An IoT-Based Classroom Monitoring Framework with Integrated Visual Cues for Student Engagement Analysis

This thesis presents an IoT-based smart classroom monitoring system that integrates face recognition and motion detection to analyze student attendance and engagement. Using camera-based sensing and computer vision techniques, the system captures student presence and identifies key behavioral patterns to generate meaningful engagement insights. In addition to system development, this research evaluates user acceptance by conducting a structured assessment of the technology’s adoption among students and academic staff. The findings aim to support the implementation of intelligent, data-driven classroom solutions that enhance learning effectiveness while ensuring usability, acceptance, and sustainability in higher education environments.