Optimization of Student Hostel Allocation Using Big Data in Federal University Gashua, Yobe State
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Background of the Study
Efficient allocation of student hostels is a critical component of university administration, impacting student satisfaction and overall operational efficiency. At Federal University Gashua in Yobe State, traditional hostel allocation methods have been predominantly manual, leading to mismatches, overcrowding, and underutilization of available resources. Big data analytics offers a promising solution by harnessing large volumes of data from student applications, historical occupancy rates, and demographic information to optimize the allocation process (Ibrahim, 2023). By applying clustering algorithms and optimization models, the university can develop a system that assigns hostel accommodations in a way that balances demand and maximizes resource utilization. This data-driven approach enables the identification of patterns in student preferences and needs, facilitating the development of allocation strategies that reduce conflicts and enhance student experience. Furthermore, integrating real-time data analysis with historical trends allows for dynamic adjustments to allocations as new data becomes available, ensuring that the system remains responsive to changing circumstances. The use of big data in hostel allocation also promotes transparency and fairness, as decisions are based on quantifiable metrics rather than subjective judgment (Chinwe, 2024). Such systems have been successfully implemented in various sectors, demonstrating improvements in efficiency and cost savings. However, challenges related to data integration, privacy concerns, and the computational complexity of processing large datasets must be addressed to fully realize the benefits of this technology. This study aims to develop and evaluate a big data-based framework for optimizing student hostel allocation, thereby enhancing operational efficiency and student satisfaction at Federal University Gashua (Olufemi, 2025).
Statement of the Problem
The current hostel allocation process at Federal University Gashua is characterized by inefficiencies and inconsistencies due to its reliance on traditional, manual methods. These methods often result in uneven distribution of hostel spaces, with some hostels experiencing overcrowding while others remain underutilized. This imbalance not only affects student comfort and satisfaction but also leads to inefficient use of university resources. Moreover, the absence of a centralized, data-driven system prevents the university from accurately forecasting demand and making informed decisions based on real-time occupancy data. Issues such as delayed updates, subjective decision-making, and a lack of integration between various data sources further exacerbate the problem (Adebola, 2023). Consequently, many students are dissatisfied with the allocation outcomes, and administrative inefficiencies persist. This study seeks to address these challenges by implementing a big data analytics framework that optimizes hostel allocation through automated, objective decision-making. The framework will integrate historical occupancy data, student demographics, and application trends to produce an allocation model that is both fair and efficient. By identifying key determinants of hostel usage and predicting future demand, the system can enable proactive planning and reduce the administrative burden associated with manual allocation processes. Ultimately, this research aims to enhance the overall efficiency of hostel management, improve student satisfaction, and provide a scalable model for other universities facing similar challenges.
Objectives of the Study:
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To develop a big data framework for optimizing student hostel allocation.
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To evaluate the efficiency and fairness of the proposed allocation system.
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To propose recommendations for integrating the system into the university’s administrative processes.
Research Questions:
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How can big data analytics improve the fairness of hostel allocation?
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What factors most significantly influence hostel occupancy patterns?
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How can the proposed framework be integrated into existing administrative systems?
Significance of the Study
This study is significant as it demonstrates the potential of big data analytics to optimize student hostel allocation, leading to improved resource utilization and enhanced student satisfaction. The findings will provide a model for data-driven decision-making in university housing management, offering practical recommendations for administrators seeking to modernize allocation processes and ensure equitable distribution of resources (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to optimizing student hostel allocation using big data at Federal University Gashua, Yobe State, and does not extend to other housing or administrative services.
Definitions of Terms:
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Big Data Analytics: The process of analyzing large datasets to extract meaningful insights and patterns.
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Hostel Allocation: The assignment of residential spaces to students within university hostels.
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Optimization: The process of making a system as effective and efficient as possible.
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