Journal of Science Education and Technology

, Volume 27, Issue 4, pp 334–347 | Cite as

Investigating the Impact of Using a CAD Simulation Tool on Students’ Learning of Design Thinking

  • Manaz Taleyarkhan
  • Chandan Dasgupta
  • John Mendoza Garcia
  • Alejandra J. Magana
Article

Abstract

Engineering design thinking is hard to teach and still harder to learn by novices primarily due to the undetermined nature of engineering problems that often results in multiple solutions. In this paper, we investigate the effect of teaching engineering design thinking to freshmen students by using a computer-aided Design (CAD) simulation software. We present a framework for characterizing different levels of engineering design thinking displayed by students who interacted with the CAD simulation software in the context of a collaborative assignment. This framework describes the presence of four levels of engineering design thinking—beginning designer, adept beginning designer, informed designer, adept informed designer. We present the characteristics associated with each of these four levels as they pertain to four engineering design strategies that students pursued in this study—understanding the design challenge, building knowledge, weighing options and making tradeoffs, and reflecting on the process. Students demonstrated significant improvements in two strategies—understanding the design challenge and building knowledge. We discuss the affordances of the CAD simulation tool along with the learning environment that potentially helped students move towards Adept informed designers while pursuing these design strategies.

Keywords

Computer-aided design Simulation Design thinking First year engineering Informed designers 

Notes

Acknowledgements

Research reported in this paper was supported by the National Science Foundation under the award DRL 1503436 and 1151019. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation.

Compliance with Ethical Standards

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

References

  1. ABET - Engineering Accreditation Commission (2016). Criteria for accrediting engineering programs: effective for reviews during the 2017 - 2018 accreditation cycle. Baltimore: ABET. http://www.abet.org/wpcontent/uploads/2016/12/E001-17-18-EAC-Criteria-10-29-16-1.pdf. Accessed Jan 25 2018.
  2. Atman, C. J., Chimka, J. R., Bursic, K. M., & Nachtmann, H. L. (1999). A comparison of freshman and senior engineering design processes. Design Studies, 20(2), 131–152.  https://doi.org/10.1016/S0142-694X(98)00031-3.CrossRefGoogle Scholar
  3. Atman, C. J., Cardella, M. E., Turns, J., & Adams, R. (2005). Comparing freshman and senior engineering design processes: An in-depth follow-up study. Design Studies, 26(4), 325–357.  https://doi.org/10.1016/j.destud.2004.09.005.CrossRefGoogle Scholar
  4. Barron, B., Schwartz, D. L., Vye, N. J., Moore, A., Petrosino, T., Zech, L., & Bransford, J. D. (1998). Doing with understanding: Lessons from research on problem and project-based learning. Journal of the Learning Sciences, 7(3 & 4), 271–311.Google Scholar
  5. Blumenfeld, P. C., Soloway, E., Marx, R. W., Krajcik, J. S., Guzdial, M., & Palincsar, A. (1991). Motivating project-based learning: Sustaining the doing, supporting the learning. Educ Psychol, 26(3–4), 369–398.  https://doi.org/10.1080/00461520.1991.9653139.CrossRefGoogle Scholar
  6. Bratitsis, T., & Demetriadis, S. (2013). Research approaches in computer-supported collaborative learning. International Journal of e-Collaboration (IJeC), 9(1), 1–8.  https://doi.org/10.4018/jec.2013010101.CrossRefGoogle Scholar
  7. Capobianco, B. M., Nyquist, C., & Tyrie, N. (2013). Shedding light on engineering design. Science and Children, 50(5), 58–64.Google Scholar
  8. Cerra, P. P., González, J. M. S., Parra, B. B., Ortiz, D. R., & Peñín, P. I. Á. (2014). Can interactive web-based CAD tools improve the learning of engineering drawing? A case study. Journal of Science Education and Technology., 23(3), 398–411.  https://doi.org/10.1007/s10956-013-9471-7.CrossRefGoogle Scholar
  9. Crismond, D. P., Howland, J., & Jonassen, D. (2011). Designing with technology. In J. How-land, D. H. Jonassen, & R. M. Marra (Eds.), Meaningful learning with technology (4th ed., pp. 72–90). Upper Saddle River, NJ: Merrill Prentice Hall.Google Scholar
  10. Crismond, D., & Adams, R. (2012). The informed design teaching and learning matrix. Journal of Engineering Education., 101(4), 738–797.  https://doi.org/10.1002/j.2168-9830.2012.tb01127.x.CrossRefGoogle Scholar
  11. Cross, N. (2000). Engineering design methods: Strategies for product design (3rd ed.). New York: John Wiley & Sons.Google Scholar
  12. Cross, N. (2001). Design cognition: Results from protocol and other empirical studies. In C. Eastman, W. Newstatter, & M. McCracken (Eds.), Design knowing and learning: Cognition in design education (pp. 79–103). Oxford: Elsevier.  https://doi.org/10.1016/B978-008043868-9/50005-X.CrossRefGoogle Scholar
  13. Cross, N. (2006). Designerly ways of knowing (First ed.). London: Springer-Verlag.Google Scholar
  14. Christophersen, E., Coupe, P. S., Lenschow, R. J., & Townson, J. (1994). Evaluation of civil and construction engineering education in Denmark. Copenhagen: Centre for Quality Assurance and Evaluation of higher education in Denmark.Google Scholar
  15. Dasgupta, C. & Moher, T. (2014). Using deficient models as scaffolds for learning engineering concepts of tradeoffs and optimization. Proceedings of the 11th international conference of the learning sciences. Boulder: International Society of the Learning Sciences.Google Scholar
  16. Dorst, K. (2003). The problem of design problems. Paper presented at the design thinking research symposium, Sydney, Australia.Google Scholar
  17. Dorst, K & Reymen, I. (2004). Levels of expertise in design education. Paper presented at the International Engineering Conference and Product Design Education Conference. Delft: The Design Society.Google Scholar
  18. Dorst, K., & Lawson, B. (2009). Design expertise. Oxford: Architectural press.Google Scholar
  19. Dorst, K. (2011). The core of ‘design thinking’ and its application. Design studies, 32(6), 521–532.  https://doi.org/10.1016/j.destud.2011.07.006.CrossRefGoogle Scholar
  20. Duderstadt, J. J. (2008). Engineering for a changing world. A roadmap to the future of engineering practice, research, and education. Ann Arbor: The University of Michigan.Google Scholar
  21. Duncan, R. G., & Hmelo-Silver, C. E. (2009). Learning progressions: Aligning curriculum, instruction, and assessment. Journal of Research in Science Teaching, 46(6), 606–609.  https://doi.org/10.1002/tea.20316.CrossRefGoogle Scholar
  22. Dym, C. L., Agogino, A. M., Eris, O., Frey, D. D., & Leifer, L. J. (2005). Engineering design thinking, teaching, and learning. Journal of Engineering Education, 94(1), 103–120.  https://doi.org/10.1002/j.2168-9830.2005.tb00832.x.CrossRefGoogle Scholar
  23. Gobert, J. D., & Tinker, R. F. (2004). Introduction to the issue. Journal of Science Education and Technology, 13(1), 1–5.  https://doi.org/10.1023/B:JOST.0000019705.04018.a3.CrossRefGoogle Scholar
  24. Gobert, J. D., & Pallant, A. (2004). Fostering students’ epistemologies of models via authentic model-based tasks. Journal of Science Education and Technology, 13(1), 7–22.  https://doi.org/10.1023/B:JOST.0000019635.70068.6f.CrossRefGoogle Scholar
  25. Göl, Ö., & Nafalski, A. (2007). Collaborative learning in engineering education. Global Journal of Engineering Education, 11(2), 173–180.Google Scholar
  26. Goldstein, M. H., Purzer, Ş., Mejia, C. V., Zielinski, M., & Douglas, K. A. (2015). Assessing idea fluency through the student design process. Paper presented at the Frontiers in Education Conference (FIE). El Paso: IEEE.  https://doi.org/10.1109/FIE.2015.7344207.
  27. Haddock, C., Rindskopf, D., & Shadish, W. (1998). Using odds ratios as effect sizes for meta-analysis of dichotomous data: A primer on methods and issues. Psychological Methods, 3(3), 339–353.  https://doi.org/10.1037/1082-989X.3.3.339.CrossRefGoogle Scholar
  28. Horwitz, P., & Barowy, B. (1994). Designing and using open-ended software to promote conceptual change. Journal of Science Education, 3(3), 161–185.Google Scholar
  29. Hsiung, C. M. (2012). The effectiveness of cooperative learning. Journal of Engineering Education, 101(1), 119–137.  https://doi.org/10.1002/j.2168-9830.2012.tb00044.x.CrossRefGoogle Scholar
  30. Jornet, A., & Roth, W.-M. (2017). Design {thinking | communicating}: A sociogenetic approach to reflective practice in collaborative design. In B. Christensen, L. J. Ball, & K. Halskov (Eds.), Analyzing design thinking: Studies of cross-cultural co-creation (pp. 331–347). Boca Raton: CRC Press.  https://doi.org/10.1201/9781315208169-23.CrossRefGoogle Scholar
  31. Krajcik, J. S., & Blumenfeld, P. C. (2006). Project-based learning. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 317–333). Cambridge: Cambridge University press.Google Scholar
  32. Logan, G. D., & Radcliffe, D. F. (1998). Artefacting in a cross-discipline design team. Proceedings of DETC98. 1998 ASME design engineering technical conference. Atlanta: American Society of Mechanical Engineers.Google Scholar
  33. Larmer, J., Mergendoller, J., & Boss, S. (2015). Setting the standard for project based learning. Alexandria: ASCD.Google Scholar
  34. Magana, A., Brophy, S., & Bodner, G. (2012). Instructors’ intended learning outcomes for using computational simulations as learning tools. Journal of Engineering Education, 101(2), 220–243.  https://doi.org/10.1002/j.2168-9830.2012.tb00049.x.CrossRefGoogle Scholar
  35. Mandinach, E. B., & Cline, H. F. (1996). Classroom dynamics: The impact of a technology-based curriculum innovation on teaching and learning. Journal of Educational Computing Research, 14(1), 83–102.  https://doi.org/10.2190/9MH6-LB76-7EF5-C43M.CrossRefGoogle Scholar
  36. Mayring, P. (2000). Qualitative content analysis. In Basics and techniques (7th edition, first edition 1983). Weinheim: German Studies publishing house.Google Scholar
  37. Mills, J. E., & Treagust, D. F. (2003). Engineering education—Is problem-based or project-based learning the answer. Australasian Journal of Engineering Education, 3(2), 2–16.Google Scholar
  38. Oss, S. (2005). Computers with wings: Flight simulation and personalized landscapes. Journal of Science Education and Technology, 14(1), 117–122.  https://doi.org/10.1007/s10956-005-2739-9.CrossRefGoogle Scholar
  39. Paas, F., Renkl, A., & Sweller, J. (2004). Cognitive load theory: Instructional implications of the interaction between information structures and cognitive architecture. Instructional Science, 32(1), 1–8.  https://doi.org/10.1023/B:TRUC.0000021806.17516.d0.CrossRefGoogle Scholar
  40. Pallant, A., & Tinker, R. F. (2004). Reasoning with atomic-scale molecular dynamic models. Journal of Science Education and Technology, 13(1), 51–66.  https://doi.org/10.1023/B:JOST.0000019638.01800.d0.CrossRefGoogle Scholar
  41. Paton, B., & Dorst, K. (2011). Briefing and reframing: A situated practice. Design Studies, 32(6), 573–587.  https://doi.org/10.1016/j.destud.2011.07.002.CrossRefGoogle Scholar
  42. Prendergast, L., & Etkina, E. (2014). Review of a first-year engineering design course. In Proceedings of the 121st American Society for Engineering Education Annual Conference & Exposition, Indianapolis: American Society for Engineering Education. https://peer.asee.org/review-of-a-first-year-engineering-design-course. Accessed Jan 25 2018.
  43. Purzer, Hathaway, Adams, Xie, & Nourian. (2015). An exploratory study of informed engineering design behaviors associated with scientific explanations. International Journal of STEM education, 2(1), 9.  https://doi.org/10.1186/s40594-015-0019-7.CrossRefGoogle Scholar
  44. Ramos, J., & Yokomoto, C. F. (1999). Making probabilistic methods real, relevant, and interesting using MATLAB. Paper presented at the 29th Frontiers in Education Conference (FIE). San Juan: IEEE.  https://doi.org/10.1109/FIE.1999.840378.
  45. Rates, C., Mulvey, B., & Feldon, D. (2016). Promoting conceptual change for complex systems understanding: Outcomes of an agent-based participatory simulation. Journal of Science Education and Technology, 25(4), 610–627.  https://doi.org/10.1007/s10956-016-9616-6.CrossRefGoogle Scholar
  46. Robertson, B. F., & Radcliffe, D. F. (2009). Impact of CAD tools on creative problem solving in engineering design. Computer-Aided Design, 41(3), 136–146.  https://doi.org/10.1016/j.cad.2008.06.007.CrossRefGoogle Scholar
  47. Roth, W.-M. (1995). Inventors, copycats, and everyone else: The emergence of shared resources and practices as defining aspects of classroom cultures. Journal of Research in Science Teaching, 30(2), 127–152.CrossRefGoogle Scholar
  48. Schön, D. (1983). The reflective practitioner: How professionals think in action. New York: Basic books, Inc.Google Scholar
  49. Sheppard, S. D., Macatangay, K., Anne, C., & William, S. (2009). Educating Engineers: Designing for the Future of the Field. Stanford: Jossey-bass.Google Scholar
  50. Simon, H. A. (1973). The structure of ill-structured problems. Artificial Intelligence., 4(3-4), 181–201.  https://doi.org/10.1016/0004-3702(73)90011-8.CrossRefGoogle Scholar
  51. Snir, J., Smith, C., & Grosslight, L. (1993). Conceptually enhanced simulations: A computer tool for science teaching. Journal of Science Education and Technology, 2(2), 373–388.  https://doi.org/10.1007/BF00694526.CrossRefGoogle Scholar
  52. Stier, K. W. (2003). Teaching lean manufacturing concepts through project-based learning and simulation. Journal of Industrial Technology, 19(4), 1–6.Google Scholar
  53. Stump, G. S., Hilpert, J. C., Husman, J., Chung, W. T., & Kim, W. (2011). Collaborative learning in engineering students: Gender and achievement. Journal of Engineering Education, 100(3), 475–497.  https://doi.org/10.1002/j.2168-9830.2011.tb00023.x.CrossRefGoogle Scholar
  54. Taleyarkhan, M. R., & Dasgupta, C., & Mendoza-Garcia, J. A., & Magana, A. J., & Purzer, S. (2016), Investigating the impact of an educational CAD modeling tool on student design thinking. In Proceedings of the 123rd ASEE Annual Conference & Exposition. New Orleans: American Society for Engineering Education.  https://doi.org/10.18260/p.25485.
  55. Ulrich, K. T., & Eppinger, S. D. (1995). Product design and development. New York: McGraw-Hill.Google Scholar
  56. Vieira, C., Goldstein, M. H., Purzer, Ş., & Magana, A. J. (2016). Using learning analytics to characterize student experimentation strategies in the context of engineering design. Journal of Learning Analytics, 3(3), 291–317.  https://doi.org/10.18608/jla.2016.33.14.CrossRefGoogle Scholar
  57. Xie, Zhang, Nourian, Pallant, & Hazzard. (2014). Time series analysis method for assessing engineering design processes using a CAD tool. International Journal of Engineering Education, 30(1), 218–230.Google Scholar
  58. Youmans, R. J., & Arciszewski, T. (2012). Design fixation: A cloak of many colors. In J. Gero (Ed.), Design Computing and Cognition'12. Dordrecht: Springer.  https://doi.org/10.1007/978-94-017-9112-0_7.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Computer and Information TechnologyPurdue UniversityWest LafayetteUSA
  2. 2.Northrop GrummanFalls ChurchUSA
  3. 3.Interdisciplinary Program in Educational TechnologyIndian Institute of Technology BombayMumbaiIndia
  4. 4.School of Engineering EducationPurdue UniversityWest LafayetteUSA
  5. 5.Institute for Excellence in Engineering EducationUniversity of FloridaGainesvilleUSA

Personalised recommendations