Fazel Ranjbar

Fazel Ranjbar is a Ph.D. candidate in Engineering and Computing Education at the University of Cincinnati, advised by Dr. P. K. Imbrie. His research looks at how first-year engineering student teams work together. He studies team processes, emergent states such as trust and potency, and how both relate to team effectiveness. His three-manuscript dissertation is titled The Dynamics of Teamwork: Investigating the Interplay of Student Behaviors, Emergent States, and Team Effectiveness.

His methods work connects measurement and educational data science. He develops and validates teamwork and noncognitive instruments using Rasch and item response theory models, factor analysis, and structural equation modeling. He also designed a theory-informed rubric procedure that uses a large language model to score more than 16,000 open-ended peer evaluation comments, with repeatability testing and audit checks built into the workflow.

He has completed two internships in UC's Office of Institutional Research and Academic Analytics, where he built retention and graduation prediction models and extended the university's enrollment projection system. Before graduate school, he spent nine years in the steel industry, most recently as a sales and production planning manager.

Ph.D.: University of Cincinnati Cincinnati, OH, 2027 (Engineering and Computing Education (in progress, expected Spring 2027))

Graduate Certificate: University of Cincinnati Cincinnati, OH, 2026 (Data Analytics)

Teamwork in engineering education, with a focus on how first-year student teams develop over a semester. This includes the behaviors students show in teams, emergent states such as trust and potency, and how these relate to team effectiveness and to what students report in peer evaluations.

Measurement and methods: instrument development and validity evidence using Rasch measurement, item response theory, factor analysis, structural equation modeling, and multilevel modeling of longitudinal team data. Large language models as transparent, auditable scoring tools for qualitative student data within mixed-methods designs.

Related interests: AI-supported peer feedback, noncognitive attributes and student retention, and institutional research and predictive analytics in higher education.

08-2022 -To Present Graduate Research Assistant, Conducts longitudinal, IRB-approved research on first-year engineering student teams. Develops and validates teamwork and noncognitive measures using Rasch and item response theory models, factor analysis, and structural equation modeling. Designed a rubric-based procedure that uses a large language model to score more than 16,000 open-ended peer evaluation comments, with repeatability testing (pairwise ICCs of .83 to .93) and a programmatic audit of model output. Led a mixed-methods study of peer evaluations from 2,077 students in 667 teams, Department of Engineering and Computing Education, University of Cincinnati, Cincinnati, OH

05-2026 -08-2026 Institutional Research Intern, Refactored and extended the university's long-term enrollment and fiscal projection system, a production R and SQL codebase that feeds Tableau dashboards used by senior administration. Improved uncertainty quantification and transition-ratio estimation, replaced manual spreadsheet calculators with a reproducible tuition revenue projection workflow, and documented the model so other analysts can replicate runs, Office of Institutional Research and Academic Analytics, University of Cincinnati, Cincinnati, OH

05-2025 -08-2025 Data Analytics Intern, Built an automated machine learning pipeline in R (tidymodels, XGBoost) that predicts one-, two-, and three-year retention and graduation for student subgroups by college. Used SHAP values and Bayesian hyperparameter tuning to keep small-subgroup models stable, and presented results to university leadership to inform student support decisions, Office of Institutional Research and Academic Analytics, University of Cincinnati, Cincinnati, OH

08-2023 -08-2025 Graduate Teaching Assistant, Probability and Statistics, Supported engineering students in probability, distributions, and hypothesis testing across six semesters through review sessions and office hours. Wrote exam and quiz items aligned with learning objectives, scored responses, reviewed item performance across sections, and revised items that performed poorly, College of Engineering and Applied Science, University of Cincinnati, Cincinnati, OH

05-2024 -08-2024 Co-Leader, STEM Summer Camp, Designed and led interactive sessions on engineering design and systems thinking for youth with intellectual and developmental disabilities, hosted by the IDD Education Center. Delivered age-appropriate engineering activities that widened access to STEM learning, College of Education, Criminal Justice, and Human Services, University of Cincinnati, Cincinnati, OH

Peer Reviewed Publications

Ranjbar, F., Yoon, S. Y., Awtrey, E., & Imbrie, P. K.Scaling interpretive inquiry: Establishing validity evidence for a theory-informed, AI-driven analysis of student team dynamics. Studies in Engineering Education (manuscript under revision)

Ranjbar, F., Yoon, S. Y., & Imbrie, P. K.  (2025). The longitudinal impact of engineering students' noncognitive attributes on their academic performance and retention during COVID-19. Proceedings of the IEEE Frontiers in Education Conference (FIE)

Ranjbar, F., Agarwal, J., & Imbrie, P. K.  (2025). Identifying behaviors contributing to first-year student team dysfunctionality. Proceedings of the IEEE Frontiers in Education Conference (FIE)

Ranjbar, F., Agarwal, J., Vahidi, E., Wang, J., & Imbrie, P. K.  (2024). WIP: A preliminary investigation of students as peer evaluators of their team members. Proceedings of the IEEE Frontiers in Education Conference (FIE)

Ranjbar, F., Agarwal, J., & Imbrie, P. K.  (2024). Work in progress: Teamwork predisposition as an indicator of team effectiveness in first-year engineering. Proceedings of the ASEE Annual Conference and Exposition

Imbrie, P. K., Ranjbar, F., & Agarwal, J.  (2023). Impact of first co-op experience on student retention and learning: A work in progress. Proceedings of the ASEE Annual Conference and Exposition

Studies in Engineering Education Peer Review/Referee Type:Editorial Service Level:International

American Society for Engineering Education (ASEE) Peer Review/Referee Type:Service to Professional Associations Level:National

IEEE Peer Review/Referee Type:Service to Professional Associations Level:International

engineering education, teamwork, team dynamics, emergent states, peer evaluation, psychometrics, Rasch measurement, item response theory, structural equation modeling, multilevel modeling, mixed methods, large language models, first-year engineering, institutional research