Abstract
In Abu Dhabi, there are many different education
curriculums where sector of private schools and quality assurance is
supervising many private schools in Abu Dhabi for many
nationalities. As there are many different education curriculums in
Abu Dhabi to meet expats’ needs, there are different requirements for
registration and success. In addition, there are different age groups
for starting education in each curriculum. In fact, each curriculum has
a different number of years, assessment techniques, reassessment
rules, and exam boards. Currently, students that transfer curriculums
are not being placed in the right year group due to different start and
end dates of each academic year and their date of birth for each year
group is different for each curriculum and as a result, we find
students that are either younger or older for that year group which
therefore creates gaps in their learning and performance. In addition,
there is not a way of storing student data throughout their academic
journey so that schools can track the student learning process. In this
paper, we propose to develop a computational framework applicable
in multicultural countries such as UAE in which multi-education
systems are implemented. The ultimate goal is to use cloud and fog
computing technology integrated with Artificial Intelligence
techniques of Machine Learning to aid in a smooth transition when
assigning students to their year groups, and provide levelling and
differentiation information of students who relocate from a particular
education curriculum to another, whilst also having the ability to
store and access student data from anywhere throughout their
academic journey.
curriculums where sector of private schools and quality assurance is
supervising many private schools in Abu Dhabi for many
nationalities. As there are many different education curriculums in
Abu Dhabi to meet expats’ needs, there are different requirements for
registration and success. In addition, there are different age groups
for starting education in each curriculum. In fact, each curriculum has
a different number of years, assessment techniques, reassessment
rules, and exam boards. Currently, students that transfer curriculums
are not being placed in the right year group due to different start and
end dates of each academic year and their date of birth for each year
group is different for each curriculum and as a result, we find
students that are either younger or older for that year group which
therefore creates gaps in their learning and performance. In addition,
there is not a way of storing student data throughout their academic
journey so that schools can track the student learning process. In this
paper, we propose to develop a computational framework applicable
in multicultural countries such as UAE in which multi-education
systems are implemented. The ultimate goal is to use cloud and fog
computing technology integrated with Artificial Intelligence
techniques of Machine Learning to aid in a smooth transition when
assigning students to their year groups, and provide levelling and
differentiation information of students who relocate from a particular
education curriculum to another, whilst also having the ability to
store and access student data from anywhere throughout their
academic journey.
| Original language | English |
|---|---|
| Pages (from-to) | 1181-1184 |
| Number of pages | 4 |
| Journal | World Academy of Science, Engineering and Technology International Journal of Humanities and Social Sciences |
| Volume | 14 |
| Issue number | 12 |
| Publication status | Published - 20 Mar 2020 |
| Externally published | Yes |