Jia Zhu

Ph.D. Candidate

Lecture Information:
  • March 13, 2023
  • 11:00 AM
  • Zoom

Speaker Bio

Jia Zhu is a Ph.D. candidate at the Knight Foundation School of Computing and Information Science (KFSCIS) at Florida International University (FIU). Her research interests include computing education, educational data mining, and data science. Her work aims at broadening participation with a focus on exploring computing access points for women, improving the computing educational experience, and creating an inclusive computing ecosystem. Before pursuing her Ph.D., Jia received her M.S. in Data Science with a concentration in computational data analytics from KFSCIS. She also holds B.S. and M.S. degrees in Hospitality Management from FIU. As a Ph.D. student, Jia has been a graduate research assistant for Dr. Monique Ross and has published nine papers at annual conferences, including the American Society of Engineering Education (ASEE), IEEE Frontiers in Education Conference (FIE), ACM Conferences on International Computing Education (ICER), and the ACM Technical Symposium on Special Interest Group on Computer Science Education (SIGCSE). Jia has also been awarded the National Science Foundation (NSF) INTERN grant to conduct a research internship with the Journal of Engineering Education (JEE). During this internship, she explores Diversity, Equity, and Inclusion (DEI) trends within JEE publications to support the broadening participation mission of JEE’s strategic planning.


he rapid growth of the computing industry requires a diverse and highly qualified workforce. Despite efforts of broadening participation in computing by the Computer Science Education Research (CSER) community, there are still significant challenges in closing the talent gaps in this field, and gender disparities continue to exist. To address this problem, attracting and engaging more women in computing careers is necessary. However, early exposure to computing is critical for inspiring career aspirations, and many women miss out on this opportunity due to a lack of earlier exposure. In addition, there is a group of post-baccalaureate women with non-computing undergraduate degrees but with deferred computing interests who would like to transition to computing later in their career trajectories. We refer to this group as non-computing women, and their potential as a source of talent in the computing industry should be further explored.

The proposed dissertation aims to explore the learning experiences of non-computing women as they transition into computing careers. The study will use a sequential mixed methods approach consisting of three phases. Phase one will employ qualitative content analysis to summarize the learning experiences of non-computing women during computing career transitions. In phase two, a quantitative survey study will be conducted, building on the findings from phase one to identify the key factors affecting non-computing women’s career decisions. These key factors will also be used to cluster the survey participants for in-depth interviews in the final qualitative phase. The final phase aims to provide insights into how non-computing women utilize coping resources during computing career transitions. By combining both the quantitative and qualitative strands, this sequential mixed method design will thoroughly explore non-computing women’s learning experiences and identify factors that promote positive experiences during computing career transitions. The findings of this dissertation will be valuable to non-computing women and educational institutions, as it will provide better support and resources for computing career transitions. Furthermore, this work encourages the development of a more inclusive and diverse computing ecosystem by empowering non-computing women to successfully transition into computing careers.