Dataset of student level prediction in UAE

Shatha Ghareeb; Abir Hussain; Wasiq Khan; Dhiya Al-Jumeily; Thar Baker; Rawaa Al-Jumeily

doi:10.1016/j.dib.2021.106908

Dataset of student level prediction in UAE

Data Brief. 2021 Feb 24:35:106908. doi: 10.1016/j.dib.2021.106908. eCollection 2021 Apr.

Authors

Shatha Ghareeb¹, Abir Hussain¹, Wasiq Khan¹, Dhiya Al-Jumeily¹, Thar Baker², Rawaa Al-Jumeily¹

Affiliations

¹ Liverpool John Moores University, United Kingdom.
² University of Sharjah, United Arab Emirates.

Abstract

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.

Keywords: Artificial intelligence; Education; Levelling; School curriculum; Student grade prediction; Student tracking.