Islam Suvon, M.N. orcid.org/0000-0001-9962-315X, Siam, S.C. orcid.org/0000-0003-3557-1340, Ferdous, M. orcid.org/0000-0001-8581-8183 et al. (2 more authors) (2022) Masters and Doctor of Philosophy admission prediction of Bangladeshi students into different classes of universities. IAES International Journal of Artificial Intelligence (IJ-AI), 11 (4). pp. 1545-1553. ISSN 2089-4872
Abstract
Many Bangladeshi students intend to pursue higher studies abroad after completing their undergraduate degrees every year. Choosing a university for higher education is a challenging task for students. Especially, the students with average and lower academic credentials (undergraduate grades, English proficiency test scores, job, and research experiences) can hardly choose the universities that could match their profile. In this paper, we have analyzed some real unique data of Bangladeshi students who had been accepted admissions at different universities worldwide for higher studies. Finally, we have produced prediction models based on random forest (RF) and decision tree (DT) techniques, which can predict appropriate universities of specific classes for students according to their past academic performances. Two separate models have been studied in this paper, one for Masters (MS)students and another for Doctor of Philosophy (PhD)students. According to the Quacquarelli Symonds (QS) World University Rankings, the universities where the students got admitted have been divided into 9 classes for MS students and 8 classes for PhD students. Accuracy, precision, recall and F1-Score have been studied for the two machine learning algorithms. Numerical results show that both the algorithm DT and RF have the same accuracy of 89% for PhD student data and 86% for MS student data.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 The Authors. This is an Open Access article distributed under the terms of the Creative Commons Attribution-ShareAlike Licence (https://creativecommons.org/licenses/by-sa/4.0/) |
Keywords: | Data normalization; Decision tree; Educational data mining ; Oversampling; Random forest |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 24 May 2023 09:58 |
Last Modified: | 24 May 2023 09:58 |
Status: | Published |
Publisher: | Institute of Advanced Engineering and Science |
Refereed: | Yes |
Identification Number: | 10.11591/ijai.v11.i4.pp1545-1553 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:199499 |