EduTrack – Smart Attendance and Student Monitoring System

Abstract

Intelligent technologies that simplify academic administration and student monitoring have been developed as a result of the growing desire for digital transformation in education. In order to improve efficiency, accuracy, and transparency in academic monitoring and attendance management, the proposed work, EduTrack—Smart Attendance and Student Monitoring System, offers an integrated, automated framework that integrates facial recognition and machine learning analytics. Conventional attendance systems frequently result in administrative inefficiencies since they are laborious, prone to errors, and susceptible to proxy entries. In order to overcome these obstacles, EduTrack uses facial recognition and QR-based verification to automate attendance capture, guaranteeing dependability and real-time data synchronization. Additionally, the system analyzes academic trends and identifies at-risk students using machine learning models like Random Forest Classifier and Logistic Regression. Teachers and parents are alerted for prompt interventions by a dedicated dashboard that provides interactive in sights. In addition to reducing human intervention, this clever platform enables educational institutions to implement data-driven tactics for student achievement and academic enhancement.