Abstract
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.
Highlights
In the past 20 years, because video can truly reproduce classroom teaching and provide detailed materials for teachers’ teaching reflection, various methods based on video have been used for teaching reflection
Video is considered as a very effective tool for teacher professional development, due to the large amount of information contained in the real scene of the classroom, teachers have problems in the selection of videos, the focus of reflection, and the energy consumed, which makes them easy to fall into the state of information overload [1,2,3,4]
Late fusion (%) 79.0 76.65 effect of early fusion is better than the late-fusion effect, and it is better than using single modal information. is indicates that RGB information and skeleton information can complement each other and achieve the effect of 1 + 1 > 2, which can better reflect the essence of teacher’s behavior
Summary
In the past 20 years, because video can truly reproduce classroom teaching and provide detailed materials for teachers’ teaching reflection, various methods based on video have been used for teaching reflection. E task of human action recognition is to classify the actions in a series of frames (or videos), which can be divided into three stages: feature extraction, action representation, and action classification. Human action recognition is mainly for simple scenes, and the main method is to extract global features, such as human contour, human skeleton, and human motion field [11,12,13,14].
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