A Structural Equation Model Analysis of Computing Identity Sub-Constructs and Student Academic Persistence
Doctor of Philosophy (PhD)
First Advisor's Name
First Advisor's Committee Title
Second Advisor's Name
Second Advisor's Committee Title
Third Advisor's Name
Mark Allen Weiss
Third Advisor's Committee Title
Fourth Advisor's Name
Peter J Clarke
Fourth Advisor's Committee Title
Fifth Advisor's Name
Fifth Advisor's Committee Title
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
Taheri, Mohsen, "A Structural Equation Model Analysis of Computing Identity Sub-Constructs and Student Academic Persistence" (2020). FIU Electronic Theses and Dissertations. 4575.
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