Enhancing Scholarship Allocation Through Machine Learning: A Review of Models and Techniques

Abstract

Scholarship allocation is essential for providing access to education, especially for students from underrepresented and economically disadvantaged backgrounds. However, traditional methods of manual and rule-based scholarship distribution often suffer from biases, inconsistencies, and inefficiencies. This systematic literature review (SLR) investigates the application of machine learning (ML) models to enhance the scholarship allocation process, addressing critical concerns regarding scalability and fairness. The review synthesizes existing research on traditional algorithms, deep learning techniques, and ensemble methods, including Random Forest, Support Vector Machines (SVM), and Gradient Boosting, while highlighting their strengths and limitations in scholarship selection. The analysis indicates that ensemble models and decision trees, such as C5.0, achieve notable accuracy, while deep learning models excel in identifying complex data patterns, albeit at the cost of significant computational resources. Additionally, effective data preprocessing techniques, such as normalization and feature engineering, are identified as vital for optimizing model performance. The study concludes with recommendations for integrating ML models into scholarship systems to promote equitable access to education, emphasizing the importance of hybrid approaches to tackle real-world challenges. This review not only consolidates current methodologies but also identifies research gaps, offering valuable insights for future studies aimed at improving the efficacy and fairness of machine learning-driven scholarship allocation.