Evaluation of Machine Learning Models for Automating Scholarship Award Processes in University of Jos, Plateau State
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Background of the Study
Scholarship awards play a critical role in supporting student success by providing financial assistance to deserving candidates. However, the scholarship award process at the University of Jos, Plateau State, is often manual, time-consuming, and prone to human error, leading to delays and inconsistencies in awarding funds. The advent of machine learning offers an opportunity to automate and optimize this process by analyzing large datasets that include academic performance, financial need, and extracurricular achievements (Ibrahim, 2023). Machine learning models can streamline the evaluation process by identifying patterns and predicting the likelihood of student success, thereby enabling a more objective and efficient allocation of scholarships. By integrating data from multiple sources, such as application forms, transcripts, and recommendation letters, these models can provide a comprehensive assessment of candidate eligibility. Advanced algorithms, including decision trees, support vector machines, and ensemble methods, can enhance prediction accuracy and reduce bias in decision-making (Chinwe, 2024). The automation of scholarship award processes not only minimizes administrative burden but also increases transparency and fairness, ensuring that scholarships are awarded based on merit and need. Despite these advantages, challenges such as data privacy, algorithmic fairness, and the integration of automated systems with existing administrative processes remain significant. This study aims to evaluate various machine learning models for automating the scholarship award process at the University of Jos, providing insights into their effectiveness and offering recommendations for implementation (Olufemi, 2025).
Statement of the Problem
The scholarship award process at the University of Jos is hampered by traditional manual methods that are inefficient, inconsistent, and subject to human bias. This results in delayed scholarship disbursement and potential inequities in the selection of candidates (Adebola, 2023). The current system struggles to process large volumes of application data accurately and promptly, leading to a lack of transparency and potential misallocation of funds. Although machine learning models offer a promising alternative by automating the evaluation of candidate data, their adoption is limited by challenges such as data quality issues, integration with existing systems, and concerns about fairness and privacy. Without an effective automated system, the university is unable to ensure that scholarship funds are allocated to the most deserving candidates in a timely manner, which undermines the overall goal of supporting academic excellence. This study seeks to address these challenges by comparing various machine learning models to determine their predictive accuracy and reliability in automating the scholarship award process. The goal is to develop a robust, data-driven framework that enhances the objectivity and efficiency of scholarship selection, thereby reducing administrative burden and improving student satisfaction.
Objectives of the Study:
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To evaluate the performance of different machine learning models in automating scholarship awards.
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To determine the key factors that influence scholarship eligibility.
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To propose an optimized, automated framework for the scholarship award process.
Research Questions:
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Which machine learning model best predicts scholarship eligibility?
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What are the key predictors of student success for scholarship awards?
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How can the automated system be integrated with existing administrative processes?
Significance of the Study
This study is significant as it evaluates the potential of machine learning to automate and improve the scholarship award process at the University of Jos. The research offers a data-driven solution that enhances efficiency, fairness, and transparency, ultimately ensuring that financial support reaches the most deserving candidates. The findings will provide actionable insights for administrators and policymakers, contributing to the digital transformation of academic support services (Ibrahim, 2023).
Scope and Limitations of the Study:
The study is limited to evaluating machine learning models for automating scholarship awards at the University of Jos, Plateau State, and does not extend to other financial aid programs or institutions.
Definitions of Terms:
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Machine Learning: A subset of artificial intelligence that enables systems to learn from data.
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Scholarship Award Process: The procedure for evaluating and granting financial assistance to students.
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Automated System: A technology-driven process that minimizes human intervention.
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