A Predictive Model for Graduate Application to Enrollment - Open Access Library Journal

OALibJ > Vol.5 No.4, April 2018

A Predictive Model for Graduate Application to Enrollment ()

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DOI: 10.4236/oalib.1104499 1,636 Downloads 4,319 Views Citations

Author(s)

Vahid Lotfi^1*, Bradley Maki²

Affiliation(s)

¹School of Management, University of Michigan Flint, Flint, Michigan, USA.
²Office of Graduate Programs, University of Michigan Flint, Flint, Michigan, USA.

ABSTRACT

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.

KEYWORDS

Graduate Education, Student Recruiting, Predictive Modeling, Enrollment Management

Share and Cite:

Lotfi, V. and Maki, B. (2018) A Predictive Model for Graduate Application to Enrollment. Open Access Library Journal, 5, 1-19. doi: 10.4236/oalib.1104499.

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