Document Type



Doctor of Philosophy (PhD)


Computer Science

First Advisor's Name

Monique Ross

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Zahra Hazari

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Mark Allen Weiss

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Peter J Clarke

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Nagarajan Prabakar

Fifth Advisor's Committee Title

Committee member


computing persistence, computing identity

Date of Defense



This dissertation explores the impact of computing identity sub-constructs on the academic persistence of computing students. This research provides models, quantified relationships, and insights to increase the number of graduates with the intention of pursuing a career in computing. Despite the growing significance of computer science and all the projected new jobs in computer science, many university and college programs suffer from low student persistence rates. One theoretical framework used to better understand persistence in other STEM disciplines is disciplinary identity. Disciplinary identity refers to how students see themselves with respect to a discipline. This study examines the effects of computing identity sub-constructs (performance/competence, recognition, interest, and sense of belonging) on the academic persistence of computing students. A quantitative analysis with three phases was performed for this study. First, confirmatory factor analysis (CFA) and structural equation model (SEM) analysis were performed to validate and explore the relationship between sub-constructs in the computing identity model. Second, a multigroup SEM was performed to estimate the impact of the identity sub-constructs on persistence for students with diverse demographics in this case by gender and level of education. Third, a time-series SEM were used to investigate the impact of identity development on computing persistence over time. The findings indicated that students' academic persistence was directly influenced by their interest as the most significant factor. In addition, performance, competence, recognition, and sense of belonging contributed to students’ identity development and academic persistence. Results of the second analysis indicated identity sub-constructs contributed differently to academic persistence among freshman and senior students; however, no significant differences were found between male and female students. Ultimately, the last analysis with time-series data indicated that interest and competence/performance, as individual factors had the strongest direct impacts on persistence over time. Considering student identity in understanding academic persistence in computing programs may provide a meaningful lens of analysis for institutes and their curriculum and extracurricular planning methods. In addition, the development of students’ self-beliefs provides ways for increasing the number of graduates with increased likelihood of pursuing computing careers.



Previously Published In

Taheri, M., Ross, M., Hazari, Z., Weiss, M., Georgiopoulos, M., Christensen, K., ... & Chari, D. (2018, October). A Structural Equation Model Analysis of Computing Identity Sub-Constructs and Student Academic Persistence. In 2018 IEEE Frontiers in Education Conference (FIE) (pp. 1-7). IEEE.

Taheri, M., Ross, M., Hazari, Z., Weiss, W., Georgiopoulos, M., Christensen, K., ... & Chari, D. (2019, January). Exploring Computing Identity and Persistence Across Multiple Groups Us-ing Structural Equation Modeling. In American Society for Engineering Education (ASEE) Conference Proceedings.

Under review: ACM TOCE (Journal): Taheri, M., Ross, M., Hazari, Z., Weiss, W., Georgiopoulos, M., Christensen, K., ... & Chari, D. Understanding Computing Persistence and Identity Through Structural Equation Modeling with Time Series Data



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