Who takes computer science in high school? Intersectional and longitudinal evidence

Abstract

This study explores how high school computer science (CS) course enrollment differs by student background through an intersectional lens. I use statewide data from North Carolina that contains longitudinal student-level background and course-taking information from 2005–2006 to the 2018–2019 school year and estimate linear probability models predicting course taking. The results suggest three findings: overall CS enrollment increases seem to be disproportionately driven by particular student groups; both between-school differences and within-school factors appear to explain CS enrollment gaps between student groups; and students who are both female and a member of specific racial groups or being identified as having a disability or LEP status are likely to face overlapping inequities. To my knowledge, this study is the first to use large-scale longitudinal student-level data and an intersectional lens to investigate how CS enrollment varies by student background at the high school level, especially in the context of North Carolina. This study sheds light on the different types of patterns and magnitudes of inequities that diverse student groups face, particularly potential overlapping inequities related to multiple intersecting identities, guiding areas that both current and new policies should target to achieve equity in CS education, which is a challenge that is relevant both within and outside of the United States.