2025 ASEE Annual Conference & Exposition

Data-Driven Analysis of Engineering Curricula: A Cross-Disciplinary Study of Complexity in Seven Programs and Its Impact on Student Pathways and Career Outcomes

This study examines how curricular complexity (CC)—modeled via network analysis—shapes post-graduation outcomes for seven engineering programs at a private institution in the Northeastern United States. Post-graduation data, including employment rates, mean salaries, and graduate-school enrollment, were correlated with these CC metrics over a five-year period. Results reveal a significant spectrum of complexity, with Mechanical and Aerospace Engineering (MAE) exhibiting the highest CC per course and Computer Science (CS) the lowest. Higher complexity did not always yield higher immediate salaries—MAE graduates initially had lower starting wages compared to CS. However, MAE graduates showed substantial long-term earnings and the highest rate of pursuing advanced degrees. A Principal Component Analysis explained over 90% of the variance in post-graduation outcomes, highlighting trade-offs between immediate job-market readiness and deeper academic engagement. These findings emphasize the importance of balancing curricular depth and flexibility to optimize student success. This research advances curriculum analytics by providing empirical metrics and actionable insights, enabling institutions to design engineering programs that better align with industry demands, support interdisciplinary learning, and promote holistic professional development.

Authors

Mohammed A. Alrizqi http://orcid.org/0000-0001-6034-8314 Cornell University [biography]

Mohammed is a PhD Mechanical Engineering Candidate at Cornell University.
Dr. Allison Godwin http://orcid.org/0000-0002-0741-3356 Cornell University [biography]

Allison Godwin, Ph.D. is the Dr. G. Stephen Irwin '67, '68 Professor in Engineering Education Research (Associate Professor) in the Robert Frederick Smith School of Chemical and Biomolecular Engineering at Cornell University. She is also the Associate Director of the Cornell NanoScale Science and Technology Facility and a McCormick Teaching Excellence Institute Research Fellow. Her research focuses on how identity, among other affective factors, influences diverse groups of students to choose engineering and persist in engineering. She also studies how different experiences within the practice and culture of engineering foster or hinder belonging, motivation, and identity development. Dr. Godwin graduated from Clemson University with a B.S. in Chemical Engineering and Ph.D. in Engineering and Science Education. Her research earned her a National Science Foundation CAREER Award focused on characterizing latent diversity, which includes diverse attitudes, mindsets, and approaches to learning to understand engineering students’ identity development. She has won several awards for her research including the 2021 Chemical Engineering Education William H. Corcoran Award, 2022 American Educational Research Association Education in the Professions (Division I) 2021-2022 Outstanding Research Publication Award, and the 2023 AIChE Excellence in Engineering Education Research Award.

Note

The full paper will be available to logged in and registered conference attendees once the conference starts on June 22, 2025, and to all visitors after the conference ends on June 25, 2025

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