Intelligent Bi-modal Timetable-aware Biometric Attendance System for Enhanced Classroom Attendance

Abstract

Attendance management is integral to many organizations and academic institutions. The manner in which attendance is managed has evolved over the years encompassing various techniques and methodologies. Although significant improvements have been made, existing systems are mostly standalone systems without proper monitoring and control from a central point. This makes it difficult for several attendance devices to be linked by a common scheme like a timetable schedule. This is integral because the lives of students in every academic institution revolves around a schedule of events and classes. There is also a need to maximize the use of the data generated from attendance systems to make meaningful decisions and predictions about students’ academic activities. The research led to the implementation of several modules which includes the key timetable-schedule module and an elaborate API structure deployed to a central server for a centralized communication network between attendance devices instead of standalone devices as traditional systems have employed. The research also investigates factors affecting the performance of students and which of these factors is the greater determinant. From the results and dataset investigated, it was found that attendance is in fact not the primary determinant of students’ academic performance. Extra educational support, extra-curricular activities and family support are the top ranked factors affecting academic performance in accordance with the results obtained from this work.