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

This paper presents a case study of multidisciplinary learning in a graduate-level modeling and simulation course. Multidisciplinary learning is understood as the integration of knowledge and methods from statistics, computer science, engineering, and domain-specific contexts to analyze and solve complex systems. We use a mixed-methods approach combining quantitative survey measures and qualitative project analysis to examine how students develop multidisciplinary competencies. Four research questions guide the study: (1) students’ perceived competence gains across key modeling and simulation domains; (2) the influence of instructional components such as peer teaching, homework, and team projects; (3) the extent to which student projects demonstrate integration of disciplinary and methodological elements; and (4) student suggestions for improving future multidisciplinary learning experiences. Findings reveal strong perceived gains across all modeled domains, a collective (but not component-specific) association between course activities and competence, clear patterns of methodological integration centered on discrete-event simulation and related tools, and student recommendations emphasizing improved scaffolding and example-driven instruction. Together, these results offer actionable insights for strengthening multidisciplinary learning in modeling and simulation curricula.