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

• URL: http://arxiv.org/abs/2601.08049v1
• Date: Mon, 12 Jan 2026 22:38:25 GMT
• Title: Integrating Attendance Tracking and Emotion Detection for Enhanced Student Engagement in Smart Classrooms
• Authors: Keith Ainebyona, Ann Move Oguti, Joseph Walusimbi, Ritah Kobusingye,
• Abstract summary: This paper presents an IoT-based system that integrates automated attendance tracking with facial emotion recognition to support classroom engagement monitoring.<n>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.<n>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.
• Score: 0.6749750044497732
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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