Evaluating Letters of Reference to Engineering Doctoral Programs for Racial and Gender Bias

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Advanced training and education in STEM are of critical importance for the nation’s workforce and economic status. Equitable access to graduate level training reduces barriers for individuals from minoritized groups. Standardized graduate exams are becoming less important in the graduate admissions process, which places a higher weight on letters of recommendation for the selection of graduate students for a program. This project focuses on examining the language used by mentors in describing students in letters of recommendations and seeks to identify racial and gender bias in the application of key words or phrases that may perpetuate enrollment disparities. The professional development plan will build capacity in engineering education research through development of skills, methods, and text-based data analyses.The goals of the project are to identify differences in the way an applicant’s potential for success in engineering is described in letters of recommendation based on their gender and race/ethnicity and develop a deeper understanding of disciplinary practices, values, and norms while building skills in text-based data analysis. Founded on role congruity theory and stereotype content model theory, the project will use qualitative methods, content analysis, and natural language processing techniques to identify and compare language used in letters of recommendation. The project aims to produce a model engineering graduate admissions landscape that is free of race and gender biases than can be used for similar investigations of other disciplines. The project is supported by NSF's EHR Core Research Building Capacity in STEM Education Research (ECR:BCSER) program, which is designed to build investigators’ capacity to carry out high-quality STEM education research.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.