A Predictive Model That Aligns Admission Offers with Student Enrollment Probability

Abstract

This study develops a process that helps admission committees of higher education institutions select interested and qualified students. This enables institutions to maintain their financial viability by reaching the quota given by the Education Administration of Taiwan. We aimed to predict the decision-making behavior of students in terms of enrollment. A logistic regression analysis was conducted on publicly and inexpensively accessible data; the selection criteria of the model are based on metrics from a confusion matrix comprising predicted and observed data. The results indicate a matching rate of close to 80% between the training data of a target university from 2018 to 2020 and the testing data from 2021. This system outputs a probability that the student will enroll and thus helps admission committees more effectively select students.