Stephanie J. Lunn
Knight Foundation School of Computing and Information Sciences
Stephanie Lunn is a Ph.D. candidate in the Knight Foundation School of Computing and Information Sciences (KF-SCIS) at Florida International University (FIU), under the supervision of Dr. Monique Ross. Her research interests span the fields of computing education, data science, and machine learning. Previously, Stephanie received her B.S. and M.S. degrees in Neuroscience from the University of Miami, in addition to B.S. and M.S. degrees in Computer Science from FIU. She was also a KF-SCIS Presidential/Director’s Fellow from 2017-2020.
What does it take to obtain a position in the computing industry? Although anecdotally we hear “hiring is broken,” empirical evidence is necessary to identify the flaws in the existing system. The goal of this dissertation was to understand what expectations companies have for job candidates in computing, along with how the hiring process may affect students’ identities. In designing the study, we were mindful about considering how practices may impact populations already underrepresented in computing such as women, Black/African American students, and Hispanic/Latinx students. The guiding theoretical frameworks for this work included identity theory, the community cultural wealth model, intersectionality, and social cognitive career theory, in order to answer the following research questions: 1) What does the hiring process in computing look like from both the applicant and industry perspective?; 2) How do cultural experiences impact technical interview preparation?; 3) How do technical interviews, and other professional and cultural experiences impact computing identity?; and 4) What are students’ experiences with the hiring process in computing?
To answer these questions, I employed a variety of methods, beginning with a systematic literature review. This was followed by an explanatory sequential mixed-methods design that leveraged a survey, statistical analysis, and discursive phenomenography. An explanatory sequential design was utilized to explore how students from different gender, racial, and ethnic backgrounds experience the phenomenon of hiring in computing, and how they leverage their own inherent capital to overcome obstacles throughout the process. The results of this work not only serve to inform students, educators, and administrators how to best prepare for technical interviews, but also to call attention to how current industry hiring practices may limit diversity. I offer suggestions and guidelines that will enable a hiring process that can still achieve its target of finding qualified employees, but that does so in a manner more inclusive to all job applicants.