Analyzing student behavior in educational scenarios is crucial for enhancing teaching quality and student engagement. Existing AI-based models often rely on classroom video footage to identify and analyze student behavior. While these video-based methods can partially capture and analyze student actions, they struggle to accurately track each student's actions in physical education classes, which take place in outdoor, open spaces with diverse activities, and are challenging to generalize to the specialized technical movements involved in these settings. Furthermore, current methods typically lack the ability to integrate specialized pedagogical knowledge, limiting their ability to provide in-depth insights into student behavior and offer feedback for optimizing instructional design. To address these limitations, we propose a unified end-to-end framework that leverages human activity recognition technologies based on motion signals, combined with advanced large language models, to conduct more detailed analyses and feedback of student behavior in physical education classes. Our framework begins with the teacher's instructional designs and the motion signals from students during physical education sessions, ultimately generating automated reports with teaching insights and suggestions for improving both learning and class instructions. This solution provides a motion signal-based approach for analyzing student behavior and optimizing instructional design tailored to physical education classes. Experimental results demonstrate that our framework can accurately identify student behaviors and produce meaningful pedagogical insights.

翻译：在教育场景中分析学生行为对于提升教学质量和学生参与度至关重要。现有基于人工智能的模型通常依赖课堂视频片段来识别和分析学生行为。尽管这些基于视频的方法能够部分捕捉和分析学生动作，但在户外开放空间、活动多样的体育课堂中，它们难以准确追踪每位学生的动作，且难以泛化至这些场景中涉及的专业技术动作。此外，当前方法通常缺乏整合专业教学知识的能力，限制了其对学生行为提供深度洞察和为优化教学设计提供反馈的可能性。为克服这些局限，我们提出了一种统一的端到端框架，该框架利用基于运动信号的人体活动识别技术，结合先进的大语言模型，对体育课堂中学生行为进行更细致的分析与反馈。我们的框架从教师的教学设计以及学生在体育课中的运动信号出发，最终生成包含教学洞察和改进建议的自动化报告，以优化学习效果和课堂教学。该解决方案提供了一种基于运动信号的方法，用于分析学生行为并优化面向体育课堂的教学设计。实验结果表明，我们的框架能够准确识别学生行为，并产生富有意义的教学洞察。