There is substantial evidence of the relationship between household income and achievement on the standardized tests often required for college admissions, yet little comparable inquiry considers the essays typically required of applicants to selective U.S. colleges and universities. We used a corpus of 240,000 admission essays submitted by 60,000 applicants to the University of California in November 2016 to measure relationships between the content of admission essays, self-reported household income, and SAT scores. We quantified essay content using correlated topic modeling and essay style using Linguistic Inquiry and Word Count. We found that essay content and style had stronger correlations to self-reported household income than did SAT scores and that essays explained much of the variance in SAT scores. This analysis shows that essays encode similar information as the SAT and suggests that college admission protocols should attend to how social class is encoded in non-numerical components of applications.

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Elective curriculums require undergraduates to choose from a large roster of courses for enrollment each term. It has proven difficult to characterize this fateful choice process because it remains largely unobserved. Using digital trace data to observe this process at scale at a private research university, together with qualitative student interviews, we provide a novel empirical study of course consideration as an important component of course selection. Clickstream logs from a course exploration platform used by most undergraduates at the case university reveal that students consider on average nine courses for enrollment for their first fall term (<2% of available courses) and these courses predict which academic major students declare two years later. Twenty-nine interviews confirm that students experience consideration as complex and reveal variation in consideration strategies that may influence how consideration unfolds. Consideration presents a promising site for intervention in problems of equity, career funneling, and college completion.

How does gender inform initial academic commitments and narrative self-presentation in science, technology, engineering, and mathematics (STEM) fields during the college application process? Analyzing 60,000 undergraduate applications to the University of California, the authors surface two key findings. First, extant gender segregation of academic disciplines also manifests in intended major choice. Additionally, gender and SAT Math scores together strongly predict intent to major in biology and engineering, the most popular and gender-segregated majors. Second, using natural language processing, the investigators find that author gender is more predictive of essay topics written by prospective engineers than prospective biologists. Specifically, women intending to major in engineering write about essay topics that signal their gender identity to a greater degree than women intending to major in biology, perhaps to mitigate gender-transgressive academic commitments. The authors subsequently argue that prescriptive and proscriptive ideas about men and women’s academic choices remain highly salient in a moment of imagining future academic and professional selves.

College admissions in the United States is carried out by a humancentered method of evaluation known as holistic review, which typically involves reading original narrative essays submitted by each applicant. The legitimacy and fairness of holistic review, which gives human readers significant discretion over determining each applicant's fitness for admission, has been repeatedly challenged in courtrooms and the public sphere. Using a unique corpus of 283,676 application essays submitted to a large, selective, state university system between 2015 and 2016, we assess the extent to which applicant demographic characteristics can be inferred from application essays. We find a relatively interpretable classifier (logistic regression) was able to predict gender and household income with high levels of accuracy. Findings suggest that data auditing might be useful in informing holistic review, and perhaps other evaluative systems, by checking potential bias in human or computational readings. CCS CONCEPTS • Computing methodologies → Classification and regression trees; • Applied computing → Education; Sociology.