A recommender system for predicting students' admission to a graduate program using machine learning algorithms
Open accessAbstract
In the 21st century, University educations are becoming a key pillar of social and economic life. It plays a major role not only in the educational process but also in the ensuring of two important things which are a prosperous career and financial security. However, predicting university admission can be especially difficult because the students are not aware of admission requirements. For that reason, the main purpose of this research work is to provide a recommender system for early predicting university admission based on four Machine Learning algorithms namely Linear Regression, Decision Tree, Support Vector Regression, and Random Forest Regression. The experimental results showed that the Random Forest Regression is the most suitable Machine Learning algorithm for predicting university admission. Also, the Cumulative Grade Point Average (CGPA) is the most important parameter that influences the chance of admission.
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More than one work finds prior GPA of students to be the most significant factor in admissions decisions.
“Notably, more than one such work finds prior GPA (Grade Point Average) of students to be the most significant factor in admissions decisions (Young and Caballero, 2019; El Guabassi et al., 2021).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as El Guabassi et al., 2021
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Research works printed but not linked: 1
- El Guabassi, I., Al Achhab, M., Jellouli, I., &Mohajir, B. E. E. (2018). Personalized ubiqui- tous learning via an adaptive engine. International Journal of Emerging Technologies in Learning (iJET), 13(12), 177-190. https://doi.org/10.3991/ijet.v13i12.7918 · DOI
Cited by
Sorted by citations
Proactive and reactive engagement of artificial intelligence methods for education: a review
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- Authors
- El Guabassi, I., Bousalem, Z., Marah, R., & Qazdar, A.
- Affiliations
- Université Hassan 1er (MA)
- Cadi Ayyad University (MA)
- Venue
- International Journal of Online and Biomedical Engineering (iJOE)
- Published
- 2021-02-12
- Volume
- 17
- Issue
- 02
- Pages
- 135-147
- ISSN
- 2626-8493 (ISSN-L)
- OpenAlex
- W3133151839
- Cited by
- 32
- References
- 21
- Citations by year
- 2025: 5 · 2024: 7 · 2023: 13 · 2022: 5 · 2021: 2
- FWCI
- 5.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Online and Biomedical Engineering (iJOE) (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Edcuational Technology Systems
- Keywords
- university admission prediction, graduate program admission, recommender systems, student admission, cumulative grade point average, random forest regression, support vector regression, regression trees, linear regression, admission requirements
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