ArXiv TLDR

RFID-Based Non-Biometric Classroom Attendance System: Proxy Attendance Detection via Weight Sensor Integration

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2604.22697

Furkan Ege, Muhsin Özdemir

cs.CYcs.HC

TLDR

This paper introduces an RFID-based, non-biometric classroom attendance system that uses weight sensors to detect and prevent proxy attendance.

Key contributions

  • Proposes a biometric-free IoT attendance system to prevent proxy attendance.
  • Integrates RFID with weight sensors to verify student presence without storing personal data.
  • Compares weight sensor data against a statistical range, avoiding biometric privacy issues.
  • Prototype uses Arduino, Bluetooth, and a Python GUI for student management and reporting.

Why it matters

Traditional attendance systems are inefficient and prone to proxy attendance, while biometric solutions raise privacy concerns. This system offers a low-cost, privacy-friendly alternative by combining RFID with weight sensors to accurately verify student presence. It enhances academic integrity and saves instructional time without storing sensitive personal data.

Original Abstract

Attendance tracking in educational institutions, when conducted through traditional methods, leads to structural problems that consume instruction time and threaten academic integrity. Attendance durations spanning several minutes in primary and secondary education and exceeding ten minutes in higher education, combined with the proxy attendance problem of signing on behalf of someone else, demonstrate the need for electronic systems. Most existing electronic solutions rely on biometric authentication, which raises legal and ethical risks under the European General Data Protection Regulation (GDPR), the Turkish Personal Data Protection Law (KVKK), and the United States Family Educational Rights and Privacy Act (FERPA). Systems using RFID alone provide no built-in safeguard against proxy attendance through card transfer. This study proposes a biometric-free IoT attendance system addressing both deficiencies. The prototype consists of an RFID module, RFID cards, weight sensors, a Bluetooth module, and an Arduino UNO microcontroller. After the student presents their RFID card, the weight sensor measurement is compared against a statistical reference range of 350 individuals (aged 18-22) compiled from three Kaggle datasets; no personal biometric data is recorded. A Python-based GUI performs student management, course tracking, and CSV-based reporting via Bluetooth. Qualitative tests in conditions close to a real classroom have shown that the RFID reading, weight verification, Bluetooth communication, and GUI modules operate in an integrated manner as expected. The proposed system offers a low-cost and reproducible solution that aims to reduce proxy attendance without storing biometric data.

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