, 李宝敏2   

  1. 1.华东师范大学 教师教育学院, 上海 200062;
    2.华东师范大学 教师发展学院, 上海 200062
  • 收稿日期:2024-05-11 出版日期:2026-07-05 发布日期:2026-07-01
  • 作者简介:屈曼祺,华东师范大学教育学系博士研究生,研究方向:智能教师教育;李宝敏,华东师范大学教师发展学院教授、博士生导师,上海市终身教育研究员,研究方向:智能教育、教师学习与发展,本文通讯作者。
  • 基金资助:
    2023年度国家社会科学基金教育学一般课题“课堂教学视频分析的人机协同诊断模型构建与应用研究”(BHA230123)

The Technology Ecosystem of Multimodal Classroom Video Analysis: Analytical Elements, Potential Space, and Risk Mitigation

QU Man-qi1, LI Bao-min2   

  1. 1. School of Teacher Education, East China Normal University, Shanghai 200062, China;
    2. School of Teacher Development, East China Normal University, Shanghai 200062, China
  • Received:2024-05-11 Online:2026-07-05 Published:2026-07-01

摘要: 课堂视频分析是揭示教学规律、促进教师专业成长的重要路径,也是教学质量评价的新常态。智能技术发展为课堂研究提供新范式,技术赋能的课堂分析成为重要研究方向。基于人本人工智能应用框架和人因设计、体现人智的技术设计、合伦设计三个维度,多模态课堂视频分析领域的分析要素、潜力空间与风险规避值得深入研究。在分析要素层面,聚焦师生行为、情感与认知三类模态信息,其中行为识别技术发展最快,情感分析趋于成熟,认知维度智能分析仍相对薄弱;在潜力空间层面,未来需推动分析对象从单一主体走向师生长时交互、分析技术从多源数据走向融合决策、分析内容从要素识别走向意义赋予;在风险规避层面,技术应用面临隐私泄露、算法偏见、价值异化与情感防御等问题,需通过隐私监护、算法公平、价值引导与情感包容加以应对。这一分析框架为智能技术支持下的精准课堂分析、精准教研与教师专业发展提供了理论参考与实践借鉴。

关键词: 多模态, 课堂视频分析, 技术生态, 技术伦理

Abstract: Classroom video analysis serves as a crucial pathway for uncovering instructional laws and fostering teacher professional growth, and has become the new normal in teaching quality evaluation. The advancement of intelligent technologies offers novel paradigms for classroom research, positioning technology-enhanced classroom analysis as a significant research direction. Grounded in the Human-Centered Artificial Intelligence (HCAI) application framework, this study systematically examines the analytical elements, potential space, and risk mitigation within the field of multimodal classroom video analysis across three dimensions: human-factor design, human-intelligence embodied technical design, and ethically-aligned design. At the level of analytical elements, the focus is on three types of modal information: teacher-student behavior, emotion, and cognition. Among these, behavioral recognition technology is developing most rapidly, affective analysis is maturing, while intelligent analysis of the cognitive dimension remains relatively underdeveloped. At the level of potential space, future efforts should be directed to promote the evolution of analytical subjects from single entities to long-term teacher-student interaction, the evolution of analytical techniques from multi-source data to integrated decision-making, and the evolution of analytical content from element identification to meaning attribution. At the level of risk mitigation, technological applications face challenges such as privacy leakage, algorithmic bias, value alienation, and emotional defense, which must be addressed through privacy stewardship, algorithmic fairness, value guidance, and emotional inclusivity. Based on these findings, this study provides theoretical references and practical insights for precise classroom analysis, precision teaching research, and teacher professional development supported by intelligent technologies.

Key words: multimodal, classroom video analysis, technology ecosystem, technology ethics

中图分类号:  G434

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