Title: Transforming Motion Data into Educational Insights: A Holistic Framework for Analyzing and Feedback on Student Behavior in Physical Education

Abstract:

Enhancing instructional quality and boosting student engagement in educational environments depend heavily on the accurate analysis of student behavior. While contemporary AI-driven models predominantly utilize classroom video recordings to detect and examine student activities, these video-centric approaches face significant limitations. Specifically, they often fail to precisely track individual movements in physical education (PE) classes, where activities occur in expansive, outdoor settings and involve a wide variety of dynamic actions. Moreover, existing methods struggle to generalize to the specialized technical movements characteristic of PE and typically lack the integration of specific pedagogical expertise. This absence hinders their capacity to deliver profound behavioral insights or provide actionable feedback for refining instructional strategies.

To overcome these challenges, we introduce a comprehensive, end-to-end framework that synthesizes human activity recognition systems based on motion signals with sophisticated large language models. This approach enables a more granular analysis and feedback mechanism for student behavior within PE contexts. The framework initiates with the teacher’s instructional plans and incorporates motion data captured from students during PE sessions, culminating in the generation of automated reports. These reports offer valuable teaching insights and recommendations aimed at optimizing both student learning outcomes and instructional design. By providing a motion signal-driven methodology for behavior analysis and instructional optimization tailored to PE, our solution addresses key gaps in current technology. Experimental findings confirm that the proposed framework successfully identifies student behaviors with high accuracy and generates meaningful pedagogical insights.

Source: arXiv Generated at: 2026-06-04 00:00:00 UTC