Abstract
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.
Abstract (translated)
在高等教育中，智能教室技术的采用主要集中在自动签到上，而对于学生在课堂上的情感和认知参与度的关注较少。这限制了教师实时识别学生脱轨并调整教学策略的能力。本文介绍了一种基于物联网（IoT）的系统——SCASED（带情绪检测的智能教室考勤系统），该系统将自动化签到跟踪与面部表情识别相结合，以支持课堂参与度监测。 SCASED系统使用树莓派摄像头和OpenCV进行人脸检测，并采用经过微调的MobileNetV2模型来分类四种学习相关的情绪状态：投入、无聊、困惑和沮丧。此外，系统还实现了一种基于会话机制的方法来管理签到与情绪监控，即每个会话记录一次签到并随后持续进行情绪分析。 学生签到及情绪数据通过云端仪表板可视化显示，为教师提供有关课堂动态的洞察力。使用DAiSEE数据集进行实验评估后，达到了89.5%的情绪分类准确性。结果表明，将签到数据与情感分析相结合可以为教师提供更多关于课堂动态的见解，并支持更具响应性的教学实践。
URL
https://arxiv.org/abs/2601.08049
