This research stems from a project investigating gatekeepers—including the people, places, programs, and policies—that contribute to demographic variations across high schools in the proportion of students who enroll in an engineering major at a four-year university. We take a macroscopic, systemic view of an entire state’s longitudinal database of high school-to-postsecondary student records to understand differences across high schools, focusing on a specific section of the pathway to an engineering career (i.e., the high school to college transition). Rather than focusing on single interventions or barriers, this research speaks to the systemic issues of access and underrepresentation in engineering and provides data related to the geographic disparities of engineering enrollment.
Leveraging the Virginia Longitudinal Data System (VLDS), a student-level administrative data set that connects Department of Education data to data collected by the State Council of Higher Education for Virginia, we are able to track each student who enrolled in a Virginia public high school into their university program of enrollment, thereby being able to characterize engineering-going pathways for the entire Commonwealth of Virginia (n=685,429 students for analyses presented). Our poster presents new results pertaining to the following underrepresented populations within engineering: women, African Americans, Hispanics, and economically disadvantaged students. We also incorporate contextual variables (e.g., average community education attainment and socioeconomic status, degree of rurality versus urbanicity) to explain some of the geographic disparities in engineering pathways, and postsecondary education pathways more broadly. Moreover, our poster presents findings from multiple cases across Virginia, where we went into high schools to interview administrators, guidance counselors, and faculty members to try to understand within-school division variation. Guided by social cognitive career theory, this qualitative data analysis unpacks the complex interactions between students’ goals, interests, and self-efficacies, which are informed by a variety of contextual influences and learning experiences, and helps pinpoint why certain schools produce lots of engineers while adjacent schools may not.
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