用于STEM课堂环境中实时测量个体学生参与度的生物传感器网络

中文摘要 AI 辅助

学生参与度（SE）是STEM教育中学术表现与学业保留的关键预测指标，但现有测量方法往往具有侵入性、人工密集型或不适用于实时课堂场景。本论文提出一种新型生物传感器网络（BSN），旨在实现STEM课堂环境中个体学生参与度的实时测量与连续跟踪。该系统通过基于摄像头的传感技术捕捉行为、情感和认知指标，同时遵守伦理与隐私约束。为以非侵入且符合伦理的方式测量这些指标，我们提出由学生处理单元（SPU）构成的BSN，SPU作为分布式传感节点。该网络明确设计满足五项目标：必须具备非侵入性、非侵害性、非污名化、实时性与自动性，同时严格保障学生数据安全与隐私。每个SPU支持两种运行模式：（i）数据集收集模式，在此模式下临时录制学生原始视频，以构建用于模型训练与验证的私有SE数据集；（ii）分析模式，在此模式下SPU对10秒视频片段执行实时推理，不存储或传输原始帧。在分析角色中，每个SPU支持全设备端处理，包括人脸检测、注视估计与情感分析，确保无身份可识别的视频数据离开设备。安全后端基础设施管理设备认证、会话编排与加密数据摄入。完整系统整合了硬件设计、计算机视觉流水线、无线网络、安全协议与会话级数据管理。

英文摘要

Student engagement (SE) is a critical predictor of academic performance and retention in STEM education, yet existing measurement approaches are often intrusive, manually intensive, or unsuitable for real-time classroom use. This thesis proposes a novel $\textit{Biometric Sensor Network}$ (BSN) designed to enable real-time measurement and continuous tracking of individual student engagement in STEM classroom environments. The system enables capturing of behavioral, emotional, and cognitive indicators through camera-based sensing while preserving ethical and privacy constraints. To measure these indicators unobtrusively and ethically, we propose a BSN composed of $\textit{Student Processing Units}$ (SPUs) that function as distributed sensing nodes. The network is explicitly designed to satisfy five objectives: it must be $\textbf{non-intrusive}, \textbf{non-invasive}, \textbf{non-stigmatizing}, \textbf{real-time}$, and $\textbf{automatic}$, while ensuring rigorous protection of student data security and privacy. Each SPU supports two operational modes: (i) a $\textit{dataset-collection mode}$, in which raw student video is temporarily recorded to construct a private SE dataset for model training and validation, and (ii) an $\textit{analysis mode}$, in which the SPU performs real-time inference on 10-second video segments without storing or transmitting raw frames. In this analysis role, each SPU enables fully on-device processing---including face detection, gaze estimation, and affective analysis---ensuring that no identifiable video data leaves the device. A secure backend infrastructure manages device authentication, session orchestration, and encrypted data ingestion. The full system integrates hardware design, computer-vision pipelines, wireless networking, security protocols, and session-level data management.

发表机构

• J.B. Speed School of Engineering（J.B. 斯皮德工程学院）
• Apple（苹果公司）

机构由 AI 辅助整理，请以论文原文为准。

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Thesis