Integrating Attendance Tracking and Emotion Detection for Enhanced Student Engagement in Smart Classrooms
Keith Ainebyona, Ann Move Oguti, Joseph Walusimbi, Ritah Kobusingye

TL;DR
This paper introduces SCASED, an IoT system combining attendance tracking and emotion detection to monitor student engagement in real time, enhancing teaching responsiveness in smart classrooms.
Contribution
The paper presents a novel integrated system using facial emotion recognition and attendance data for real-time classroom engagement monitoring.
Findings
Emotion classification accuracy of 89.5% on DAiSEE dataset
Integration of attendance and emotion data offers deeper classroom insights
System supports real-time engagement monitoring for instructors
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…
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Taxonomy
TopicsEmotion and Mood Recognition · Intelligent Tutoring Systems and Adaptive Learning · Mind wandering and attention
