Optimization of AI-Based Smart Attendance Monitoring System in Federal University of Technology, Minna, Niger State
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Background of the Study
Attendance monitoring is a fundamental aspect of academic management in universities. Traditional methods of tracking attendance, such as manual roll calls or paper registers, are time-consuming and prone to errors. The Federal University of Technology, Minna, Niger State, has faced challenges in maintaining accurate and efficient attendance records. AI-based smart attendance systems can automate and optimize the process by using facial recognition, biometric scanning, and other advanced technologies (Abdullahi & Saidu, 2023). These systems not only improve the accuracy of attendance records but also reduce administrative burden, allowing faculty and administrative staff to focus on more strategic tasks.
Statement of the Problem
The current attendance monitoring system at the Federal University of Technology, Minna, is inefficient, prone to errors, and time-consuming. The manual methods of tracking attendance do not provide real-time data, which may lead to discrepancies in student records. This study aims to optimize the AI-based smart attendance monitoring system to ensure better accuracy, real-time data processing, and a more efficient workflow.
Objectives of the Study
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To assess the current attendance monitoring system at the Federal University of Technology, Minna.
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To design and optimize an AI-based smart attendance monitoring system using facial recognition and biometric technologies.
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To evaluate the effectiveness of the optimized system in improving the accuracy and efficiency of attendance monitoring.
Research Questions
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What are the limitations of the current attendance monitoring system at the Federal University of Technology, Minna?
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How can AI-based technologies improve the accuracy and efficiency of attendance monitoring?
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How effective is the optimized AI-based attendance system in reducing administrative overhead and ensuring accurate attendance records?
Research Hypotheses
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The implementation of an AI-based attendance monitoring system will significantly improve the accuracy of attendance records compared to traditional methods.
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There is a significant relationship between the use of facial recognition technology and the efficiency of attendance tracking.
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Optimizing the AI-based attendance system will reduce the time spent on attendance monitoring in the university.
Significance of the Study
This study will contribute to the development and optimization of AI-based attendance monitoring systems in universities, offering a more efficient and accurate alternative to traditional methods. It will also reduce administrative workload and enhance the management of student attendance records at the Federal University of Technology, Minna.
Scope and Limitations of the Study
The study will focus on the optimization of the AI-based smart attendance system at the Federal University of Technology, Minna. The research will explore the system’s impact on accuracy and efficiency in attendance monitoring. Limitations include potential issues with data privacy and ensuring the system’s scalability for large classes.
Definitions of Terms
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AI-Based Smart Attendance System: A system that uses artificial intelligence technologies, such as facial recognition or biometrics, to track and record student attendance automatically.
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Facial Recognition: A biometric technology that identifies individuals based on their facial features.
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Attendance Monitoring: The process of tracking student attendance during classes or academic activities.
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