Modeling the Pathways to Self-Confidence for Graduate School in Computing

Annie M. Wofford ()
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Annie M. Wofford: UCLA Moore Hall

Research in Higher Education, 2021, vol. 62, issue 3, No 4, 359-391

Abstract: Abstract Given the significant need to increase and diversify graduate enrollments within computing fields, it is vital to understand what shapes students’ pathways to computing graduate school. This study examines the predictors of undergraduate students’ self-confidence in being admitted to computing graduate school among students who enrolled in an introductory computing course during the 2015–2016 academic year and completed both an end-of-intro-course survey as well as a follow-up survey two years later. Guided by social cognitive career theory, this longitudinal and multi-institutional study uses structural equation modeling to illustrate the direct and indirect relationships between students’ social identities (specifically gender and race/ethnicity), psychosocial beliefs, perceptions of support, and self-confidence for computing graduate admission. Findings suggest that gender and racial/ethnic inequities in self-confidence for graduate admission are present during introductory computing courses, and women’s early perceptions in intro courses (e.g., math self-concept) seem to play an especially vital role in explaining why women ultimately report lower self-confidence for computing graduate admission than men. Findings also highlight the key mediating role of computing self-efficacy in cultivating students’ self-confidence for computing graduate admission. Taken together, these results have important implications for understanding intro computing students’ perceptions about their graduate school trajectories and how to foster a more diverse graduate applicant pool.

Keywords: Self-confidence; Graduate school aspirations; Computing; Diversity; Structural equation modeling (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11162-020-09605-9

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