Keywords

graduate admissions, admissions validation study, academic analytics

Abstract

While both subjective measures and quantitative metrics play an important role in admissions decisions, quantitative metrics are amenable to critical analysis using the tools of academic analytics. The hypotheses that motivated this study are: 1. Can an applicant’s undergraduate grade point average (UGPA) and scores on the Graduate Records Examinations (GRE) be used to accurately predict the performance of the applicant in a graduate mechanical engineering program? 2. Is a single construct based on these quantitative predictive metrics a valuable tool in efficiently making admissions decisions? This study analyzed the relationship between quantitative predictive metrics, available at the time of application to a mechanical engineering graduate program, and quantitative performance assessments measured at the thesis defense. The sample includes 92 students graduating with MS degrees in mechanical engineering from a private university in the United States. The input variables include UGPA, and percentiles for the verbal, quantitative, and written sections of the GRE. The performance metrics were obtained at the thesis defense. They are graduate grade point average, months to graduation, peer-review publication rating, and advisor determined performance rating. Each variable was normalized and the relationship between the predictive metrics and the performance metrics was analyzed statistically. Regression models were created for each performance metric and for a weighted sum of all the performance metrics. The dominant predictors are the UGPA and the score on the quantitative section of the GRE. A quantitative application rating is found to be 5 times the normalized UGPA plus four times the normalized score on the quantitative section of the GRE. Quantitative metrics account for one fifth of the variance in the performance metrics. The Quantitative Application Rating—a single construct based on the quantitative predictive metrics studied—aids in making admissions decisions.

Original Publication Citation

International Journal of Engineering Education Vol. 30, No. 5, pp. 1145–1165, 2014

Document Type

Peer-Reviewed Article

Publication Date

2014-06-23

Permanent URL

http://hdl.lib.byu.edu/1877/3311

Publisher

International Journal of Engineering Education

Language

English

College

Ira A. Fulton College of Engineering and Technology

Department

Mechanical Engineering

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