Design and Implementation of AI-Based Automated Student Attendance Monitoring in Kogi State University, Anyigba, Kogi State
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Background of the study
Student attendance plays a significant role in academic success, as it is directly related to students’ engagement, participation, and overall learning outcomes. However, manual attendance monitoring in universities is often time-consuming and prone to inaccuracies. AI-based systems offer an innovative solution by automating the attendance tracking process, allowing for more accurate and efficient monitoring. These systems can use facial recognition, biometric data, or RFID technology to automatically mark student attendance, significantly reducing the administrative burden and improving data accuracy. In Kogi State University, Anyigba, the implementation of an AI-based automated attendance monitoring system has the potential to enhance the management of student attendance, minimize human errors, and ensure transparency. This study aims to design and implement an AI-powered system for automated attendance monitoring, evaluate its effectiveness, and explore students' and faculty members' perceptions of the system.
Statement of the problem
Manual attendance monitoring in Kogi State University is inefficient, often leading to discrepancies in attendance records and time wasted on administrative tasks. This has resulted in delayed reports, inaccuracies in attendance tracking, and increased workload for faculty members. Furthermore, the manual process is vulnerable to student manipulation, such as proxy attendance. Despite the growing interest in automated systems, there has been limited research and adoption of AI-based attendance monitoring systems in Kogi State University. The introduction of AI technologies could potentially address these challenges, but the feasibility and effectiveness of such systems in the university’s context remain uncertain.
Objectives of the study
1. To design and implement an AI-based automated student attendance monitoring system at Kogi State University, Anyigba.
2. To evaluate the effectiveness of the AI-based system in improving attendance tracking accuracy and efficiency.
3. To assess students' and faculty members' perceptions of the AI-based attendance monitoring system.
Research questions
1. How effective is the AI-based automated attendance system in improving the accuracy and efficiency of attendance tracking at Kogi State University?
2. What impact does the AI-based attendance system have on the reduction of proxy attendance?
3. How do students and faculty members perceive the use of AI-based automated attendance monitoring?
Research hypotheses
1. The AI-based automated attendance system will significantly improve attendance tracking accuracy and efficiency.
2. The use of the AI system will reduce the incidence of proxy attendance at Kogi State University.
3. Students and faculty members will have positive perceptions of the AI-based automated attendance monitoring system.
Significance of the study
This study will provide insights into how AI-powered attendance monitoring systems can enhance the management of student attendance in universities, improve operational efficiency, and reduce errors. The findings could help Kogi State University, and other institutions, to adopt more efficient and transparent attendance tracking systems.
Scope and limitations of the study
This study will focus on the design, implementation, and evaluation of an AI-based automated attendance monitoring system for Kogi State University, Anyigba. Limitations include challenges related to technology infrastructure, data privacy concerns, and user acceptance.
Definitions of terms
• Automated Attendance Monitoring: A system that uses AI or other technologies to automatically track student attendance without manual intervention.
• AI (Artificial Intelligence): Technology that enables machines to perform tasks that typically require human intelligence, such as recognizing faces or making decisions.
• Proxy Attendance: The act of a student being marked as present when they are not physically in class, typically by another student.
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