Abstract

UAE has made significant progress in the field of education, including Higher Education, by attracting students from all around the world to various colleges and universities. Student dropout or attrition is a major issue that is faced by Higher Education Institutions (HEI). Hence, we need effective techniques to identify student data that affects attrition. This research aims to explore the use of machine learning algorithms to predict student attrition at Rochester Institute of Technology- Dubai (RIT Dubai). In this research, the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology was used as it is widely used in projects based on data mining and machine learning. Using IBM SPSS Modeler and SPSS Statistics, various machine learning models including Random Trees, Logistic Regression, Linear Support Vector Machines (LSVM), and Neural Networks were explored to find the most suitable predictive model that could accurately find student attrition and support in providing early intervention to mitigate this problem at the HEI. The logistic regression model provided the best results with the highest accuracy, AUC, precision, and recall at 1.0. The research involves analyzing student data such as demographics, results of language tests, data on academic performance, acceptance and start dates, socioeconomic factors, and other contextual data from RIT Dubai. The results of the study provide a clear understanding of the most significant predictors of student attrition that were found through machine learning. This will prove to be an important instrument for RIT Dubai to improve student retention rates.

Publication Date

5-2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Professional Studies (MS)

Department, Program, or Center

Graduate Programs & Research

Advisor

Sanjay Modak

Advisor/Committee Member

Hammou Messatfa

Campus

RIT Dubai

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