Leveraging Machine Learning for Predictive Pathways in Higher Education: A Case Study at Al-Zaytoonah University of Jordan
| International Journal of Electronics and Communication Engineering |
| © 2024 by SSRG - IJECE Journal |
| Volume 11 Issue 11 |
| Year of Publication : 2024 |
| Authors : Mohammad Muhairat, Wael Alzyadat, Ameen Shaheen, Aysh Alhroob, A. Nasser Asfour |
10.14445/23488549/IJECE-V11I11P104
How to Cite?
Mohammad Muhairat, Wael Alzyadat, Ameen Shaheen, Aysh Alhroob, A. Nasser Asfour, "Leveraging Machine Learning for Predictive Pathways in Higher Education: A Case Study at Al-Zaytoonah University of Jordan," SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 11, pp. 28-44, 2024. Crossref, https://doi.org/10.14445/23488549/IJECE-V11I11P104
Abstract:
This study investigates the application of Machine Learning (ML) for developing predictive pathways in higher education, focusing on the software engineering bachelor program at Al-Zaytoonah University of Jordan. The primary objective is to create a comprehensive mapping system that assists academic planning by exploring various scenario combinations. The study utilizes the Apriori algorithm to identify frequent item sets and generate association rules, thus providing a robust approach for predicting academic trajectories. The analysis involves extracting and restructuring data from the study plan, followed by in-depth pattern identification using advanced ML techniques. The results emphasize the importance of incorporating domain knowledge to enhance prediction accuracy and reliability. This study lays the foundation for innovative academic planning tools, offering significant potential for broader applications in educational and other domains. Future work will focus on refining the predictive models and expanding the approach to other educational programs, aiming to further improve the effectiveness of academic planning and decision-making processes.
Keywords:
Machine Learning, Predictive pathways, Apriori algorithm, Data mining, Academic planning component.
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