Abstract
Aim/Purpose: Student dropout in higher education institutions is a universal problem. This study identifies the characteristics of dropout. In addition, it develops a mathematical model to predict students who may dropout. Methodology: The paper develops a mathematical model to predict students who may dropout. The sample includes 555 freshmen in a non-profit private university. The study uses both descriptive statistics, such as cross tabulation, and a binary regression model to predict student dropout. Contribution: There are two major contributions for the paper. First, it identifies the dropout rates of each group, a finding that may be used to better allocate resources at higher education institutions. Second, it develops a predictive model that may be used in order to predict the probability of a student dropping out and take preventive actions. Findings: This study compared dropout rates of one and a half year of enrollment among Traditional Undergraduate Students. Two major findings are the following: (1) Some of the resources designed to assist student are misallocated, and (2) Predictive models can be used to calculate the probability of a student dropping out. Recommendations for Practitioners: The study recommends that institutions must create initiatives to assist freshmen students and have annual assessment to measure the success of the initiatives. Recommendation for Researchers: Two, mathematical models may be used to predict dropout rates, the paper includes a model that predicted with 66.6% accuracy students who will dropout.