Implementation and Deployment of an Internet of Things (IoT)-Based Fingerprint Attendance Monitoring System for Evaluating Students’ Academic Performance: A Pilot Study at Moshood Abiola Polytechnic, Abeokuta, Nigeria

This study reports the development and pilot evaluation of an Internet-of-Things (IoT)-based fingerprint attendance system at Moshood Abiola Polytechnic, Abeokuta, Nigeria. The system was designed to automate attendance monitoring and investigate its relationship with students’ academic performance, thereby addressing the inefficiencies and susceptibility to manipulation associated with manual roll-call methods. By integrating biometric fingerprint authentication with real-time cloud-based data storage, the system ensured accurate, secure, and tamper-resistant attendance records. The implementation was carried out over one academic semester across three engineering departments (Computer Engineering, Electrical/Electronics, and Mechanical Engineering), involving two hundred (200) students and ten (10) academic staff. Attendance data were captured using IoT-enabled fingerprint devices, while academic performance indicators were derived from quizzes, assignments, and examinations. Data analysis employed descriptive statistics alongside inferential techniques, including Pearson correlation, linear regression, and one-way ANOVA. The results revealed mean attendance rates of 85.4%, 78.2%, and 92.1% across the respective departments, with corresponding average academic scores of 75.6%, 68.3%, and 81.2%. A statistically significant and strong positive correlation (p = 0.001) was observed between attendance and academic performance. Regression analysis further established attendance as a significant predictor of academic outcomes, while ANOVA findings indicated that students with high attendance accounted for a substantial proportion (71.3%) of performance variance. In conclusion, the system significantly improved student engagement and established a reliable linkage between attendance and academic outcomes, thereby strengthening academic accountability. Although minor network and device-related constraints were encountered, the results offer a solid basis for extended validation across diverse, multi-institutional educational environments.