QR Code-Driven Attendance Automation: A Comprehensive Review of the Smart Attendance Management System Using MERN Stack

Abstract

Accurate and tamper-resistant attendance recording is a persistent challenge in academic environments. Conventional approaches that rely on paper registers or manual digital entry are vulnerable to proxy marking, data loss, and administrative inefficiency. This paper presents a review of the Smart Attendance Management System (SAMS), a full-stack web application developed with the MongoDB, Express.js, React.js, and Node.js (MERN) technology stack. The system replaces manual processes with a QR code-based attendance mechanism in which faculty generate session-specific, time-bounded QR codes that students must scan within a defined validity window. Role-based access control, enforced through JSON Web Token (JWT) authentication, grants differentiated capabilities to three user categories: Administrator, Faculty, and Student. The Administrator oversees institution-wide user management; Faculty members conduct sessions, generate QR codes, and export attendance reports; Students scan codes and monitor their individual attendance percentages in real time. Security measures include bcrypt password hashing and a limit of five QR generation attempts per session to mitigate misuse. Automated report generation in both PDF and Excel formats reduces the clerical burden on academic staff. Experimental validation confirmed that attendance marking latency decreased from five to ten minutes using manual methods to under thirty seconds, with 99.8% recording accuracy and no proxy attendance incidents across all evaluated sessions. The paper also examines related work, architectural design decisions, implementation methodology, and prospective enhancements including geofencing and AI-based attendance forecasting.

Introduction

The text discusses the need for improving attendance management systems in higher education due to the limitations of traditional methods such as paper registers and manual roll calls. These conventional approaches are time-consuming, error-prone, and vulnerable to proxy attendance, which undermines academic integrity. To address these issues, the paper proposes a Smart Attendance Management System (SAMS) built using the MERN stack, integrating time-limited QR codes and JWT-based authentication to ensure secure, efficient, and automated attendance tracking.

The literature review highlights the evolution of attendance systems from hardware-based solutions like biometric fingerprint and RFID systems to more modern approaches such as facial recognition and QR codes. While biometrics and RFID provide accuracy, they suffer from scalability and security issues like hardware dependency and card sharing. Facial recognition systems achieve high accuracy but face challenges such as lighting variability, privacy concerns, and high computational cost. Static QR codes are simple but vulnerable to proxy misuse, whereas dynamic QR codes significantly reduce fraud by introducing time limits, though they still lack advanced control mechanisms.

Web-based solutions using full-stack frameworks like MEAN or MERN improve scalability and usability, with React.js offering efficient real-time updates. Security enhancements using JWT and role-based access control (RBAC) further strengthen authentication and data protection in academic systems.

Conclusion

This paper presented a comprehensive review of the Smart Attendance Management System, a MERN stack web application designed to automate academic attendance recording through QR code-based session management, role-differentiated access control, and automated report generation. The system addresses the principal deficiencies of conventional attendance methods: susceptibility to proxy marking, administrative inefficiency, and lack of real-time visibility for students and faculty. Experimental evaluation confirmed that the system reduced attendance recording time by 93.4%, maintained 99.8% recording accuracy, and successfully prevented all proxy attendance attempts across forty evaluated sessions. API performance measurements demonstrated sub-200 millisecond response times for all interactive operations, confirming suitability for concurrent classroom deployment. Cross-browser and cross-device testing validated the functional completeness of the responsive web interface across contemporary desktop and mobile platforms. Prospective enhancements identified for future development include: integration of GPS-based geofencing to verify student physical proximity to the designated classroom before accepting scan submissions; implementation of a machine learning model trained on historical attendance patterns to identify students at risk of failing minimum attendance thresholds before the examination period; addition of SMS gateway integration to extend report delivery and low-attendance alerts to users with limited internet access; migration to a Progressive Web App (PWA) architecture to enable limited offline functionality; and development of dedicated Android and iOS native applications to leverage platform-specific notification and biometric authentication capabilities. The Smart Attendance Management System demonstrates that modern web technologies, when thoughtfully integrated, can deliver measurable improvements in academic administrative workflows without requiring specialised hardware investment, thereby providing a scalable and cost-effective solution suitable for adoption across a wide range of educational institutions.

References

[1] D. L. McCabe and L. K. Trevino, \"Academic Dishonesty: Honor Codes and Other Contextual Influences,\" Journal of Higher Education, vol. 64, no. 5, pp. 522-538, 1993. [2] A. Banks and E. Porcello, Learning React: Modern Patterns for Developing React Apps, 2nd ed., Sebastopol, CA: O\'Reilly Media, 2020. [3] V. Prabhu and K. R. Rao, \"Fingerprint-Based Attendance Management System for Educational Institutions,\" International Journal of Engineering Research and Technology, vol. 9, no. 3, pp. 411-416, 2020. [4] M. Sharma, P. Jain, and R. Mehta, \"Design and Implementation of RFID-Based Student Attendance System,\" International Journal of Computer Science and Mobile Computing, vol. 10, no. 4, pp. 45-52, 2021. [5] A. Gupta and S. Verma, \"Real-Time Facial Recognition for Classroom Attendance Using Convolutional Neural Networks,\" IEEE Access, vol. 10, pp. 34521-34530, 2022. [6] M. Veale and F. Z. Borgesius, \"Demystifying the Draft EU Artificial Intelligence Act,\" Computer Law Review International, vol. 22, no. 4, pp. 97-112, 2021. [7] A. Nair and J. Joseph, \"QR Code Based Attendance Monitoring System,\" International Journal of Advanced Research in Computer Science, vol. 12, no. 2, pp. 78-83, 2021. [8] D. Patel, S. Shah, and R. Trivedi, \"Time-Bound Dynamic QR Code System for Fraud-Resistant Attendance Management,\" in Proc. International Conference on Intelligent Computing and Control Systems (ICICCS), 2022, pp. 1203-1208. [9] N. Singh and A. Kumar, \"MEAN Stack Based Real-Time Attendance Dashboard for Academic Institutions,\" Journal of Computing Technologies, vol. 12, no. 1, pp. 33-41, 2023. [10] E. Luthra and M. Chopra, \"Performance Benchmarking of React.js and Angular in Dynamic Data-Intensive Web Applications,\" International Journal of Web Engineering, vol. 7, no. 2, pp. 14-22, 2022. [11] C. Cimpanu, \"Session Fixation Vulnerabilities in University Learning Management Systems,\" ZDNet Security, 2020. [Online]. Available: https://www.zdnet.com [12] M. B. Jones, J. Bradley, and N. Sakimura, \"JSON Web Token (JWT),\" RFC 7519, Internet Engineering Task Force (IETF), May 2015. [Online]. Available: https://datatracker.ietf.org/doc/html/rfc7519 [13] R. Naidu, G. V. Prasad, and S. Lakshmi, \"Implementing Role-Based Access Control in Cloud-Hosted Educational ERP Systems,\" International Journal of Computer Science and Engineering, vol. 11, no. 2, pp. 110-118, 2023. [14] P. Reddy and U. Lakshmi, \"OTP-Based Student Attendance System Using Flutter and Firebase,\" in Proc. International Conference on Emerging Technologies in Computer Engineering, 2024, pp. 215-220. [15] MongoDB Inc., \"MongoDB Atlas Documentation,\" 2024. [Online]. Available: https://www.mongodb.com/docs/atlas/ [16] Express.js Foundation, \"Express.js API Reference,\" 2024. [Online]. Available: https://expressjs.com/en/api.html [17] Auth0 Inc., \"Introduction to JSON Web Tokens,\" 2024. [Online]. Available: https://jwt.io/introduction

Copyright

Copyright © 2026 Bethamsetty Gopinadh, Medisetti Rajkumar, Narala Bhaskara Sai Siva Durga, Shaik Irfan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.