A Latent Class Analysis of University Degree Trajectories Among Computer Science Transfer Students

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  In Roundtable Session: Retention, Persistence, Outcomes

Abstract

This multi-institutional study uses longitudinal survey data to examine the degree trajectories of community college transfer students in computer science. We employ latent class analysis to identify conceptually distinct degree trajectory patterns among transfer students who earn bachelor’s degrees in computer science. Expanding upon prior qualitative transfer pathways scholarship, we quantitatively identify four distinct profiles characterizing the trajectories that transfer students follow: (a) linear upward persisters, (b) pathway changers, (c) motivated persisters, and (d) efficient returners. In addition to capturing variation in how long it took transfer students to complete their CS degrees, these latent classes also capture variation in the support that they received at their universities, their engagement at their university and beyond, and their computing self-efficacy scores.

Authors

• Kaitlyn N. Stormes, University of Georgia

• Jennifer M. Blaney, University of Georgia