Predicting Student Enrolments and Attrition Patterns in Higher Educational Institutions using Machine Learning
Samar Shilbayeh, Abdullah A. Abonamah
Abstract
In higher educational institutions, student enrollment management and increasing student retention are fundamental performance metrics to academic and financial sustainability. In many educational institutions, high student attrition rates are due to a variety of circumstances, including demographic and personal factors such as age, gender, academic background, financial abilities, and academic degree of choice. In this study, we will make use of machine learning approaches to develop prediction models that can predict student enrollment behavior and the students who have a high risk of dropping out. This can help higher education institutions develop proper intervention plans to reduce attrition rates and increase the probability of student academic success. In this study, real data is taken from Abu Dhabi School of Management (ADSM) in the UAE. This data is used in developing the student enrollment model and identifying the student's characteristics who are willing to enroll in a specific program, in addition to that, this research managed to find out the characteristics of the students who are under the risk of dropout.