2026 ASEE Annual Conference & Exposition

Modeling and Simulation in Engineering: A Case Study of Multidisciplinary Learning

Presented at Mechanical Engineering (MECH) Session 8: Sustainability, Service Learning, and Broader Impact

In this paper, we present a case study of multidisciplinary learning in a graduate-level modeling and simulation course. Here multidisciplinary learning refers to the integration and application of knowledge, methods, and perspectives from multiple disciplines to solve complex problems. In the context of modeling and simulation, this may involve combining statistical analysis, computer science, engineering principles, and domain-specific knowledge (mechanical engineering in our case study).

To assess the effectiveness of multidisciplinary learning, a mixed-methods approach is taken in our study, which combines quantitative survey data with qualitative project analysis. Based on the data that we have collected in the course on the fundamentals of the modeling and simulation, we aim to engage an in-depth study of the impact of multidisciplinary learning with following four research questions: (1) RQ1: How do students perceive the development of multidisciplinary competencies through modeling and simulation courses? (2) RQ2: How do course components, such as peer teaching, team projects, and homework, impact multidisciplinary learning? (3) RQ3: In what ways do student projects reflect the integration of knowledge and methods from multiple disciplines? (4) RQ4: What suggestions do students offer to improve the multidisciplinary learning experiences?

The first research question captures student perceptions of learning gains that are transferable across disciplines, even if not explicitly labeled as “multidisciplinary” in the survey. It invites analysis of both quantitative survey responses and qualitative open-ended feedback. The second research question allows us to explore which instructional strategies are most effective in fostering cross-disciplinary thinking and collaboration. It connects directly to the survey items on the usefulness of course components. The third research question focuses on analyzing project titles and descriptions to identify disciplinary diversity and integration. It provides indirect evidence of multidisciplinary learning in practice. The last research question points out areas of improvements in future offerings of the course.

Authors

Dr. Wenbing Zhao http://orcid.org/0000-0002-3202-1127 Cleveland State University [biography]

Wenbing Zhao received his Ph.D. in Electrical and Computer Engineering at University of California, Santa Barbara, in 2002. Dr. Zhao has a Bachelor of Science degree in Physics in 1990, and a Master of Science degree in Physics in 1993, both at Peking University, Beijing, China. Dr. Zhao joined Cleveland State University (CSU) faculty in 2004 and is currently a Professor in the Department of Electrical and Computer Engineering at CSU. Dr. Zhao published over 300 peer-reviewed papers in the area of distributed systems, smart and connected health, computer vision, machine learning, physics, and engineering education. Dr. Zhao’s research is supported in part by the US National Science Foundation, the US Department of Energy, the US Department of Education. Dr. Zhao is currently serving on the organizing committee and the technical program committee for numerous international conferences. He is an Associate Editor for IEEE Access and for MDPI Computers. Dr. Zhao is a senior member of IEEE.
Dr. Yong Tao P.E. Cleveland State University [biography]

Dr. Yong Tao holds the Betty L. Gordon Endowed Chair and Distinguished Professor of the Department of Mechanical Engineering and is the past department chair at CSU. He received his Ph.D. in mechanical engineering from the University of Michigan. Prior to joining CSU in 2019, Dr. Tao has served in higher education for three decades with different roles as professor, research director, chair, associate dean, and dean in several public or private research universities. He has been active in research of the fundamentals of thermal sciences, renewable energy applications in buildings and energy system modeling and simulations. His research at CSU involves collaboration with industries on district energy systems. Dr. Tao founded Center for Integrated Modeling for Energy, Sustainability and Resiliency (IERS Center) at CSU. His recent endeavors include a Department of Education grant to establish the experiential learning environment in modeling and simulation of energy system engineering, and coordination of development of professional microcredential and certification courses in microgrid and power engineering.
Dr. Xiongyi Liu [biography]

Xiongyi Liu is an Associate Professor in the School of Education and Counseling at Cleveland State University (CSU), USA. Dr. Liu obtained her Ph.D. in Educational Psychology from University of Nebraska-Lincoln, USA. Prior to joining CSU, she has worked as research faculty at the Center for Research and Evaluation, University of Maine-Orono. Her research interests include educational technology, motivation, self-regulation, peer assessment, teacher education, STEM education, and artificial intelligence in education. At CSU, Dr. Liu is affiliated with TechHub and the Center for Integrated Modeling for Energy, Sustainability and Resiliency. She has worked on multiple interdisciplinary research projects as co-PI and evaluator for the development of innovative approaches to engineering education at various educational levels in such critical areas as modeling and simulation, cybersecurity, and AI.

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