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Dataset of student level prediction in UAE
A primary dataset is presented comprising student grading records and educational diversity information. The dataset is collected from two international schools, a British curriculum, and an American Curriculum schools based in Abu Dhabi, United Arab Emirates. Following the ethical approval from Liv...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937958/ https://www.ncbi.nlm.nih.gov/pubmed/33732825 http://dx.doi.org/10.1016/j.dib.2021.106908 |
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author | Ghareeb, Shatha Hussain, Abir Khan, Wasiq Al-Jumeily, Dhiya Baker, Thar Al-Jumeily, Rawaa |
author_facet | Ghareeb, Shatha Hussain, Abir Khan, Wasiq Al-Jumeily, Dhiya Baker, Thar Al-Jumeily, Rawaa |
author_sort | Ghareeb, Shatha |
collection | PubMed |
description | A primary dataset is presented comprising student grading records and educational diversity information. The dataset is collected from two international schools, a British curriculum, and an American Curriculum schools based in Abu Dhabi, United Arab Emirates. Following the ethical approval from Liverpool John Moores University (19/CMS/001), the data is collected through gatekeepers. A permission letter was granted from the Ministry of Education and Knowledge in Abu Dhabi, UAE to provide access to the schools. The dataset is anonymised by eliminating sensitive and identifiable students’ information and prepared to be used for pattern analysis and prediction of student grading based on diverse educational backgrounds that might be useful for automated student levelling, i.e., at which level the student needs to be entered when moved from a different school with different international curriculum. |
format | Online Article Text |
id | pubmed-7937958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-79379582021-03-16 Dataset of student level prediction in UAE Ghareeb, Shatha Hussain, Abir Khan, Wasiq Al-Jumeily, Dhiya Baker, Thar Al-Jumeily, Rawaa Data Brief Data Article A primary dataset is presented comprising student grading records and educational diversity information. The dataset is collected from two international schools, a British curriculum, and an American Curriculum schools based in Abu Dhabi, United Arab Emirates. Following the ethical approval from Liverpool John Moores University (19/CMS/001), the data is collected through gatekeepers. A permission letter was granted from the Ministry of Education and Knowledge in Abu Dhabi, UAE to provide access to the schools. The dataset is anonymised by eliminating sensitive and identifiable students’ information and prepared to be used for pattern analysis and prediction of student grading based on diverse educational backgrounds that might be useful for automated student levelling, i.e., at which level the student needs to be entered when moved from a different school with different international curriculum. Elsevier 2021-02-24 /pmc/articles/PMC7937958/ /pubmed/33732825 http://dx.doi.org/10.1016/j.dib.2021.106908 Text en © 2021 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Data Article Ghareeb, Shatha Hussain, Abir Khan, Wasiq Al-Jumeily, Dhiya Baker, Thar Al-Jumeily, Rawaa Dataset of student level prediction in UAE |
title | Dataset of student level prediction in UAE |
title_full | Dataset of student level prediction in UAE |
title_fullStr | Dataset of student level prediction in UAE |
title_full_unstemmed | Dataset of student level prediction in UAE |
title_short | Dataset of student level prediction in UAE |
title_sort | dataset of student level prediction in uae |
topic | Data Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7937958/ https://www.ncbi.nlm.nih.gov/pubmed/33732825 http://dx.doi.org/10.1016/j.dib.2021.106908 |
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