COMPUTER-BASED ACADEMIC MONITORING AND CONTROL OF LECTURE ATTENDANCE AT MICHAEL OKPARA UNIVERSITY OF AGRICULTURE, UMUDIKE

Abstract

Academic monitoring in Nigerian tertiary institutions largely depends on students’ and lecturers’ punctual attendance at lectures as well as the outcomes reflected in their examination reports. However, ensuring consistent attendance remains a major challenge, and the traditional manual method of taking attendance is often cumbersome and ineffective. To address these limitations, attempts have been made using electronic cards and clock-in systems. Nevertheless, such methods present issues such as the possibility of recording attendance for another individual. In light of these shortcomings, this research successfully applied computer-based Face Recognition Technology (FRT) to monitor and control lecture attendance. The objective of the study was to develop an automated academic monitoring system capable of capturing students’ and lecturers’ lecture attendance and relating these records to examination performance. The system was designed using the Object-Oriented Analysis and Design (OOAD) methodology. Data on students and lecturers were stored in a system database created with MySQL Database Management System (MySQL-DBMS), with each user assigned a unique identification number. The platform integrated the Fisherface algorithm, available in the Python library and implemented through OpenCV, to perform face detection and recognition for accurate time monitoring of lecture attendance. The results showed that the system efficiently recognized faces, recorded lecture sessions in real time, and captured still images for monitoring purposes. Furthermore, the system demonstrated the ability to generate students’ results that established a relationship between lecture attendance and examination performance.