Modeling nurse-entry success by integrating student characteristics with gateway course performance



Journal Title

Journal ISSN

Volume Title



Decades of undergraduate retention research has uncovered common predictors of student success and methods, such as early-alert intervention, that promote persistence among academically at-risk students. This study addresses low retention rates among nurse-entry majors enrolled at Texas Woman's University (TWU) by integrating a gateway course performance variable with other student characteristic variables and using predictive analytics to identify at-risk nurse-entry students after their first semester of coursework. A logistic regression model was built to predict the probability of being admitted into the upper division nursing program after persisting for two to three years. Significant interaction between gateway course performance and first-semester GPA was detected, conveying valuable insight into the odds of success for these students based on first semester behavior. Because this model exhibits exceptional predictive accuracy, it may realistically serve as a basis for early-alert intervention programs among TWU nurse-entry students in the future.



Undergraduate retention, Retention, Student success, Early-alert, Intervention, At-risk, Nurse-entry, Nursing, Gateway course, Gateway course performance, Logistic regression, Predictive modeling, Predictive analytics, Upper division nursing, Persistence