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Inequity and College Applications: Assessing Differences and Disparities in Letters of Recommendation from School Counselors with Natural Language Processing

Letters of recommendation from school counselors are required to apply to many selective colleges and universities. Still, relatively little is known about how this non-standardized component may affect equity in admissions. We use cutting-edge natural language processing techniques to algorithmically analyze a national dataset of over 600,000 student applications and counselor recommendation letters submitted via the Common App platform. We examine how the length and topical content of letters (e.g., sentences about Personal Qualities, Athletics, Intellectual Promise, etc.) relate to student self-identified race/ethnicity, sex, and proxies for socioeconomic status. Paired with regression analyses, we explore whether demographic differences in letter characteristics persist when accounting for additional student, school, and counselor characteristics, as well as among letters written by the same counselor and among students with comparably competitive standardized test scores. We ultimately find large and noteworthy naïve differences in letter length and content across nearly all demographic groups, many in alignment with known inequities (e.g., many more sentences about Athletics among White and higher-SES students, longer letters and more sentences on Personal Qualities for private school students). However, these differences vary drastically based on the exact controls and comparison groups included – demonstrating that the ultimate implications of these letter differences for equity hinges on exactly how and when letters are used in admissions processes (e.g., are letters evaluated at face value across all students, or are they mostly compared to other letters from the same high school or counselor?). Findings do not point to a clear recommendation whether institutions should keep or discard letter requirements, but reflect the importance of reading letters and overall applications in the context of structural opportunity. We discuss additional implications and possible recommendations for college access and admissions policy/practice.

Keywords
holistic admissions, selective admissions, college access, educational inequity, letters of recommendation, natural language processing, nlp
Education level
Document Object Identifier (DOI)
10.26300/pmv2-r349
EdWorkingPaper suggested citation:
Kim, Brian Heseung, Julie J. Park, Pearl Lo, Dominique J. Baker, Nancy Wong, Stephanie Breen, Huong Truong, Jia Zheng, Kelly Ochs Rosinger, and OiYan Poon. (). Inequity and College Applications: Assessing Differences and Disparities in Letters of Recommendation from School Counselors with Natural Language Processing. (EdWorkingPaper: -953). Retrieved from Annenberg Institute at Brown University: https://doi.org/10.26300/pmv2-r349

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