Optimization of the University Admission Process Using Big Data in University of Maiduguri, Borno State
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Background of the Study
The university admission process is a critical function that significantly influences the academic and financial stability of higher education institutions. At the University of Maiduguri, Borno State, traditional admission processes have often been challenged by inefficiencies, inaccuracies, and a lack of predictive capabilities regarding student success. With the advent of big data, there is an opportunity to revolutionize the admission process by incorporating advanced analytics to predict student performance, identify potential candidates, and streamline decision-making. Big data analytics can integrate various data sources—including academic records, standardized test scores, demographic information, and extracurricular achievements—to develop predictive models that inform admission decisions (Nasir, 2023).
This data-driven approach allows for more objective and transparent admissions processes, reducing biases that may arise from manual evaluations. Furthermore, predictive analytics can help in forecasting enrollment numbers, optimizing resource allocation, and planning for future academic needs. The use of big data in the admission process also facilitates real-time monitoring of trends and patterns, enabling institutions to adapt quickly to changes in applicant demographics and academic performance. However, implementing big data analytics in the admission process requires robust data infrastructure, comprehensive data integration, and adherence to strict data privacy and security protocols (Chinwe, 2024). Additionally, there is a need to ensure that predictive models are continuously validated and refined to maintain accuracy and fairness in the admission process. This study aims to investigate how big data analytics can be used to optimize the admission process at the University of Maiduguri by comparing traditional methods with data-driven approaches and providing actionable recommendations for policy and practice improvements (Nasir, 2023; Chinwe, 2024; Musa, 2025).
Statement of the Problem
The current admission process at the University of Maiduguri suffers from inefficiencies and a lack of objectivity, resulting in suboptimal student selection and enrollment planning. Traditional methods rely heavily on manual review of applicant data, which can be inconsistent, time-consuming, and susceptible to human bias. These limitations often lead to inaccurate predictions of student success and difficulties in managing enrollment numbers (Nasir, 2023). Furthermore, the fragmented nature of applicant data—stored in various formats and systems—poses significant challenges for comprehensive analysis and decision-making. The absence of a unified, data-driven framework hinders the university’s ability to accurately forecast admission trends and allocate resources effectively. Privacy concerns and regulatory constraints further complicate the integration of big data analytics into the admission process (Chinwe, 2024). As a result, the university risks both under-enrollment and over-enrollment, each of which has detrimental effects on institutional performance and resource management. This study seeks to address these challenges by developing a big data-based predictive model for the admission process that enhances accuracy, efficiency, and fairness, thereby ensuring that the institution attracts and selects students with the highest potential for success (Musa, 2025).
Objectives of the Study:
• To develop a predictive model for university admission using big data analytics.
• To compare the performance of the big data-driven model with traditional admission methods.
• To propose strategies for integrating big data analytics into the admission process while ensuring data privacy.
Research Questions:
• How does big data analytics improve the accuracy of university admission predictions?
• What are the limitations of traditional admission methods compared to a data-driven approach?
• How can data privacy and security be ensured in a big data-based admission system?
Significance of the Study
This study is significant as it explores the application of big data analytics to optimize the university admission process at the University of Maiduguri. The findings will provide valuable insights for enhancing admission accuracy, resource allocation, and strategic planning, ultimately contributing to improved academic quality and institutional effectiveness (Nasir, 2023).
Scope and Limitations of the Study:
This study is limited to the optimization of the university admission process using big data at the University of Maiduguri, Borno State, and does not extend to other administrative processes or institutions.
Definitions of Terms:
• Big Data Analytics: The use of advanced computational techniques to analyze large, complex datasets (Nasir, 2023).
• Predictive Model: A statistical model used to forecast future outcomes based on historical data (Chinwe, 2024).
• Admission Process: The set of procedures used to select and enroll students into an institution (Musa, 2025).
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