Evaluation of AI-Based Facial Recognition-Based Smart Attendance Monitoring in Usmanu Danfodiyo University, Sokoto, Sokoto State
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Background of the Study
Student attendance is a critical metric used in higher education institutions to track student participation, but traditional attendance methods often face challenges like manual errors, inaccuracies, or student manipulation. Usmanu Danfodiyo University, Sokoto, is no exception, as reliance on paper-based or card swipe systems can lead to inefficiencies. The advent of AI-based facial recognition systems has the potential to revolutionize attendance monitoring by providing a more accurate, automated, and secure solution. These systems use AI algorithms to analyze facial features and authenticate students, ensuring that attendance records are tamper-proof and up-to-date in real-time. This study aims to evaluate the feasibility and effectiveness of an AI-based facial recognition attendance monitoring system for Usmanu Danfodiyo University to streamline attendance processes, enhance accuracy, and reduce administrative workload.
Statement of the Problem
At Usmanu Danfodiyo University, Sokoto, traditional attendance methods often lead to inefficiencies, including delays in recording attendance, potential errors in data entry, and the opportunity for students to manipulate the attendance records. While some electronic systems are in place, they lack the real-time automation and accuracy that an AI-based facial recognition system can offer. This gap in efficient attendance management can result in inaccurate records, making it difficult for instructors and administrators to effectively monitor student participation. The introduction of an AI-based facial recognition system is hypothesized to address these inefficiencies by providing an automated, accurate, and secure attendance monitoring solution.
Objectives of the Study
1. To design and implement an AI-based facial recognition system for student attendance at Usmanu Danfodiyo University, Sokoto.
2. To evaluate the accuracy and reliability of the AI facial recognition system in comparison to traditional attendance methods.
3. To assess the perception of both students and staff regarding the AI-based facial recognition attendance system.
Research Questions
1. How accurate is the AI-based facial recognition system in recording student attendance compared to traditional methods?
2. How does the use of AI facial recognition improve the efficiency of attendance monitoring at Usmanu Danfodiyo University?
3. What are the attitudes of students and faculty towards the use of facial recognition technology for attendance monitoring?
Research Hypotheses
1. The AI-based facial recognition system has higher accuracy in recording student attendance compared to traditional manual or electronic methods.
2. The use of AI-based facial recognition significantly improves the efficiency and speed of attendance recording in classroom settings.
3. Students and faculty are more satisfied with AI-based attendance monitoring compared to traditional attendance methods.
Significance of the Study
This research will contribute to enhancing the effectiveness of student attendance management at Usmanu Danfodiyo University by introducing a more efficient, secure, and automated solution. The study will also provide insights into the feasibility of implementing AI technology in everyday university administration, ultimately leading to improved operational efficiency and student monitoring.
Scope and Limitations of the Study
This study will focus on implementing and evaluating the AI-based facial recognition attendance system within selected departments at Usmanu Danfodiyo University, Sokoto. Limitations may include challenges with system integration into existing university infrastructure and potential resistance from students and staff to the use of biometric data for attendance.
Definitions of Terms
• AI-Based Facial Recognition: A technology that uses machine learning algorithms to identify individuals based on their facial features for authentication purposes.
• Attendance Monitoring: The process of tracking and recording student presence in academic settings.
• Biometric Data: Unique physical or behavioral characteristics, such as facial features or fingerprints, used for identification and authentication.
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