Predicting Student Enrolments and Attrition Patterns
in Higher Educational Institutions using Machine Learning
Samar Shilbayeh and Abdullah Abonamah
Business Analytics
Department, Abu Dhabi School of Management, UAE
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.
Keywords: Machine learning, predictive model, apriori algorithm,
student retention, enrolment behaviour, association rule mining, boosting,
ensemble method.
Received March 10, 2020;
accepted July 19, 2020