From Motion Signals to Insights: A Unified Framework for Student Behavior Analysis and Feedback in Physical Education Classes

2025-03-09

AI Summary

Existing AI models struggle to accurately track diverse student movements in outdoor physical education (PE) classes and lack integration of pedagogical knowledge, hindering deep behavioral analysis and actionable instructional feedback. To address this, we propose an end-to-end wearable sensing framework tailored for PE instruction: it replaces video-based action recognition with inertial measurement unit (IMU) signals and introduces a novel motion-signal-driven behavioral modeling paradigm synergized with educational domain-specific large language models (LLMs). Our method incorporates multimodal feature alignment, pedagogy-informed prompt engineering, and LLM fine-tuning to embed PE teaching principles, enabling precise movement parsing and strategy-level feedback generation. Empirical evaluation demonstrates a 92.7% action recognition accuracy; automatically generated pedagogical insight reports achieved 87% acceptance among frontline PE teachers, significantly improving timeliness of error correction and efficiency of lesson plan optimization.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Motion & Tracking

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Accurate tracking of student actions in outdoor physical education classes.
• Integration of specialized pedagogical knowledge for in-depth behavior analysis.
• Automated feedback and insights for optimizing instructional design in PE.

Innovation

• Motion signal-based human activity recognition
• Integration of large language models
• Automated teaching insights and feedback