Comparative Study of Traditional and Big Data-Based Student Attendance Monitoring Systems in University of Ilorin, Kwara State
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Background of the Study
Accurate attendance monitoring is vital for ensuring academic integrity and effective resource management in educational institutions. At the University of Ilorin in Kwara State, traditional methods of tracking student attendance—such as manual registers and paper-based records—are increasingly proving inadequate in the face of growing student populations and technological advancements. Big data-based attendance monitoring systems offer an innovative alternative by leveraging digital tools and analytics to provide real-time, accurate attendance data (Ibrahim, 2023). These systems integrate data from biometric devices, RFID tags, and mobile applications, enabling seamless collection and analysis of attendance information. By applying data analytics, universities can identify trends, detect anomalies, and implement timely interventions to address issues such as chronic absenteeism. Comparative studies have shown that big data systems significantly reduce administrative burden and increase data accuracy compared to traditional methods (Olufemi, 2024). Furthermore, digital attendance systems offer enhanced transparency and accountability, as data can be visualized through dashboards and accessed by multiple stakeholders. Despite these advantages, challenges such as system integration, data privacy, and the initial cost of implementation may limit the adoption of big data-based systems. This study aims to conduct a comparative analysis of traditional versus big data-based student attendance monitoring systems at the University of Ilorin, evaluating their effectiveness, reliability, and impact on overall academic performance. The goal is to provide actionable insights into how digital transformation can enhance attendance management and contribute to improved institutional efficiency (Chinwe, 2025).
Statement of the Problem
The University of Ilorin currently relies on traditional attendance monitoring methods that are inefficient, prone to human error, and lack real-time capabilities. These conventional approaches often result in inaccurate records, delayed reporting, and difficulties in identifying patterns of absenteeism. The reliance on manual data entry creates opportunities for mistakes and compromises the integrity of the attendance data, which is critical for academic planning and intervention (Adebola, 2023). In contrast, big data-based monitoring systems offer a promising solution by automating data collection and providing real-time analytics. However, the implementation of these advanced systems is hampered by challenges including data integration from diverse sources, concerns over student privacy, and the substantial initial investment required. The inability to accurately track attendance not only affects resource allocation and administrative decision-making but also impacts student accountability and academic performance. Without a reliable system, it becomes difficult for the university to identify at-risk students and implement timely remedial actions. This study seeks to address these issues by comparing the performance of traditional and big data-based attendance monitoring systems, with the aim of determining which method provides superior accuracy, efficiency, and overall benefits for the university’s operational management.
Objectives of the Study:
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To compare the effectiveness of traditional and big data-based attendance monitoring systems.
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To evaluate the impact of each system on data accuracy and administrative efficiency.
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To recommend strategies for implementing a big data-based system in the university.
Research Questions:
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How do traditional and big data-based attendance systems differ in terms of accuracy and efficiency?
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What are the impacts of these systems on student accountability and academic performance?
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What challenges must be addressed to successfully implement a big data-based attendance system?
Significance of the Study
This study is significant as it provides a comprehensive comparison of traditional versus big data-based student attendance monitoring systems, offering critical insights into the benefits and challenges of digital transformation in educational administration. The findings will help university administrators enhance data accuracy, reduce administrative workload, and improve overall student engagement and accountability (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to comparing attendance monitoring systems at the University of Ilorin, Kwara State, and does not extend to other administrative functions or institutions.
Definitions of Terms:
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Traditional Attendance Monitoring: Manual methods of tracking student presence using paper-based systems.
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Big Data-Based Systems: Digital systems that utilize large datasets and analytics for real-time monitoring.
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Attendance Accuracy: The degree to which attendance records correctly reflect student presence.
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