Advertisement

Are their designs iterative or fixated? Investigating design patterns from student digital footprints in computer-aided design software

  • Helen Z. ZhangEmail author
  • Charles Xie
  • Saeid Nourian
Article

Abstract

This paper investigates iteration and fixation in design by mining digital footprints left by designers. High school students used computer-aided design software to create buildings in an urban area, with the goal of applying passive solar design techniques to ensure optimal solar gains of the buildings throughout a year. Students were required to complete three different designs. Fine-grained data including design actions, intermediate artifacts, and reflection notes were logged. Computational analytics programs were developed to mine the logs through three indicators: (a) frequency of the action of using energy analysis tools; (b) solar performance of the final designs; and (c) difference in solar performance between the prototype and final designs. Triangulating results from the indicators suggests three types of iteration—efficacious, inadequate and ineffective. Over half of the participants were detected as being efficacious iterative during the first design and becoming more and more fixated toward the end of the project, which resonates with previous findings on fixation effect among college students and professional designers. Overall the results demonstrate the power of applying computational analytics to investigate complex design processes. Findings from this work shed light on how to quantitatively assess and research student performance and processes during design projects.

Keywords

Design fixation Iterative design Computer-aided design Computational analytics Design patterns 

Notes

Funding

This work presented in this manuscript is based upon work supported by the USA National Science Foundation (NSF) under Grant DUE #1348530. Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the NSF.

References

  1. Adams, R. (2001). Cognitive processes in iterative design behavior. University of Washington, Unpublished doctoral dissertation.Google Scholar
  2. Adams, R. S., & Atman, C. J. (1999). Cognitive processes in iterative design behavior. In 29th Annual Frontiers Education Conference, 1999. FIE’99. (vol. 1, pp. 11A6–13). IEEE.Google Scholar
  3. Adams, R. S., Atman, C. J., Nakamura, R., Kalonji, G., & Denton, D. (2002). Assessment of an international freshmen research and design experience: A triangulation study. International Journal of Engineering Education, 18(2), 180–192.Google Scholar
  4. Adams, R. S., Turns, J., & Atman, C. J. (2003). Educating effective engineering designers: The role of reflective practice. Design Studies, 24(3), 275–294.CrossRefGoogle Scholar
  5. Atman, C. J., Adams, R. S., Cardella, M. E., Turns, J., Mosborg, S., & Saleem, J. (2007). Engineering design processes: A comparison of students and expert practitioners. Journal of Engineering Education, 96(4), 359–379.CrossRefGoogle Scholar
  6. 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.CrossRefGoogle Scholar
  7. Atman, C. J., Chimka, J. R., Bursic, K. M., & Nachtmann, H. N. (1999). A comparison of freshman and senior engineering design processes. Design Studies, 20(2), 131–152.CrossRefGoogle Scholar
  8. Bailey, R., & Szabo, Z. (2006). Assessing engineering design process knowledge. International Journal of Engineering Education, 22(3), 508–518.Google Scholar
  9. Baker, R. S., & Clarke-Midura, J. (2013). Predicting successful inquiry learning in a virtual performance assessment for science. In International conference on user modeling, adaptation, and personalization (pp. 203–214). Berlin, Heidelberg: Springer.Google Scholar
  10. Braha, D., & Maimon, O. (1997). The design process: Properties, paradigms, and structure. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 27(2), 146–166.CrossRefGoogle Scholar
  11. Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97(3), 369–387.CrossRefGoogle Scholar
  12. Brown, P. (2009). CAD: Do computers aid the design process after all? Intersect: The Stanford Journal of Science Technology and Society, 2(1), 52–66.Google Scholar
  13. Chrysikou, E. G., & Weisberg, R. W. (2005). Following the wrong footsteps: fixation effects of pictorial examples in a design problem-solving task. Journal of Experimental Psychology. Learning, Memory, and Cognition, 31(5), 1134–1148.CrossRefGoogle Scholar
  14. Crismond, D. P., & Adams, R. S. (2012). The informed design teaching and learning matrix. Journal of Engineering Education, 101(4), 738–797.CrossRefGoogle Scholar
  15. Davies, A. (2011). Making classroom assessment work. Bloomington: Solution Tree.Google Scholar
  16. Dorst, K., & Cross, N. (2001). Creativity in the design process: Co-evolution of problem–solution. Design Studies, 22(5), 425–437.CrossRefGoogle Scholar
  17. Dym, C. L. (1994). Engineering design: A synthesis of views. MA: Cambridge University Press.Google Scholar
  18. 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.CrossRefGoogle Scholar
  19. Feng, M., Heffernan, N., & Koedinger, K. R. (2009). Addressing the assessment challenge with an online system that tutors as it assesses. User Modeling and User-Adapted Interaction, 19(3), 243–266.CrossRefGoogle Scholar
  20. Ferreira, M. M., & Trudel, A. R. (2012). The impact of problem-based learning (PBL) on student attitudes toward science, problem-solving skills, and sense of community in the classroom. The Journal of Classroom Interaction, 47(1), 23.Google Scholar
  21. Fortus, D., Dershimer, R. C., Krajcik, J., Marx, R. W., & Mamlok-Naaman, R. (2004). Design-based science and student learning. Journal of Research in Science Teaching, 41(10), 1081–1110.CrossRefGoogle Scholar
  22. Gertzman, A., & Kolodner, J.L. (1996). A case study of problem-based learning in a middle-school science class: Lessons learned. In Proceedings of ICLS ‘96 (p. 667). Charlottesville, VA: AACE.Google Scholar
  23. Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learning about complex systems. Journal of the Learning Sciences, 9, 247–298.CrossRefGoogle Scholar
  24. Hybs, I., & Gero, J. S. (1992). An evolutionary process model of design. Design Studies, 13(3), 273–290.CrossRefGoogle Scholar
  25. Ibrahim, R., & Pour Rahimian, F. (2010). Comparison of CAD and manual sketching tools for teaching architectural design. Automation in Construction, 19(8), 978–987.CrossRefGoogle Scholar
  26. Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12(1), 3–11.CrossRefGoogle Scholar
  27. Jonassen, D. H. (1997). Instructional design models for well-structured and ill-structured problem-solving learning outcomes. Educational Technology Research and Development, 45(1), 65–94.CrossRefGoogle Scholar
  28. Kafai, Y. B., & Resnick, M. (1996). Constructionism in practice: Designing, thinking, and learning in a digital world. London: Routledge.Google Scholar
  29. Kline, S. J. (1985). Innovation is not a linear process. Research Management, 28(4), 36–45.CrossRefGoogle Scholar
  30. Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., et al. (2003). Problem-Based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design into practice. Journal of the Learning Sciences, 12(4), 495–547.CrossRefGoogle Scholar
  31. Linsey, J. S., Tseng, I., Wood, K. L., Schunn, C., Fu, K., & Cagan, J. (2010). A study of design fixation, its mitigation and perception in engineering design faculty. Journal of Mechanical Design, 132(4), 041003.CrossRefGoogle Scholar
  32. Marsh, R. L., Ward, T. B., & Landau, J. D. (1999). The inadvertent use of prior knowledge in a generative cognitive task. Memory & Cognition, 27(1), 94–105.CrossRefGoogle Scholar
  33. Mathison, S. (1988). Why triangulate? Educational Researcher, 17(2), 13–17.CrossRefGoogle Scholar
  34. Pahl, G., & Beitz, W. (1988). Engineering design: a systematic approach. NASA STI/Recon Technical Report A, 89, 47350Google Scholar
  35. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books Inc.Google Scholar
  36. Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. New York: Basic Books.Google Scholar
  37. Pellegrino, J. W., Chudowsky, N., & Glaser, R. (2001). Knowing what students know: The science and design of educational assessment. Washington, DC: National Academies Press.Google Scholar
  38. Perttula, M., & Sipilä, P. (2007). The idea exposure paradigm in design idea generation. Journal of Engineering Design, 18(1), 93–102.CrossRefGoogle Scholar
  39. Petroski, H. (1985). To engineer is human. New York: St. Martin’s Press.Google Scholar
  40. Purcell, A. T., & Gero, J. S. (1996). Design and other types of fixation. Design Studies, 17(4), 363–383.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. Sadler, P. M., Coyle, H. P., & Schwartz, M. (2000). Engineering competitions in the middle school classroom: Key elements in developing effective design challenges. The Journal of the Learning Sciences, 9(3), 299–327.CrossRefGoogle Scholar
  43. Shute, V., & Ventura, M. (2013). Stealth assessment: Measuring and supporting learning in video games. Cambridge, MA: MIT Press.Google Scholar
  44. Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for everyday and abstract problem solving. New York: Praeger.Google Scholar
  45. Smith, R., & Tjandra, P. (1998). Experimental observation of iteration in engineering design. Research in Engineering Design, 10(2), 107–117.CrossRefGoogle Scholar
  46. Smith, S. M., & Blankenship, S. E. (1991). Incubation and the persistence of fixation in problem solving. The American Journal of Psychology, 104(1), 61–87.CrossRefGoogle Scholar
  47. Suwa, M., Gero, J., & Purcell, T. (2000). Unexpected discoveries and S-invention of design requirements: important vehicles for a design process. Design Studies, 21(6), 539–567.CrossRefGoogle Scholar
  48. Tseng, I., Moss, J., Cagan, J., & Kotovsky, K. (2008). The role of timing and analogical similarity in the stimulation of idea generation in design. Design Studies, 29(3), 203–221.CrossRefGoogle Scholar
  49. Vincenti, W. (1990). What engineers know and how they know it. Baltimore and London: The Johns Hopkins University Press.Google Scholar
  50. Viswanathan, V. K., & Linsey, J. S. (2010). Physical models in idea generation: Hindrance or help?. In ASME 2010 international design engineering technical conferences and computers and information in engineering conference, American Society of Mechanical Engineers (pp. 329–339).Google Scholar
  51. Xie, C., Zhang, Z., Saeid, N., Pallant, A., & Bailey, S. (2014a). On the instructional sensitivity of CAD logs. International Journal of Engineering Education, 30(4), 760–778.Google Scholar
  52. Xie, C., Zhang, Z., Saeid, N., Pallant, A., & Hazzard, E. (2014b). A time series analysis method for assessing engineering design processes using a CAD tool. International Journal of Engineering Education, 30(1), 218–230.Google Scholar
  53. Youmans, R. J., & Arciszewski, T. (2014). Design fixation: Classifications and modern methods of prevention. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 28(02), 129–137.CrossRefGoogle Scholar
  54. Zahner, D., Nickerson, J. V., Tversky, B., Corter, J. E., & Ma, J. (2010). A fix for fixation? Rerepresenting and abstracting as creative processes in the design of information systems. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 24(02), 231–244.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2017

Authors and Affiliations

  1. 1.Department of Biology, Lynch School of EducationBoston CollegeChestnut HillUSA
  2. 2.The Concord ConsortiumConcordUSA

Personalised recommendations