PERC 2019 Abstract Detail Page
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| Abstract Title: | Using Machine Learning to Understand Physics Graduate School Admissions |
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| Abstract: | Among all of the first-year graduate students enrolled in doctoral-granting physics departments, the percentage of women and underrepresented minorities has remained unchanged for the past 20 years. The current graduate program admissions process can create challenges for achieving diversity goals in physics. In this presentation, we will investigate how the various aspects of a prospective student's application to a physics doctoral program affect the likelihood the applicant will be admitted. Admissions data was collected from a large, Midwestern public research university that has a decentralized admissions process and included applicants' undergraduate GPAs and institutions and GRE and physics GRE scores. Supervised machine learning algorithms were used to create models that predict who was admitted into the PhD program. Here, we will present the results of this analysis as well as compare models between the various subdisciplines of physics represented in this department. |
| Abstract Type: | Contributed Poster Presentation |
| Session Time: | Poster Session II |
| Poster Number: | B18 |
| Contributed Paper Record: | Contributed Paper Information |
| Contributed Paper Download: | Download Contributed Paper |
Author/Organizer Information | |
| Primary Contact: |
Nicholas T Young Department of Physics and Astronomy, Michigan State University |
| Co-Author(s) and Co-Presenter(s) |
Marcos D. Caballero Center for Computing in Science Education & Department of Physics, University of Oslo, Department of Physics and Astronomy, Michigan State University, CREATE for STEM Institute, Michigan State University |
Contributed Poster | |
| Contributed Poster: | Download the Contributed Poster |
