This study involved an investigation of factors that
affect a graduate applicant in accepting an offer of admission and enrolling in
a graduate program of study at a mid-sized public university. A predictive
model was developed, using Decision Tree methodology to assess the probability
that an admitted student would enroll in the program during the semester following
acceptance. The study included actual
application information such as demographic information, distance from the
campus, program of interest, tests scores, financial aid, and other pertinent
application items of over 4600 graduate applications over a three-year period.
The Decision Tree model was then compared with a Bayesian Network model to
reaffirm its validity and its predictive power. The method with the more
promising outcome was used to develop predictive models for applicants
interested in a sample of academic majors. The results of the predictive models
were used to illustrate development of recruitment strategies for all
applicants as well as for those interested in specific majors.
Cite this paper
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