Evaluation of Big Data-Based Course Enrollment Prediction Models in Federal University Dutsin-Ma, Katsina State
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Background of the Study
Accurate course enrollment predictions are essential for effective academic planning, resource allocation, and maintaining institutional efficiency. At Federal University Dutsin-Ma, Katsina State, traditional enrollment forecasting methods, which often rely on historical data and manual calculations, have proven insufficient in capturing the dynamic trends of student preferences and market conditions. Big data-based course enrollment prediction models leverage vast datasets from admissions records, demographic information, and historical enrollment trends to provide more precise and timely forecasts (Chinwe, 2023). These models utilize advanced machine learning algorithms and statistical techniques to identify patterns and correlations that traditional methods might overlook. With the integration of real-time data analytics, universities can not only predict enrollment numbers with higher accuracy but also adapt course offerings to meet changing student demands. Such predictive systems facilitate more efficient scheduling, budgeting, and resource management, ultimately enhancing the quality of education (Ibrahim, 2024). However, the adoption of big data analytics faces challenges, including data integration from disparate sources, ensuring data quality, and addressing privacy concerns. Despite these challenges, the potential benefits of more accurate enrollment predictions are significant, providing universities with the ability to optimize course allocation and improve overall student satisfaction (Adebayo, 2023). This study aims to evaluate the performance of big data-based enrollment prediction models at Federal University Dutsin-Ma, comparing them with traditional forecasting methods, and offering recommendations to improve the accuracy and operational efficiency of enrollment predictions (Balogun, 2025).
Statement of the Problem
Federal University Dutsin-Ma currently faces challenges in accurately forecasting course enrollment numbers using traditional methods. These conventional techniques, which rely heavily on historical enrollment data and manual analysis, often fail to capture the complex variables influencing student registration trends, leading to mismatches in course capacity and resource allocation (Chinwe, 2023). Inaccurate enrollment predictions can result in over- or under-provisioning of academic resources, thereby affecting classroom management, faculty allocation, and overall student satisfaction. Although big data-based predictive models offer a promising solution by integrating diverse data sources and applying advanced analytics, their implementation is hampered by issues related to data quality, integration challenges, and concerns over student data privacy (Ibrahim, 2024). Additionally, the lack of a standardized framework for model validation makes it difficult for administrators to assess the reliability of these predictions. This gap in accurate, real-time forecasting hinders the university’s ability to respond proactively to enrollment fluctuations and to plan effectively for future academic terms. Therefore, this study seeks to address these issues by evaluating the effectiveness of big data-based course enrollment prediction models, comparing their performance with traditional methods, and developing recommendations to enhance data integration, model accuracy, and operational efficiency (Adebayo, 2023; Balogun, 2025).
Objectives of the Study:
- To develop and validate a big data-based predictive model for course enrollment.
- To compare the accuracy of big data-based predictions with traditional forecasting methods.
- To recommend strategies for improving data integration and model reliability.
Research Questions:
- How do big data-based models improve course enrollment predictions compared to traditional methods?
- What are the main challenges in implementing these predictive models?
- How can data integration and quality issues be addressed to enhance prediction accuracy?
Significance of the Study
This study is significant as it investigates the effectiveness of big data-based enrollment prediction models, providing critical insights for optimizing course planning and resource allocation at Federal University Dutsin-Ma. The research findings will enable more accurate enrollment forecasting, reduce operational inefficiencies, and support strategic academic planning, thereby improving overall institutional performance (Chinwe, 2023).
Scope and Limitations of the Study:
This study is limited to evaluating enrollment prediction models at Federal University Dutsin-Ma, Katsina State.
Definitions of Terms:
• Big Data Analytics: The analysis of large datasets to extract insights and inform decision-making (Ibrahim, 2024).
• Course Enrollment Prediction: The process of forecasting student registration numbers for academic courses (Chinwe, 2023).
• Predictive Model: A statistical model used to forecast future outcomes based on historical data (Balogun, 2025).
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