SmartTasjeel: A GPS-Based Automated Attendance Tracking System for Medical Education Built on Microsoft PowerApps

Abstract

Background: Attendance in medical undergraduate programmes is consistently associated with academic performance, which makes accurate per-session tracking essential for early identification of at-risk students. At the Department of Basic Medical Sciences, Kulliyyah of Medicine, International Islamic University Malaysia, the previous biometric thumbprint system captured only twice-daily entries and excluded students with dermatological conditions affecting fingerprint recognition. This made per-session attendance percentage calculations impossible to automate. Methods: We developed SmartTasjeel, a GPS-based attendance system built entirely on free Microsoft 365 Education licences using PowerApps, SharePoint Online, and Excel with Power Query. The system uses geofencing validation with a 0.001-degree tolerance (approximately 110 metres at the campus latitude) and matches each time-in to a session pulled from track-specific timetables. It was deployed for 285 students across three curriculum tracks. Results: Per-session attendance processing dropped from 30 minutes (paper) or 10 minutes (biometric queues) to under 15 seconds. Weekly percentage calculations dropped from around 2 hours of manual computation to under 30 minutes of automated dashboard review. Data granularity increased from a maximum of 10 weekly capture points (biometric) to 20–25 per student (per-session). No calculation errors were reported by coordinators over two academic semesters. Hardware cost dropped from RM3,500–7,000 (biometric machines) to zero. Students previously excluded by fingerprint recognition issues were able to participate. Conclusion: SmartTasjeel demonstrates that GPS-based automated attendance tracking can be implemented using only free Microsoft 365 Education licences, with no premium connectors, no custom code, and no additional hardware purchase. The architecture is transferable to other institutions and the per-session data structure supports policy enforcement, warning-letter workflows, and student self-monitoring. Future work should formally evaluate whether real-time attendance feedback influences student attendance behaviour and academic performance.