BadgeX：物联网增强型可穿戴分析与大语言模型结合用于协作学习
BadgeX: IoT-Enhanced Wearable Analytics Meets LLMs for Collaborative Learning
AI总结：
BadgeX结合轻量级可穿戴物联网设备与大语言模型，实现实时协作学习分析，通过多模态传感器数据生成高层面学习洞察。
摘要：
我们提出了BadgeX，一种将轻量级可穿戴物联网设备（智能徽章/智能手机）与大语言模型（LLMs）结合的新型系统，以实现实时协作学习分析。该系统从学习者处捕获多模态传感器数据（例如音频、图像、运动、深度），将其处理为结构化特征，并利用LLM驱动的框架来解释这些特征，生成基于学习理论的高层面洞察。初步研究证明了该系统能够捕获丰富的协作轨迹，并且LLM能够从传感器衍生的特征中生成合理且理论一致的叙述分析。BadgeX旨在降低部署障碍，使复杂的协作动态可视化，并在教育环境中提供实时支持的途径。
英文摘要：
We present BadgeX, a novel system integrating lightweight wearable IoT devices (smart badges/smartphones) with Large Language Models (LLMs) to enable real-time collaborative learning analytics. The system captures multimodal sensor data (e.g., audio, image, motion, depth) from learners, processes it into structured features, and employs an LLM-driven framework to interpret these features, generating high-level insights grounded in learning theory. A pilot study demonstrated the system's capability to capture rich collaboration traces and for an LLM to produce plausible, theoretically coherent narrative analyses from sensor-derived features. BadgeX aims to lower deployment barriers, making complex collaborative dynamics visible and offering a pathway for real-time support in educational settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
