用摄像头同时盯考勤和情绪，让老师实时掌握学生注意力
Integrating Attendance Tracking and Emotion Detection for Enhanced Student Engagement in Smart Classrooms
- 用树莓派+摄像头抓人脸，用微调MobileNetV2分四种情绪
- 在DAiSEE数据集上情绪识别准确率达89.5%
- 支持教师通过云端看板查看班级整体情绪变化
智能教室技术在高等教育中的应用多集中于自动考勤，对学生课堂情绪与认知投入的关注较少，限制了教师及时发现注意力不集中并调整教学策略的能力。本文提出SCASED（带情绪检测的智能考勤系统），一个基于物联网的系统，将自动考勤与面部情绪识别相结合，实现课堂参与度监测。系统采用树莓派摄像头与OpenCV进行人脸检测，并使用微调后的MobileNetV2模型对四种学习相关情绪状态（专注、无聊、困惑、挫败）进行分类。通过会话机制实现一次签到后持续情绪分析。考勤与情绪数据通过云端仪表板可视化，帮助教师了解课堂动态。在DAiSEE数据集上的实验表明，情绪分类准确率达到89.5%。结果表明，将考勤数据与情绪分析结合，可为教师提供更全面的课堂洞察，支持更具响应性的教学实践。
原文摘要 · Abstract (English)
The increasing adoption of smart classroom technologies in higher education has mainly focused on automating attendance, with limited attention given to students' emotional and cognitive engagement during lectures. This limits instructors' ability to identify disengagement and adapt teaching strategies in real time. This paper presents SCASED (Smart Classroom Attendance System with Emotion Detection), an IoT-based system that integrates automated attendance tracking with facial emotion recognition to support classroom engagement monitoring. The system uses a Raspberry Pi camera and OpenCV for face detection, and a finetuned MobileNetV2 model to classify four learning-related emotional states: engagement, boredom, confusion, and frustration. A session-based mechanism is implemented to manage attendance and emotion monitoring by recording attendance once per session and performing continuous emotion analysis thereafter. Attendance and emotion data are visualized through a cloud-based dashboard to provide instructors with insights into classroom dynamics. Experimental evaluation using the DAiSEE dataset achieved an emotion classification accuracy of 89.5%. The results show that integrating attendance data with emotion analytics can provide instructors with additional insight into classroom dynamics and support more responsive teaching practices.
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