METRICS FIRST, DIVERSITY LATER? MAKING THE SHORT LIST AND GETTING ADMITTED TO PHYSICS PhD PROGRAMS

Posselt, Julie R.; Hernandez, Theresa E.; Cochran, Geraldine L.; Miller, Casey W.

doi:10.1615/jwomenminorscieneng.2019027863

Cited by 27 publications

(21 citation statements)

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“…The Graduate Record Examination, developed and administered by the private nonprofit Educational Testing Service, is a standardized test used by many graduate institutions as a means of evaluating applicants. The GRE is commonly used and trusted during the admissions process [16], likely because early research [17,18] indicated that high GRE scores correlated with success metrics (e.g., likelihood of passing qualifying exams, degree completion, research productivity). However, more recent research contradicts and complicates those findings.…”

Section: Gre Scoring Is a Poor Metric For Career Successmentioning

confidence: 99%

“…In a qualitative study of students with underrepresented identities eligible for the American Physical Society's Bridge program, a third of the graduates, who were interested in applying to physics PhD programs but chose not to, cited GRE scores as a reason. Analysis of admissions decisions for six large physics PhD programs in 2019 revealed that GRE and physics subject GRE scores significantly predicted whether the applicant was shortlisted and admitted [16]. Once shortlisted, applicants with underrepresented racial and ethnic identities were much more likely to be admitted than White applicants.…”

Section: Gre Scoring Is a Poor Metric For Career Successmentioning

confidence: 99%

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Review of Racially Equitable Admissions Practices in STEM Doctoral Programs

et al. 2021

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This study reviews literature on racially equitable admissions practices relevant to graduate programs in STEM. Graduate Record Exam (GRE) scores correlate more strongly with race, gender, and socioeconomic status than performance metrics for research during or after graduate school. Structural changes to admissions processes that can improve equity of admissions decisions and reduce correlations between admissions decisions and demographic data include using holistic review or composite scores that quantize more components of an application, removing hard limits on course requirements, admitting students as a cohort instead of to individual faculty sponsors, and diversifying admissions committees. Some alternative scoring methods attempt to measure personality traits, but performing these measurements during admissions may present difficulties. Bridge programs—whether they are implemented as collaborations with a minority-serving institution, a personalized educational program for each student admitted to a program, or a stand-alone program before the doctoral degree program—may be able to improve both recruitment and retention of students with underrepresented racial and ethnic identities in their field of study. Finally, financial barriers to applications can disproportionately affect underrepresented applicants due to systemic racism. We end with recommendations for graduate programs to improve equity in admissions processes.

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Section: Gre Scoring Is a Poor Metric For Career Successmentioning

confidence: 99%

Section: Gre Scoring Is a Poor Metric For Career Successmentioning

confidence: 99%

Review of Racially Equitable Admissions Practices in STEM Doctoral Programs

et al. 2021

View full text Add to dashboard Cite

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“…Previous research indicates that the Graduate Record Exam is a barrier to participation in graduate education [33,[51][52][53]. The statement of AAPT recommending that the GRE be eliminated from the admissions process is an effort to remove the barrier [54].…”

Section: Removing Barriersmentioning

confidence: 99%

A framework for improving diversity work in physics

Cochran

Boveda²

2020

2020 Physics Education Research Conference Proceedings

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In this paper we draw on Black Feminist Theory to motivate examining diversity work in physics with an emphasis on physics education research. In our framework, we consider three major approaches to doing diversity work acknowledging and naming diversity, affirming diversity, and removing barriers to increase diversity. We examine these approaches with goals of attending to the affordances of existing diversity work and identifying the constraints for those currently seeking to diversify the field. We use intersectionality to critique diversity efforts in physics and conclude with suggestions on elevating and complicating diversity work in physics and physics education research.

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“…We chose to apply a random forest model instead of a more traditional technique for classifying data such as logistic regression (as used by Attiyeh and Attiyeh [22] and Posselt et al [7] to study graduate admissions) due to these feature importances. As feature importances measure all factors on the same scale, that is how much they change the area under the curve, factors of otherwise different scales can be compared.…”

Section: Random Forest Modelmentioning

confidence: 99%

“…Missing in this analysis is an investigation of the actual applications of prospective physics graduate students. To our knowledge, there has only been one such study [7].…”

Section: Introductionmentioning

confidence: 99%

Using machine learning to understand physics graduate school admissions

Young¹,

Caballero²

2020

2019 Physics Education Research Conference Proceedings

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Among all of the first-year graduate students enrolled in doctoral-granting physics departments, the percentage of female and racial minority students 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 paper, 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, research interests, and GRE scores. Because the collected data varied in scale, we used supervised machine learning algorithms to create models that predict who was admitted into the PhD program. We find that using only the applicant's undergraduate GPA and physics GRE score, we are able to predict with 75% accuracy who will be admitted to the program.

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METRICS FIRST, DIVERSITY LATER? MAKING THE SHORT LIST AND GETTING ADMITTED TO PHYSICS PhD PROGRAMS

Cited by 27 publications

References 0 publications

Review of Racially Equitable Admissions Practices in STEM Doctoral Programs

Review of Racially Equitable Admissions Practices in STEM Doctoral Programs

A framework for improving diversity work in physics

Using machine learning to understand physics graduate school admissions

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