Deep learning tool for higher learning scholarship award decisions: a case of higher institutions of learning

Abstract

In today’s highly competitive higher education environment, scholarship programs play a crucial role in attracting and retaining talented students, especially those from disadvantaged backgrounds. However, traditional scholarship award processes often rely on manual and subjective methods, which can lead to inefficiencies, biases, and inequitable distribution of financial aid. The lack of standardized criteria and the complexity of handling large volumes of applications further complicates the decision-making process. This study developed a deep learning-based tool to assist in higher education scholarship award decisions, utilizing convolutional neural network (CNN) to automate the selection process. Through a data-driven approach, the tool was designed to analyse student profiles across multiple dimensions, including academic performance, financial need, extracurricular involvement, and personal background. The deep learning model aims to provide more accurate, objective, and fair scholarship allocations by removing human biases and ensuring that all eligible students are assessed equitably. The developed tool was tested on a dataset of historical scholarship applications and awards, and the performance of the CNN model was evaluated based on accuracy, fairness, and efficiency compared to traditional manual methods. The model achieved an accuracy of 93% surpassing the performance of all the reviewed models at the end of fine-tuning. The findings of this study offer valuable insights into the feasibility and effectiveness of integrating AI-driven tools into scholarship management systems in higher education institutions. Ultimately, this study contributes to ongoing efforts to make scholarship distribution more equitable and aligned with institutional goals and societal needs.

Keywords: Scholarship Allocation, Deep Learning, Convolutional neural networks (CNNs), machine learning, higher education, automated decision-making, data-driven scholarship distribution, AI in education