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

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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.

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References

  • Adams, R. (2001). Cognitive processes in iterative design behavior. University of Washington, Unpublished doctoral dissertation.

  • 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.

  • 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 

  • Adams, R. S., Turns, J., & Atman, C. J. (2003). Educating effective engineering designers: The role of reflective practice. Design Studies, 24(3), 275–294.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Bailey, R., & Szabo, Z. (2006). Assessing engineering design process knowledge. International Journal of Engineering Education, 22(3), 508–518.

    Google Scholar 

  • 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.

  • 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.

    Article  Google Scholar 

  • Brophy, S., Klein, S., Portsmore, M., & Rogers, C. (2008). Advancing engineering education in P-12 classrooms. Journal of Engineering Education, 97(3), 369–387.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • Crismond, D. P., & Adams, R. S. (2012). The informed design teaching and learning matrix. Journal of Engineering Education, 101(4), 738–797.

    Article  Google Scholar 

  • Davies, A. (2011). Making classroom assessment work. Bloomington: Solution Tree.

    Google Scholar 

  • Dorst, K., & Cross, N. (2001). Creativity in the design process: Co-evolution of problem–solution. Design Studies, 22(5), 425–437.

    Article  Google Scholar 

  • Dym, C. L. (1994). Engineering design: A synthesis of views. MA: Cambridge University Press.

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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 

  • 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.

    Article  Google Scholar 

  • 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.

  • Hmelo, C. E., Holton, D. L., & Kolodner, J. L. (2000). Designing to learning about complex systems. Journal of the Learning Sciences, 9, 247–298.

    Article  Google Scholar 

  • Hybs, I., & Gero, J. S. (1992). An evolutionary process model of design. Design Studies, 13(3), 273–290.

    Article  Google Scholar 

  • Ibrahim, R., & Pour Rahimian, F. (2010). Comparison of CAD and manual sketching tools for teaching architectural design. Automation in Construction, 19(8), 978–987.

    Article  Google Scholar 

  • Jansson, D. G., & Smith, S. M. (1991). Design fixation. Design Studies, 12(1), 3–11.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Kafai, Y. B., & Resnick, M. (1996). Constructionism in practice: Designing, thinking, and learning in a digital world. London: Routledge.

    Google Scholar 

  • Kline, S. J. (1985). Innovation is not a linear process. Research Management, 28(4), 36–45.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Mathison, S. (1988). Why triangulate? Educational Researcher, 17(2), 13–17.

    Article  Google Scholar 

  • Pahl, G., & Beitz, W. (1988). Engineering design: a systematic approach. NASA STI/Recon Technical Report A, 89, 47350

  • Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books Inc.

    Google Scholar 

  • Papert, S. (1993). The children’s machine: Rethinking school in the age of the computer. New York: Basic Books.

    Google Scholar 

  • 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 

  • Perttula, M., & Sipilä, P. (2007). The idea exposure paradigm in design idea generation. Journal of Engineering Design, 18(1), 93–102.

    Article  Google Scholar 

  • Petroski, H. (1985). To engineer is human. New York: St. Martin’s Press.

    Google Scholar 

  • Purcell, A. T., & Gero, J. S. (1996). Design and other types of fixation. Design Studies, 17(4), 363–383.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Shute, V., & Ventura, M. (2013). Stealth assessment: Measuring and supporting learning in video games. Cambridge, MA: MIT Press.

    Google Scholar 

  • Sinnott, J. D. (1989). A model for solution of ill-structured problems: Implications for everyday and abstract problem solving. New York: Praeger.

    Google Scholar 

  • Smith, R., & Tjandra, P. (1998). Experimental observation of iteration in engineering design. Research in Engineering Design, 10(2), 107–117.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Vincenti, W. (1990). What engineers know and how they know it. Baltimore and London: The Johns Hopkins University Press.

    Google Scholar 

  • 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).

  • 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.

  • 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.

  • 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.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

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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.

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Authors and Affiliations

  1. Department of Biology, Lynch School of Education, Boston College, 140 Commonwealth Avenue, Chestnut Hill, MA, 02467, USA

    Helen Z. Zhang

  2. The Concord Consortium, 25 Love Lane, Concord, MA, 01472, USA

    Charles Xie & Saeid Nourian

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  1. Helen Z. Zhang
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  2. Charles Xie
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  3. Saeid Nourian
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Correspondence to Helen Z. Zhang.

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Zhang, H.Z., Xie, C. & Nourian, S. Are their designs iterative or fixated? Investigating design patterns from student digital footprints in computer-aided design software. Int J Technol Des Educ 28, 819–841 (2018). https://doi.org/10.1007/s10798-017-9408-1

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