Exploring Employability Factors: A Machine Learning Approach Using Association Rules in Business and Economics Graduates at Qassim University

Abstract: The growing number of business and economics graduates raises concerns about employability in a competitive job market. Furthermore, scrutiny from the Saudi Education and Training Evaluation Commission on educational outcomes highlights the relevance of this research for university administrations. Current literature often overlooks the factors affecting employment outcomes for recent graduates. Understanding these factors is essential for addressing concerns. This study aims to fill these gaps by focusing on graduates from the College of Business and Economics at Qassim University, using association rule mining to uncover patterns and relationships among academic performance, skills, and employment status. This analysis uses a dataset of 407 graduates to examine factors such as gender, major, cumulative GPA, and employment status. As the job market evolves, the findings offer valuable observations for universities on aligning educational programs with employer needs. The association rules model was utilized to predict graduates' likelihood of securing employment based on these attributes, showing that factors such as GPA and skills significantly impact employment outcomes. The proposed model demonstrated high accuracy in predicting employability and generated 147 association rules, indicating its effectiveness in identifying the factors that influence employment outcomes. It also reveals actionable knowledge for curriculum development. The effectiveness of the association rules in identifying the most impactful attributes related to employment outcomes reinforces the importance of addressing the skills and competencies sought by employers. The proposed model demonstrates its reliability for practical use. By aligning educational offerings with market demands, universities can enhance the employability of graduates, ensuring they are prepared for a dynamic environment. This research highlights the critical role of data mining in informing educational strategies and connecting academia with industry.

Hussain Mohammad Abu-Dalbouh, Osman Abdalla Mohamed Elhadi, Ajlan Suliman Al-Ajlan, Leenah Sulaiman Almuhanna Abalkhail, Abdullah Suliman Almutlaq, Wejdan Aamer Alasqah, Mayadah Shikh Othman and Sulaiman Abdullah Alateyah. “Exploring Employability Factors: A Machine Learning Approach Using Association Rules in Business and Economics Graduates at Qassim University”. International Journal of Advanced Computer Science and Applications (IJACSA) 17.3 (2026). http://dx.doi.org/10.14569/IJACSA.2026.0170319

@article{Abu-Dalbouh2026,
title = {Exploring Employability Factors: A Machine Learning Approach Using Association Rules in Business and Economics Graduates at Qassim University},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2026.0170319},
url = {http://dx.doi.org/10.14569/IJACSA.2026.0170319},
year = {2026},
publisher = {The Science and Information Organization},
volume = {17},
number = {3},
author = {Hussain Mohammad Abu-Dalbouh and Osman Abdalla Mohamed Elhadi and Ajlan Suliman Al-Ajlan and Leenah Sulaiman Almuhanna Abalkhail and Abdullah Suliman Almutlaq and Wejdan Aamer Alasqah and Mayadah Shikh Othman and Sulaiman Abdullah Alateyah}
}