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• Background: Over a decade ago, the Stanford Study of Writing (SSW) collected more than 15,000 writing samples from undergraduate students, but to this point the corpus has not been analyzed using computational methods. Through the use of natural language processing (NLP) techniques, this study attempts to reveal underlying structures in the SSW, while at the same time developing a set of interpretable features for computationally understanding student writing. These features fall into three categories: topic-based features that reveal what students are writing about; stance-based features that reveal how students are framing their arguments; and structure-based features that reveal sentence complexity. Using these features, we are able to characterize the development of the SSW participants across four years of undergraduate study, specifically gaining insight into the different trajectories of humanities, social science, and STEM students. While the results are specific to Stanford University’s undergraduate program, they demonstrate that these three categories of features can give insight into how groups of students develop as writers.

• Literature Review: The Stanford Study of Writing (Lunsford et al., 2008; SSW, 2018) involved the collection of more than 15,000 writing samples from 189 students in the Stanford class of 2005. The literature surrounding the original study is largely qualitative (Fishman, Lunsford, McGregor, & Otuteye, 2005; Lunsford, 2013; Lunsford, Fishman, & Liew, 2013), so this study makes a first attempt at a quantitative analysis of the SSW. When