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A Model to Predict Student Matriculation from Admissions Data

Khajuria, Saket

Abstract Details

2007, Master of Science (MS), Ohio University, Industrial and Manufacturing Systems Engineering (Engineering).

Enrollment in a university can be increased by changing any of the following three attributes: the applicant pool size, the marketing strategies for applicants, or the admission standards. Universities are using different predictive modeling techniques to increase enrollment, given their changing demographics, tuition rates, and other factors. This document presents a model for predicting the likelihood that a specific undergraduate applicant will matriculate if admitted. A regression model was built using data on the applicants for the 2004 freshman class. Using this model, applicants from 2005 were evaluated, and matriculation was predicted. Applicants predicted to matriculate did so 47.91% of the time, while students projected not to matriculate only matriculated 28.05% of the time. Considering all four possible outcomes (correct matriculation prediction to incorrect non-matriculation decision), the overall accuracy was 60.2%. The accuracy of the model was similar to studies in the literature.

David Koonce (Advisor)
88 p.

Recommended Citations

Citations

  • Khajuria, S. (2007). A Model to Predict Student Matriculation from Admissions Data [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960

    APA Style (7th edition)

  • Khajuria, Saket. A Model to Predict Student Matriculation from Admissions Data. 2007. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960.

    MLA Style (8th edition)

  • Khajuria, Saket. "A Model to Predict Student Matriculation from Admissions Data." Master's thesis, Ohio University, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1167852960

    Chicago Manual of Style (17th edition)