Development of a Big Data Framework for Student Admission Management in Ahmadu Bello University, Zaria, Kaduna State
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Background of the Study
The exponential growth of student populations in higher education institutions necessitates efficient admission management systems. At Ahmadu Bello University in Zaria, Kaduna State, the development of a big data framework for student admission management offers a robust solution to streamline the admission process. Big data technologies can handle vast amounts of information from various sources, including application forms, academic records, and demographic data, enabling a more accurate and efficient selection process (Babatunde, 2023). By leveraging advanced analytics and data mining techniques, the proposed framework can identify patterns and trends that inform decision-making, reduce processing times, and enhance transparency in the admission process. Such a system also enables real-time data integration and analysis, ensuring that admission decisions are based on the most current and comprehensive information available (Ibrahim, 2024). The framework will incorporate modules for data collection, storage, processing, and visualization, allowing admission officers to easily access and analyze key performance indicators. This digital transformation is expected to reduce administrative burdens, minimize errors, and improve the overall efficiency of student admissions. Furthermore, the integration of big data into the admission process can help universities tailor their recruitment strategies to attract high-quality candidates and meet enrollment targets effectively. This study aims to design and develop a big data framework specifically for managing student admissions at Ahmadu Bello University, evaluating its impact on operational efficiency and decision accuracy, and providing recommendations for its broader implementation (Chinwe, 2025).
Statement of the Problem
The traditional student admission process at Ahmadu Bello University is hampered by manual data handling, fragmented information systems, and inefficiencies that lead to delays and errors in decision-making. These challenges not only affect the timely processing of applications but also undermine the transparency and fairness of the admission process (Olu, 2023). As the volume of applications continues to grow, the existing system struggles to manage the data effectively, resulting in bottlenecks and administrative burdens. Although big data technologies have the potential to revolutionize admission management by providing real-time insights and automation, their implementation in the university’s current framework is limited by technical, infrastructural, and organizational challenges. This study seeks to address these issues by developing a comprehensive big data framework that integrates data from multiple sources to streamline the admission process. The framework aims to enhance the accuracy of admission decisions, reduce processing times, and improve overall administrative efficiency. The research will identify critical bottlenecks in the current system, assess the potential of big data analytics to address these challenges, and propose a scalable model for implementation. By evaluating the performance of the new framework against traditional methods, the study will provide evidence-based recommendations for improving student admission management at Ahmadu Bello University (Udo, 2024).
Objectives of the Study:
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To design and develop a big data framework for managing student admissions.
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To evaluate the framework’s impact on processing efficiency and decision accuracy.
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To propose strategies for the effective integration of big data technologies into the admission process.
Research Questions:
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How does a big data framework improve the efficiency of the student admission process?
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What impact does it have on the accuracy and fairness of admission decisions?
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What challenges are encountered in implementing big data solutions, and how can they be mitigated?
Significance of the Study
This study is significant as it explores the use of big data to optimize student admission management at Ahmadu Bello University. The research aims to enhance operational efficiency, improve decision accuracy, and ensure a more transparent and fair admission process. The findings will provide actionable insights for university administrators and policymakers, facilitating the digital transformation of admission systems in higher education.
Scope and Limitations of the Study:
The study is limited to the development and evaluation of a big data framework for student admission management at Ahmadu Bello University, Zaria, Kaduna State, and does not extend to other administrative processes or institutions.
Definitions of Terms:
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Big Data Framework: A structured system for managing and analyzing large volumes of data.
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Student Admission Management: The process of handling student applications and enrollment procedures.
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Data Analytics: The science of analyzing raw data to draw meaningful conclusions.
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