SES: Smart Education System

Responding to inefficiencies in traditional classroom management methods, this paper proposes an innovative, technology-driven approach to enhance educational processes. Central to our proposal is the development of a facial recognition system designed for efficient student attendance tracking, thus eliminating time-consuming roll calls. Additionally, we plan to introduce interactive whiteboard features to enrich classroom dynamics between students and faculty. However, our approach extends beyond mere attendance tracking and interactive learning. We aim to launch a Program Learning Outcomes (PLO) and Course Learning Outcomes (CLO) mapper. Leveraging Natural Language Processing (NLP) techniques, this tool will auto-align CLOs with PLOs, facilitating a more efficient curriculum development process. We also suggest implementing a feature powered by YOLOv5 to monitor and assess student attention in the classroom. Our comprehensive suite of tools is designed to equip educators with resources to refine their teaching strategies and boost student learning outcomes. By integrating facial recognition for attendance, interactive whiteboard features, NLP-based CLO/PLO mapping, and attention monitoring, we aspire to provide a robust solution enabling educators to adapt their teaching methods to students' unique needs.