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arXiv 2601.08049cs.AI

融合考勤追踪与情绪检测以提升智能教室学生参与度

Integrating Attendance Tracking and Emotion Detection for Enhanced Student Engagement in Smart Classrooms

Soroti University（索罗蒂大学）

Keith Ainebyona, Ann Move Oguti, Joseph Walusimbi, Ritah Kobusingye

备注：
15 pages, 8 figures
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AI总结：

SCASED系统通过融合考勤追踪与情绪检测，提升智能教室学生参与度，实验准确率达89.5%。

摘要：

随着智能教室技术在高等教育中的广泛应用，主要集中在自动化考勤，对讲座中学生情绪和认知参与的关注有限。这限制了教师识别不参与并实时调整教学策略的能力。本文提出了SCASED（智能教室考勤系统与情绪检测），一个基于物联网的系统，整合了自动考勤追踪与面部情绪识别，以支持课堂参与度监控。系统使用Raspberry Pi相机和OpenCV进行面部检测，并使用微调的MobileNetV2模型来分类四种与学习相关的情绪状态：参与、厌倦、困惑和沮丧。实现了一个基于会话的机制来管理考勤和情绪监测，通过记录每次会话的考勤情况并随后进行持续的情绪分析。考勤和情绪数据通过基于云的仪表板进行可视化，为教师提供课堂动态的洞察。使用DAiSEE数据集的实验评估实现了情绪分类准确率89.5%。结果表明，将考勤数据与情绪分析结合可以为教师提供课堂动态的额外见解，并支持更具响应性的教学实践。

英文摘要：

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.

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。

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