IoT + AI Attendance Monitoring to Predict Dropout Risk — Wisdom School Musanze, Rwanda

Seven researchers from Rwanda Polytechnic (IPRC-Kigali) and the University of Rwanda built an IoT and AI-based attendance monitoring system at Wisdom School Musanze, a non-boarding secondary school in Musanze District, to address dropout linked to unmonitored absenteeism. The system combines a fingerprint sensor for attendance registration and verification, a passive infrared (PIR) sensor to detect a student's physical presence, and a real-time clock to timestamp records, feeding a web application that gives parents and administrators real-time visibility and generates daily, monthly and annual reports.
Using secondary data from the school's 2020–2021 academic-year records for students in Senior One through Senior Six — including sex, orphan status and school-fees payment history — the team trained a decision-tree machine-learning classifier that reached 91.4% accuracy in demonstrating that higher attendance rates are strongly associated with lower dropout in this school. The work was published in the European Journal of Technology (21 March 2023) and is also held in the University of Rwanda's institutional repository.
This is a single-school case study with an undisclosed sample size, so the 91.4% figure has not been shown to generalise beyond Wisdom School Musanze. The paper does not address the data-protection and consent implications of fingerprinting minors, nor whether the system has been adopted at any other school since publication.

Read the full analysis: https://ajpojournals.org/journals/index.php/EJT/article/view/1383