Dataset of Student Level Prediction in UAE
The dataset comprises novel aspects specifically, in terms of student grading in diverse educational cultures within the multiple countries – Researchers and other education sectors will be able to see the impact of having varied curriculums in a country. Dataset compares different levelling cases when student transfer from curriculum to curriculum and the unreliable levelling criteria set by schools currently in an international school. The collected data can be used within the intelligent algorithms specifically machine learning and pattern analysis methods, to develop an intelligent framework applicable in multi-cultural educational systems to aid in a smooth transition “levelling, hereafter” of students who relocate from a particular education curriculum to another; and minimize the impact of switching on the students’ educational performance. The preliminary variables taken into consideration when deciding which data to collect depended on the variables. UAE is a multicultural country with many expats relocating from regions such as Asia, Europe and America. In order to meet expats needs, UAE has established many international private schools, therefore UAE was chosen to be the location of study based on many cases and struggles in levelling declared by the Ministry of Education and schools. For the first time, we present this dataset comprising students’ records for two academic years that included math, English, and science for 3 terms. Selection of subject areas and number of terms was based on influence from other researchers in similar subject matters.
Steps to reproduce
We present a primary dataset 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, UAE. The data is collected through gatekeepers. A permission letter was granted from the Ministry of Education and Knowledge in Abu Dhabi to access the schools for this research. The dataset is anonymized 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.