“Improve the accuracy of students admission at universities using machine learning techniques,”
Abstract
The advancement of technology contributes in the development of many field of life. One of the major fields to focus on is the field of higher education. Actually, Saudi's universities provide free education to the students, so large number of students apply to the universities. In response to that, universities usually maintain admission policies. Universities' admission policies and procedures focus on students Grade Point Average in high school (GPAH), General Aptitude Test (GAT) and Achievement Test (AT). In fact, guiding students to the suitable major improves students' achievements and success. This paper studies the admission criteria for universities in Saudi Arabia. This paper investigates the hidden details that lies behind students' GP AH, GAT and AT. Those details influence the process of students' major selection at universities. Indeed, this research uses machine learning models to include more features such as the grades of high school courses to predict the suitable majors for the students. We use K-Nearest Neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM) to classify students into suitable majors. This process enhances the enrollments of applicants in appropriate majors. Furthermore, the experiments show that KNN gives the highest accuracy rate as it reaches 100%, while DT's accuracy rate is 81 % and SVM's accuracy rate is 75%.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Machine learning has also been used to classify students into suitable majors in an attempt to set them up for academic success.
“Machine learning has also been used to classify students into suitable majors in an attempt to set them up for academic success (Assiri et al., 2022).”
From Proactive and reactive engagement of artificial intelligence methods for education: a review (2023) · cites this paper as Assiri et al., 2022
Cited by
Sorted by citations
Proactive and reactive engagement of artificial intelligence methods for education: a review
Mallik, S., & Gangopadhyay, A. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1151391 · Cited by 139 · see citing works
“Machine learning has also been used to classify students into suitable majors in an attempt to set them up for academic success [41].”
BackgroundFair and Transparent Student Admission Prediction Using Machine Learning Models
Raftopoulos, G., Davrazos, G., & Kotsiantis, S. B. (2024) · Algorithms · doi:10.3390/a17120572 · Cited by 30 · see citing works
“The study by Assiri et al. [18] examined university admissions criteria in Saudi Arabia, with an emphasis on enhancing the process of student major selection through machine learning methodologies.”
Wu, J., & Gu, Y. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.979059 · Cited by 14 · see citing works
Assiri, B., Bashraheel, M., & Alsuri, A. (2024) · Applied Sciences · doi:10.3390/app14031109 · Cited by 11 · see citing works
Self-citationShin, N., Choi, E., & Chang, S. (2024) · Education and Information Technologies · doi:10.1007/s10639-024-12649-4 · Cited by 9 · see citing works
“Similarly, a university in Saudi Arabia leveraged a machine learning algorithm to enhance the accuracy of predicting suitable majors for prospective students (Assiri et al., 2022).”
Li, G., & Jiang, L. (2023) · IEEE Eurasian Conference on Educational Innovation (ECEI) · doi:10.1109/ecei57668.2023.10105416 · Cited by 6 · see citing works
“By using the classification modeling algorithm in machine learning, problems in social science can be transformed into classification prediction problems, and a model with a prediction function can be constructed [5,6].”
MethodMorales, N. O., & García, P. (2024) · European Public & Social Innovation Review · doi:10.31637/epsir-2024-1605 · Cited by 5 · see citing works
Harsono, S., Utami, E., & Yaqin, A. (2024) · doi:10.1109/aims61812.2024.10513089 · Cited by 2 · see citing works
“…subjects preferred while at the school of origin, the carrying capacity of education financing from parents [2], [3], the influence of psychology, gender, the number of job vacancies available, the effect of friendships, and the condition of education policies implemented by the government [4].”
BackgroundWu, Q., Chang, H. J., & Ma, L. (2025) · Journal of Advanced Academics · doi:10.1177/1932202x251362944 · Cited by 1 · see citing works
“Especially, many research has been done on how AI can be used in education in many different capacities (Ai, 2023; Assiri et al., 2022; Attri et al., 2023; Furman, 2024; Jing et al., 2023; Liu, 2023; Mayahi & Al-Bahri, 2020; Poudyal et al., 2020).”
Chowdhury, M., & Alam, S. (2023) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit59222.2023.10205640 · Cited by 1 · see citing works
“The work in [23] utilized machine learningbased (KNN, decision tree, SVM) techniques to improve the student selection accuracy for university admission by taking into account grade point criteria, aptitude tests, achievement test results, and other admission criteria.”
MethodPlăcintă, D.-D. (2022) · Journal of Social and Economic Statistics · doi:10.2478/jses-2022-0007
Niu, S., & Jiang, L. (2023) · IEEE International Conference on Knowledge Innovation and Invention (ICKII) · doi:10.1109/ickii58656.2023.10332603
“[6] proposed using ML methods to improve the accuracy of student profiling.”
BackgroundMethodKNN Algorithm for Predicting MTsN Padang Panjang Students’ to High School Placement
Zahira, R., Gunawan, P. H., & Indwiarti, I. (2025) · International Conference on Data Science and Its Applications (ICoDSA) · doi:10.1109/icodsa67155.2025.11157399
“To minimize failure in the admission selection process, machine learning can be utilized to develop predictive models that provide valuable insights for students and schools in preparing for the selection process to these top high schools [3] [4].”
- Authors
- Assiri, B., Bashraheel, M., & Alsuri, A.
- Affiliations
- Jazan University (SA)
- Venue
- 2022 7th International Conference on Data Science and Machine Learning Applications (CDMA)
- Publisher
- IEEE
- Published
- 2022-03
- Pages
- 127-132
- OpenAlex
- W4220986257
- Cited by
- 13
- References
- 0
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 4 · 2022: 2
- FWCI
- 4.24
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Data Science and Machine Learning Applications (CDMA) (publishedVersion)
- 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, major selection, student admission prediction, grade point average, General Aptitude Test, achievement tests, high school grades, k-nearest neighbors, decision tree, support vector machine, Saudi universities
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
