Recruiting prospective students efficiently and effectively is a very important challenge for universities, mainly because of the increasing competition and the relevance of enrollment-generated revenues. This work provides an intelligent system for modeling the student enrollment decisions problem. A nested logit classifier was constructed to predict which prospective students will eventually enroll in different Bachelor degree programs of a small-sized, private Chilean university. Feature selection is performed to identify the key features that influence the student decisions, such as socio-demographic variables (gender, age, school type, among others), admission efforts, and admission test results. Our results suggest that on-campus activities are far more productive than career fairs and other efforts performed off campus, demonstrating the importance of bringing prospective students to the university