| Author | Regmi, Bibhuti |
| Note | A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of
Master of Engineering in Information Management |
| Publisher | Asian Institute of Technology |
| Abstract | The trend of using data analytics in fields like retail industry, medicine, politics, tourism is gradually increasing. But in comparison to other fields, data analytics is a relatively new topic in the educational sector. The university has accumulated large amounts of student
data for years. However, this data is typically not put in use. Education sector can reap a lot
of benefits using data analytics. Despite the availability of data and benefits of using analytics in admission data of students, there are still few tools and techniques to do so. Further
research in data analytics in the education domain has a huge scope. Thus, exploration of
detailed data analytics process in this area is necessary.
This study discusses the previous research done in student admission and enrollment along with various techniques and tools used for student recruitment and admission. It discusses
the decisions that are performed by the three level of management in student admission and recruitment domain. It also explores the use of descriptive and predictive analytic to analyse enrollment patterns in student admission, group similar countries using clustering, find relationship between students attributes with association technique, predict student enrollment
decision using machine learning techniques and visualize past data using google data studio. |
| Year | 2020 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Information and Communications Technologies (DICT) |
| Academic Program/FoS | Information Management (IM) |
| Chairperson(s) | Chutiporn Anutariya; |
| Examination Committee(s) | Anwarm Naveed Vatcharaporn Esichaikul; |
| Scholarship Donor(s) | AIT Fellowship; |