Teaching reflection based on videos is the main method in teacher education and professional development. However, it takes a long time to analyse videos, and teachers are easy to fall into the state of information overload. With the development of “AI + education,” automatic recognition of teacher behavior to support teaching reflection has become an important research topic. In this paper, taking online open classroom teaching video as the data source, we collected and constructed a teacher behavior dataset. Using this dataset, we explored the behavior recognition methods based on RGB video and skeleton information, and the information fusion between them is carried out to improve the recognition accuracy. The experimental results show that the fusion of RGB information and skeleton information can improve the recognition accuracy, and the early-fusion effect is better than the late-fusion effect. This study helps to solve the problems of time-consumption and information overload in teaching reflection and then helps teachers to optimize the teaching strategies and improve the teaching efficiency.

中文翻译：

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基于多模式信息融合的教师行为识别

基于视频的教学反思是教师教育和专业发展的主要方法。但是，分析视频需要花费很长时间，并且教师很容易陷入信息过载的状态。随着“人工智能+教育”的发展，教师行为的自动识别以支持教学反思已成为重要的研究课题。本文以在线开放课堂教学视频为数据源，收集并构建了教师行为数据集。利用该数据集，探索了基于RGB视频和骨架信息的行为识别方法，并进行了信息融合以提高识别的准确性。实验结果表明，将RGB信息与骨架信息进行融合可以提高识别精度，早期融合效果优于后期融合效果。这项研究有助于解决教学反思中的时间消耗和信息过载的问题，进而帮助教师优化教学策略，提高教学效率。