Analysis of AI-Based Automated Attendance Systems for Large Lecture Halls: A Case Study of Modibbo Adama University, Yola (Yola North LGA, Adamawa State)
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Background of the Study
Tracking student attendance in large lecture halls has become a significant challenge in many universities, particularly with increasing class sizes and limited resources for manual attendance management. Traditional attendance systems, such as roll calls or sign-in sheets, can be time-consuming, prone to errors, and difficult to manage effectively (Nwankwo et al., 2023). In contrast, AI-based automated attendance systems offer a more efficient and accurate approach by using facial recognition, biometric data, or smart card technologies to automatically track student attendance without the need for manual input (Chika et al., 2024). These systems not only save time but also reduce human error, ensuring that attendance data is more accurate and reliable. The integration of AI with these technologies has the potential to revolutionize the way universities manage student attendance, particularly in large lecture halls where monitoring can be especially challenging (Ogunyemi et al., 2025).
Modibbo Adama University, Yola, located in Yola North LGA, Adamawa State, offers a case study to investigate the potential of implementing AI-based automated attendance systems in large lecture settings. The university, like many institutions in Nigeria, is grappling with overcrowded classrooms and the logistical challenges of managing attendance. This study explores the use of AI-driven solutions to automate and streamline the attendance-taking process, focusing on their feasibility, effectiveness, and potential benefits in improving the accuracy and efficiency of student attendance monitoring.
Statement of the Problem
At Modibbo Adama University, Yola, the manual tracking of student attendance in large lecture halls is inefficient and prone to human error. The university faces challenges such as delayed class starts due to manual roll calls, discrepancies in attendance records, and time lost during each session. These issues not only affect the academic environment but also hinder the timely implementation of administrative actions related to attendance-based policies. While AI-based attendance systems have been proposed as a potential solution, their adoption and effectiveness in the context of Nigerian universities, particularly in large classrooms, have not been fully explored.
Objectives of the Study
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To analyze the feasibility and effectiveness of implementing AI-based automated attendance systems at Modibbo Adama University, Yola.
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To evaluate the impact of AI-based attendance systems on the accuracy and efficiency of attendance tracking in large lecture halls.
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To assess student and faculty perceptions of AI-based automated attendance systems in improving classroom management.
Research Questions
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How feasible is the implementation of AI-based automated attendance systems in large lecture halls at Modibbo Adama University, Yola?
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What is the impact of AI-based attendance systems on the accuracy and efficiency of attendance tracking in large classes?
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How do students and faculty perceive the use of AI-based automated attendance systems in improving class management?
Significance of the Study
This study will provide insights into how AI can improve the efficiency and accuracy of attendance tracking in large lecture settings. The findings could inform the wider adoption of AI-based attendance systems in Nigerian universities, ultimately enhancing classroom management and ensuring timely administrative actions.
Scope and Limitations of the Study
This study will focus on analyzing the use of AI-based automated attendance systems in large lecture halls at Modibbo Adama University, Yola, located in Yola North LGA, Adamawa State. It will primarily assess the feasibility, effectiveness, and user perceptions of such systems, excluding other factors like class size and overall administrative systems.
Definitions of Terms
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AI-Based Automated Attendance System: An automated system that uses AI technologies such as facial recognition or biometric data to track student attendance without manual intervention.
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Facial Recognition Technology: A biometric system that identifies individuals based on their facial features, often used for authentication purposes.
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Lecture Hall Management: The process of managing and organizing classroom activities, including attendance, student engagement, and overall learning conditions.
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