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DOI: 10.14569/IJACSA.2022.0130937
PDF

Predicting University Student Retention using Artificial Intelligence

Author 1: Samer M. Arqawi
Author 2: Eman Akef Zitawi
Author 3: Anees Husni Rabaya
Author 4: Basem S. Abunasser
Author 5: Samy S. Abu-Naser

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 9, 2022.

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Abstract: Based on the advancement in the field of Artificial Intelligence, there is still a room for enhancement of student university retention. The main objective of this study is to assess the probability of using Artificial Intelligence techniques such as deep and machine learning procedures to predict university student retention. In this study a variable assessment is carried out on the dataset which was collected from Kaggle repository. The performance of twenty supervised algorithms of machine learning and one algorithm of deep learning is assessed. All algorithms were trained using 10 variables from 1100 records of former university student registrations that have been registered in the University. The top performing algorithm after hyper-parameters tuning was NuSVC Classifier. Therefore, we were able to use the current dataset to create supervised Machine Learning (ML) and Deep Learning (DL) models for predicting student retention with F1-score (90.32 percent) for ML and the proposed DL algorithm with F1-score (93.05 percent).

Keywords: Artificial intelligence; machine learning; deep learning; retention; student; prediction

Samer M. Arqawi, Eman Akef Zitawi, Anees Husni Rabaya, Basem S. Abunasser and Samy S. Abu-Naser, “Predicting University Student Retention using Artificial Intelligence” International Journal of Advanced Computer Science and Applications(IJACSA), 13(9), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130937

@article{Arqawi2022,
title = {Predicting University Student Retention using Artificial Intelligence},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130937},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130937},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {9},
author = {Samer M. Arqawi and Eman Akef Zitawi and Anees Husni Rabaya and Basem S. Abunasser and Samy S. Abu-Naser}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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