Designing a Simulation Course in the Quantitative Sciences: Lessons Learned

• In Event: Simulation as Method, Pedagogy, and Epistemology: A Framework for Monte Carlo Simulation Methods

Abstract

This presentation details the design, implementation, and instructional philosophy behind a graduate-level simulation course developed to teach quantitative research methods through Monte Carlo techniques. Grounded in project-based learning and reproducibility pedagogy, the course was conceived as a response to two persistent gaps in quantitative training: (1) the lack of formal instruction on simulation methods, and (2) the disconnect between coding skills and methodological reasoning. The speaker shares the evolution of the course structure, key instructional decisions, and lessons learned from piloting the course with a cross-disciplinary group of students.
The course was designed around weekly instructional modules, each aligned with one or more phases of a framework we later called the “6 C’s” of simulation study design: Conceptualize, Comprehend, Conduct, Calculate, Chart, and Communicate. Early weeks focused on foundational concepts and syntax (R/Python), while later sessions emphasized the design and analysis of simulation studies relevant to educational and psychological measurement, such as power analysis, measurement error, and model comparison. A distinctive feature was the use of a “simulation hackathon” as the course’s culminating project, replacing traditional exams with a time-bound, team-based simulation challenge. Students collaborated to design and interpret a novel simulation study, present results, and submit reproducible code via GitHub.
In addition to walking through course content and structure, the presentation will highlight the logistical and pedagogical considerations of co-teaching a simulation course. Topics include scaffolding for diverse student backgrounds, balancing coding instruction with statistical reasoning, and using open-source tools (e.g., Quarto, GitHub) to foster reproducibility. Reflections on course evaluation, challenges (e.g., time constraints, varying skill levels), and adaptations for future iterations will also be shared.
Ultimately, this paper contributes practical guidance for methodologists, instructors, and program directors interested in integrating simulation and reproducible research workflows into graduate education. The insights presented will help others replicate or adapt the course to fit their disciplinary and institutional contexts.

Author

• Zachary K. Collier, University of Connecticut