Looking Back on Graduate BME Admissions Data: Lessons Learned and Implications for Holistic Review and Diversity

Abstract

Graduate school applications in Biomedical Engineering (BME) are steadily rising, making competition stiffer, applications more complex, and reviews more resource intensive. Holistic reviews are being increasingly adopted to support increased diversity, equity, and inclusion in graduate student BME admissions, but which application metrics are the strongest predictors of admission and enrollment into BME programs remains unclear. In this perspectives article, we aim to shed light on some of the key predictors of student acceptance in graduate school. We share data from a three-year retrospective review of our own institution's graduate BME applications and admission rates and review the influence of grade point averages (GPA), standardized test scores (e.g., GRE), and prior research experience on graduate school admission rates. We also examine how the waiver of GRE requirements has changed the landscape of BME graduate applications in recent years. Finally, we discuss efforts taken by our institution and others to develop and implement holistic reviews of graduate applications that encourage students from underrepresented backgrounds to apply and successfully gain admission to graduate school. We share five key lessons we learned by performing the retrospective review and encourage other institutions to "self-reflect" and examine their historical graduate admissions data and past practices. Efforts aimed at engaging faculty to overcome their own implicit biases, engaging with underrepresented students in hands-on, research-intensive programs, and networking with diverse student populations have strong potential to enhance the diversity of BME graduate programs and our STEM workforce.

Supplementary information: The online version contains supplementary material available at 10.1007/s43683-022-00080-5.

Keywords: Biomedical engineering; Diversity; GRE; Graduate admissions; Holistic reviews; Networking; REU; Underrepresentation.

Figure 1

Illustration of associations between applicant metrics and Graduate BME program Acceptance, Financial Offer and Enrollment. Bars represent the three outcomes: graduate program acceptance (black), program acceptance with a financial offer (gray), and enrollment (gray striped) into the BME graduate program. Asterisks represent the strongest Nagelkerke R² estimates that were statistically significant (p < 0.05). “How hear” refers to the categorical variable related to how the applicant’s heard about the program and were categorized as: Online (referent group), Conference, Department Research, Faculty, Internship-REU, Peer and Student-Alumni and Other. How students heard about the program returned Nagelkerke R² values of 0.40 (p < 0.0001), 0.37 (p = 0.0002) and 0.37 (p = 0.007) for admissions, financial offers, and enrollment respectively. Hence, nearly 40% of the variation in program acceptance is explained by how the students heard about the program. This variable was the strongest predictor of graduate admissions (stronger than GPA and standardized GRE scores). Prior research experience coded as a binary variable (i.e., yes/no) yielded a low Nagelkerke R², presumably because > 90% of our applicant pool indicated that they had prior research experience. Clearly REU-type experiences were strongly associated with graduate admissions. Full description of our statistical methods have been provided as a supplement.

Figure 2

Number of graduate applicants and acceptance rates between 2016 and 2020. We performed a retrospective review of our graduate admissions from 2016 to 2018 and then implemented changes to recruitment methods, waiving the need for GRE scores and adoption of more holistic reviews at the end of 2018. We’ve observed significant improvements in recruitment of URMs between 2019 and 2021. We offered 9 (28%) acceptance letters to URM students in 2019, 16 (48%) in 2020 and 24 (40%) in 2021.