Technical Papers
May 11, 2017

Modeling and Simulation in Engineering Education: A Learning Progression

Publication: Journal of Professional Issues in Engineering Education and Practice
Volume 143, Issue 4

Abstract

This study used a Delphi technique to identify relevant modeling and simulation practices required in present-day workplace engineering. Participants consisted of 37 experts divided into two panels: 18 experts in academia on one panel, and 19 experts from industry on another panel. Panel members participated in three rounds of data collection in which they offered their opinions about the relevance of specific modeling and simulation skills, and opinions about when these practices should be introduced into the undergraduate and graduate engineering curricula. The guiding research question was: What are the required modeling and simulation practices to be integrated as part of the engineering curricula at the undergraduate and graduate levels? Findings from this study were used to inform a preliminary learning progression for modeling and simulation in undergraduate and graduate engineering education. Outcomes from this study represent a linchpin for future research about learning and achievement of modeling and simulation practices, which can delineate the nature of productive instructional pathways toward modeling and simulation proficiency.

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Acknowledgments

The work reported here was supported in part by the National Science Foundation under the award EEC 1449238. The views represented here are those of the author and do not represent the National Science Foundation. The author is exceedingly grateful to the participants in the Delphi study and other collaborators and students who provided feedback throughout the process.

References

ABET. (2013). “2014-2015 criteria for accrediting engineering programs.” Engineering Accreditation Commission, Baltimore.
ACM (Association for Computing Machinery)–IEEE. (2013). “Computer science curricula 2013: Curriculum guidelines for undergraduate degree programs in computer science curriculum guidelines for undergraduate degree programs in computer science.” Joint Task Force on Computing Curricula Association for Computing Machinery and IEEE-Computer Society.
Alabi, O., Magana, A. J., and Garcia, R. E. (2015). “Gibbs, computational simulation as a teaching tool for students’ understanding of thermodynamics of materials concepts.” J. Mater. Edu., 37(5–6), 239–260.
ASCE. (2008). Civil engineering body of knowledge for the 21st century—Preparing the civil engineer for the future, 2nd Ed., Reston, VA.
ASEE (American Society for Engineering Education). (2013). “Transforming undergraduate education in engineering (TUEE).” Phase I: Synthesizing and integrating industry perspectives, Arlington, VA.
Balogh, Z. E., and Criswell, M. E. (2013). “Framework of knowledge for master’s-level structural engineering education.” J. Prof. Issues Eng.Edu. Pract., 04013007.
Chen, J. C., Ellis, M., Lockhart, J., Hamoush, S., Brawner, C. E., and Tront, J. G. (2000). “Technology in engineering education: What do the faculty know and want?” J. Eng.Edu., 89(3), 279–283.
Corcoran, T. B, Mosher, F. A., and Rogat, A. (2009). “Learning progressions in science: An evidence-based approach to reform.”, Consortium for Policy Research in Education, Center on Continuous Instructional Improvement, Teachers College, Columbia Univ., New York.
Cortes, J. M, Pellicer, E., and Catala, J. (2011). “Integration of occupational risk prevention courses in engineering degrees: Delphi study.” J. Prof. Issues Eng. Educ. Pract., 31–36.
Dalkey, N. C., Brown, B. B., and Cochran, S. (1969). The Delphi method: An experimental study of group opinion, Vol. 3, Rand Corporation, Santa Monica, CA.
Desimone, L. M, and Le Floch, K. C. (2004). “Are we asking the right questions? Using cognitive interviews to improve surveys in education research.” Educ. Eval. Policy Anal., 26(1), 1–22.
DoD (Department of Defense). (2006). “Acquisition modeling and simulation master plan.” Office of the Under Secretary of Defense, Washington, DC.
DoD (Department of Defense). (2008). “DoD modeling and simulation body of knowledge (BOK).”, Washington, DC.
DOE (Department of Energy). (2010). “Computational materials science and chemistry: Accelerating discovery and innovation through simulation-based engineering and science.”, Bethesda, MD.
Duffield, C. (1993). “The Delphi technique: A comparison of results obtained using two expert panels.” Int. J. Nurs. Stud., 30(3), 227–237.
Duncan, R. G., and Hmelo-Silver, C. E. (2009). “Learning progressions: Aligning curriculum, instruction, and assessment.” J. Res. Sci. Teach., 46(6), 606–609.
Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., and Leifer, L. J. (2005). “Engineering design thinking, teaching, and learning.” J. Eng. Educ., 94(1), 103–120.
Emmott, S. (2008). “Towards 2020 science.” Sci. Parliament, 65(4), 31–33.
Felder, R. M., and Brent, R. (2003). “Designing and teaching courses to satisfy the ABET engineering criteria.” J. Eng. Educ., 92(1), 7–25.
Guzdial, M. (2011). “From science to engineering.” Commun. ACM, 54(2), 37–39.
Hasson, F., Keeney, S., and McKenna, H. (2000). “Research guidelines for the Delphi survey technique.” J. Adv. Nurs., 32(4), 1008–1015.
Hsu, C. C., and Sandford, B. A. (2007). “The Delphi technique: Making sense of consensus.” Pract. Assess. Res. Eval., 12(10), 1–8.
Hu, C. (2007). “Integrating modern research into numerical computation education.” Comput. Sci. Eng., 9(5), 78–81.
International Engineering Alliance. (2011). “Washington Accord program outcomes.” Washington, DC.
Kadiyala, M., and Crynes, B. L. (2000). “A review of literature on effectiveness of use of information technology in education.” J. Eng. Educ., 89(2), 177–189.
Kendall, J. E., Kendall, K. E., Smithson, S., and Angell, I. O. (1992). “SEER: A divergent methodology applied to forecasting the future roles of the systems analyst.” Human Syst. Manage., 11(3), 123–135.
Krajcik, J., McNeill, K. L., and Reiser, B. J. (2008). “Learning-goals-driven design model: Developing curriculum materials that align with national standards and incorporate project-based pedagogy.” Sci. Educ., 92(1), 1–32.
Lenox, T. A., Ressler, S. J., O’Neil, R. J., and Conley, C. H. (1997). “Computers in the integrated civil engineering curriculum: A time of transition.” Proc., 1997 American Society of Engineering Education Conf., Milwaukee.
Li, J., and Fu, S. (2012). “A systematic approach to engineering ethics education.” Sci. Eng. Ethics, 18(2), 339–349.
Magana, A. J., Brophy, S. P., and Bodner, G. M. (2012). “Instructors’ intended learning outcomes for using computational simulations as learning tools.” J. Eng. Educ., 101(2), 220–243.
Magana, A. J., and Coutinho, G. S. (2017). “Modeling and simulation practices for a computational thinking-enabled engineering workforce.” Comput. Appl. Eng. Educ., 25(1), 62–78.
Magana, A. J., Falk, L. M., and Reese, J. M. (2013). “Introducing discipline-based computing in undergraduate engineering education.” ACM Trans. Comput. Educ., 13(4), 1–22.
Magana, A. J., Falk, M. L., Vieira, C., and Reese, M. J. (2016). “A case study of undergraduate engineering students’ computational literacy and self-beliefs about computing in the context of authentic practices.” Comput. Human Behav., 61, 427–442.
Magana, A. J., Falk, M. L., Vieira, C., Reese, M. J., Jr., Alabi, O., and Patinet, S. (2017). “Affordances and challenges of computational tools for supporting modeling and simulation practices.” Comput. Appl. Eng. Educ., in press.
Magana, A. J., and Mathur, J. I. (2012). “Motivation, awareness, and perceptions of computational science.” Comput. Sci. Eng., 14(1), 74–79.
Maria, A. (1997). “Introduction to modeling and simulation.” Proc., 29th Conf. on Winter Simulation, Atlanta.
McKenna, A. F., and Carberry, A. R. (2012). “Characterizing the role of modeling in innovation.” Int. J. Eng. Educ., 28(2), 263–269.
NRC (National Research Council). (2003). “BIO 2010: Transforming undergraduate education for future research biologists.” Committee on Undergraduate Biology Education to Prepare Research Scientists for the 21st Century, National Academies Press, Washington, DC.
NRC (National Research Council). (2008). “Integrated computational materials engineering: A transformational discipline for improved competitiveness and national security.” Committee on Integrated Computational Materials Engineering, National Academies Press, Washington, DC.
NRC (National Research Council). (2011). “Report of a workshop on the pedagogical aspects of computational thinking.” National Academies Press, Washington, DC.
NSF (National Science Foundation). (2004). “Simulation based engineering and science: A report on a workshop.” Arlington, VA.
NSF (National Science Foundation). (2006). “Revolutionizing engineering science through simulation.”, Washington, DC.
NSF (National Science Foundation). (2011). “Report of the high performance computing task force advisory committee for cyberinfrastructure task force on grand challenges.” Washington, DC.
Okoli, C., and Pawlowski, S. D. (2004). “The Delphi method as a research tool: An example, design considerations and applications.” Inf. Manage., 42(1), 15–29.
Prince, M., Vigeant, M., and Nottis, K. (2012). “Development of the heat and energy concept inventory: Preliminary results on the prevalence and persistence of engineering students’ misconceptions.” J. Eng. Educ., 101(3), 412–438.
Rossouw, A., Hacker, M., and de Vries, M. J. (2011). “Concepts and contexts in engineering and technology education: An international and interdisciplinary Delphi study.” Int. J. Technol. Des. Educ., 21(4), 409–424.
Schwarz, C., Reiser, B. J., Achér, A., Kenyon, L., and Fortus, D. (2012). “Challenges in defining a learning progression for scientific modeling.” Learning progression in science: Current challenges and future directions, A. C. Alonzo and A. Wenk Gotwals, eds., Sense Publishers, Boston.
Schwarz, C., and White, B. Y. (2005). “Metamodeling knowledge: Developing students’ understanding of scientific modeling.” Cognit. Instruction, 23(2), 165–205.
Schwarz, C. V., et al. (2009). “Developing a learning progression for scientific modeling: Making scientific modeling accessible and meaningful for learners.” J. Res. Sci. Teach., 46(6), 632–654.
Shiflet, A. B. (2002). “Computer science with the sciences: An emphasis in computational science.” ACM SIGCSE Bull., 34(4), 40–43.
Shiflet, A. B., and Shiflet, G. W. (2014). Introduction to computational science: Modeling and simulation for the sciences, Princeton University Press, Princeton, NJ.
Smith, C. L., Wiser, M., Anderson, C. W., and Krajcik, J. (2006). “Implications of research on children’s learning for standards and assessment: A proposed learning progression for matter and the atomic-molecular theory.” Meas. Interdiscip. Res. Perspect., 4(1–2), 1–98.
Streveler, R. A., Olds, B. M., Miller, R. L., and Nelson, M. A. (2003). “Using a Delphi study to identify the most difficult concepts for students to master in thermal and transport science.” Proc., Annual Conf. of the American Society for Engineering Education, American Society for Engineering Education, Nashville, TN.
Thornton, K., Nola, S., Garcia, R. E., Asta, M., and Olson, G. B. (2009). “Computational materials science and engineering education: A survey of trends and needs.” J. Miner. Metals Mater. Soc., 61(10), 12–17.
Van Someren, M. W., Barnard, Y. F., and Sandberg, J. A. C. (1994). The think aloud method: A practical guide to modelling cognitive processes, Vol. 2, Academic Press, London.
Vergara, C. E., et al. (2009). “Aligning computing education with engineering workforce computational needs: New curricular directions to improve computational thinking in engineering graduates.” Frontiers in Education Annual Conf., IEEE, San Antonio.
Vieira, C., Magana, A. J., Falk, M. L., and Garcia, R. E. (2017). “Writing in-code comments to self-explain in computational science and engineering education.” ACM Trans. Comput. Educ. (TOCE), in press.
Vieira, C., Magana, A. J., Roy, A., Falk, L. M., and Reese, J. M. (2016a). “Exploring undergraduate students’ computational literacy in the context of problem solving.” Comput. Educ. J., 7(1), 100–112.
Vieira, C., Magana, A. J., Roy, A., Falk, L. M., and Reese, J. M. (2016b). “In-code comments as a self-explanation strategy for computational science education.” Proc., 123rd ASEE Annual Conf. and Exposition, American Society for Engineering Education, New Orleans.
Wiggins, G., and McTighe, J. (1997). Understanding by design, Association for Supervision and Curriculum Development, Alexandria, VA.
Wiggins, G., and McTighe, J. (2005). Understanding by design, Association for Supervision and Curriculum Development, Alexandria, VA.
WTEC (World Technology Evaluation Center). (2009). International assessment of research and development in simulation-based engineering and science, S. C. Glotzer, ed., Baltimore.

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Go to Journal of Professional Issues in Engineering Education and Practice
Journal of Professional Issues in Engineering Education and Practice
Volume 143Issue 4October 2017

History

Received: Sep 15, 2016
Accepted: Feb 16, 2017
Published online: May 11, 2017
Published in print: Oct 1, 2017
Discussion open until: Oct 11, 2017

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Alejandra J. Magana [email protected]
Associate Professor, Computer and Information Technology and Engineering Education, Purdue Univ., 401 N. Grant St., West Lafayette, IN 47906. E-mail: [email protected]

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