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。现有的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，但在体育课堂中难以准确追踪每位学生的行为——体育课通常在户外开放空间进行，活动形式多样，且难以推广到涉及专业运动技术的场景。此外，当前方法通常缺乏整合专业教学知识的能力，限制了其深入理解学生行为并为优化教学设计提供反馈的能力。为应对这些局限，我们提出一个统一的端到端框架，该框架利用基于运动信号的人类活动识别技术，结合先进的大语言模型，对体育课堂中的学生行为进行更细致的分析和反馈。我们的框架从教师的教学设计和学生在体育课期间的运动信号出发，最终生成包含教学洞察及改进学习和课堂指导建议的自动化报告。该方案为体育课堂量身定制，提供了一种基于运动信号的学生行为分析与教学设计优化方法。实验结果表明，我们的框架能够准确识别学生行为并产生有意义的教学洞察。