Open Access

ARTICLE

A New Framework for Scholarship Predictor Using a Machine Learning Approach

by Bushra Kanwal¹, Rana Saud Shoukat², Saif Ur Rehman^2,*, Mahwish Kundi³, Tahani AlSaedi⁴, Abdulrahman Alahmadi⁴

1 School of Science and Technology, Foundation University Islamabad, Rawalpindi, 46000, Pakistan
2 University Institute of Information Technology, PMAS-University of Arid Agriculture, Rawalpindi, 46000, Pakistan
3 Maynooth International Engineering College, Maynooth University, Maynooth, W23 F2H6, Irland
4 Applied College, Taibah University, Madina, 42353, Saudi Arabia

* Corresponding Author: Saif Ur Rehman. Email:

Intelligent Automation & Soft Computing 2024, 39(5), 829-854. https://doi.org/10.32604/iasc.2024.054645

Received 03 June 2024; Accepted 16 July 2024; Issue published 31 October 2024

Abstract

Education is the base of the survival and growth of any state, but due to resource scarcity, students, particularly at the university level, are forced into a difficult situation. Scholarships are the most significant financial aid mechanisms developed to overcome such obstacles and assist the students in continuing with their higher studies. In this study, the convoluted situation of scholarship eligibility criteria, including parental income, responsibilities, and academic achievements, is addressed. In an attempt to maximize the scholarship selection process, numerous machine learning algorithms, including Support Vector Machines, Neural Networks, K-Nearest Neighbors, and the C4.5 algorithm, were applied. The C4.5 algorithm, owing to its efficiency in the prediction of scholarship beneficiaries based on extraneous factors, was capable of predicting a phenomenal 95.62% of predictions using extensive data of a well-esteemed government sector university from Pakistan. This percentage is 4% and 15% better than the remainder of the methods tested, and it depicts the extent of the potential for the technique to enhance the scholarship selection process. The Decision Support Systems (DSS) would not only save the administrative cost but would also create a fair and transparent process in place. In a world where accessibility to education is the key, this research provides data-oriented consolidation to ensure that deserving students are helped and allowed to get the financial assistance that they need to reach higher studies and bridge the gap between the demands of the day and the institutions of intellect.

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Keywords

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