Dataset of student level prediction in UAE

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

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number106908
JournalData in Brief
Volume35
DOIs
Publication statusPublished - 24 Feb 2021

Bibliographical note

Funding Information:
I wish to acknowledge the school principal and the entire team for the support in establishing the data. I am grateful for the Ministry of Education and Knowledge in Abu Dhabi for granting me access to the school and allowed this research to take place. I wish to acknowledge my supervision team for guiding me and providing me with vital information.

Publisher Copyright:
© 2021 The Authors

Keywords

  • Artificial intelligence
  • Education
  • Levelling
  • School curriculum
  • Student grade prediction
  • Student tracking

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