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International Journal of Technology and Design Education

Erschienen in:

22.03.2022

Comparing the design cognitive process between problem-driven and solution-driven industrial design students

verfasst von: Guodong Chen, Qixun Zhao, Pan Rong, Zuting Li, Kong Bei

Erschienen in: International Journal of Technology and Design Education | Ausgabe 2/2023

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Abstract

Industrial design (ID) education involves students with different cognition types. This study aims to explore and compare the design activities of problem-driven and solution-driven students and examine the stability of the design process of the two classifications regarding constraints. Based on the P-S index derived from Function–Behavior–Structure (FBS) ontology, 54 participants were classified into problem-driven and solution-driven classification. Design Issue, Syntactic Design Process, Process Transitions, and P-S index sequence of the two classifications were analyzed and compared. The results showed significant differences in these design activities between the two. Problem-driven students are inclined to constitute statements about the problem repeatedly rather than articulate the design details in the processes of design activity. Solution-driven students focus on the development of solution structure, and the intensity of generate solution is relatively high. It was found that the design cognitive classifications of ID students had relative stability during different experimental conditions. The constraint condition greatly increased the intensity of the design process of solution-driven students, but it was more complicated for the problem-driven students. The findings and results of this study would be useful for evaluating and understanding students’ design activities, and it provides valuable insights and a basis into ID education and practice of different students’ classifications.

Vorheriger Artikel Technology-driven design process: teaching and mentoring technology-driven design process in industrial design education

Nächster Artikel Teaching design for additive manufacturing: efficacy of and engagement with lecture and laboratory approaches

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Ashrafganjouei, M., & Gero, J. S. (2020). Exploring the effect of a visual constraint on students’ design cognition. Artificial Intelligence for Engineering Design Analysis and Manufacturing, 35(1), 1–17. https://doi.org/10.1017/S0890060420000335CrossRef

Ball, L. J., Onarheim, B., & Christensen, B. T. (2010). Design requirements, epistemic uncertainty and solution development strategies in software design. Design Studies, 31(6), 567–589.CrossRef

Basadur, M., & Head, M. (2001). Team performance and satisfaction: A link to cognition type within a process framework. The Journal of Creative Behavior, 35(4), 227–248. https://doi.org/10.1002/j.2162-6057.2001.tb01048.xCrossRef

Biskjaer, M. M., Christensen, B. T., Friis-Olivarius, M., Abildgaard, S. J., Lundqvist, C., & Halskov, K. (2020). How task constraints affect inspiration search strategies. International Journal of Technology and Design Education, 30(1), 101–125.CrossRef

Chan, C. S. (1990). Cognitive processes in architectural design problem solving. Design Studies, 11(2), 60–80.CrossRef

Cheng, P., Mugge, R., & Schoormans, J. (2014). A new strategy to reduce design fixation: Presenting partial photographs to designers. Design Studies., 35(4), 374–391. https://doi.org/10.1016/j.destud.2014.02.004CrossRef

Chevalier, A., & Ivory, M. Y. (2003). Web site designs: Influences of designer’s expertise and design constraints. International Journal of Human-Computer Studies, 58, 57–58.CrossRef

Christiaans, H., & Dorst, K. H. (1992). Cognitive models in industrial design engineering: A protocol study. In D. L. Taylor & D. A. Stauffer (Eds.), Design theory and methodology (DTM92). American Society of Mechanical Engineers.

Crilly, N., & Firth, R. M. (2019). Creativity and fixation in the real world: Three case studies of invention, design and innovation. Design Studies, 64, 169–212.CrossRef

Cross, N. (2006). Designerly ways of knowing. Springer.

Cross, N. (2011). Design thinking: Understanding how designers think and work. Berg.CrossRef

Dorst, K., & Cross, N. (2001). Creativity in the design process: Co-evolution of problem–solution. Design Studies, 22(5), 425–437. https://doi.org/10.1016/S0142-694X(01)00009-6CrossRef

Gero, J. S. (1990). Design prototypes: A knowledge representation schema for design. AI Magazine, 11(4), 26–26. https://doi.org/10.1609/aimag.v11i4.854CrossRef

Gero, J. S., Jiang, H., & Williams, C. B. (2013). Design cognition differences when using unstructured, partially structured, and structured concept generation creativity techniques. International Journal of Design Creativity and Innovation, 1(4), 196–214.CrossRef

Gero, J. S., & Kannengiesser, U. (2004). The situated function–behavior–structure framework. Design Studies, 25(4), 373–391. https://doi.org/10.1016/j.destud.2003.10.010CrossRef

Gero, J. S., Kannengiesser, U., & Pourmohamadi, M. (2014). Commonalities across designing: Empirical results. In J. S. Gero (Ed.), Design computing and cognition 12 (pp. 265–281). Springer.CrossRef

Gero, J. S., & Milovanovic, J. (2020). A framework for studying design thinking through measuring designers’ minds, bodies and brains. Design Science. https://doi.org/10.1017/dsj.2020.15CrossRef

Gero, J. S., & Neill, T. M. (1998). An approach to the analysis of design protocols. Design Studies, 19(1), 21–61. https://doi.org/10.1016/S0142-694X(97)00015-XCrossRef

Goldschmidt, G., & Sever, A. L. (2011). Inspiring design ideas with texts. Design Studies, 32(2), 139–155. https://doi.org/10.1016/j.destud.2010.09.006CrossRef

Hamraz, B., Caldwell, N. M., Ridgman, T. W., & Clarkson, P. J. (2015). FBS linkage ontology and technique to support engineering change management. Research in Engineering Design, 26, 3–35. https://doi.org/10.1007/s00163-014-0181-9CrossRef

Jiang, H. (2012). Understanding senior design students’ product conceptual design activities—A comparison between industrial and engineering design students (Phd thesis). National University of Singapore. https://doi.org/10.1080/21650349.2013.801760.

Jiang, H., Gero, J. S., & Yen, C. C. (2014). Exploring designing types using a problem-solution division. In J. Gero (Ed.), Design computing and cognition 12 (pp. 79–94). Springer.CrossRef

Kan, J. W. (2021). Cognitive design research: A scientific way to investigate design cognition. New Building, 2, 11–15. https://doi.org/10.12069/j.na.202102011CrossRef

Kan, J. W., & Gero, J. S. (2017). Quantitative methods for studying design protocols. Springer.CrossRef

Kan, J. W., & Gero, S. J. (2011) Learning to collaborate during team designing. In ICORD 11: Proceedings of the 3rd International Conference on Research into Design Engineering, Bangalore, India (pp. 687–694).

Kim, E., & Kim, K. (2015). Cognition types in design problem solving: Insights from network-based cognitive maps. Design Studies, 40, 1–38. https://doi.org/10.1016/j.destud.2015.05.002CrossRef

Kruger, C., & Cross, N. (2006). Solution driven versus problem driven design: Strategies and outcomes. Design Studies, 27(5), 527–548. https://doi.org/10.1016/j.destud.2006.01.001CrossRef

Kun, Z. (2021). Research on design knowledge flow of industrial design award from the perspective of designer (Phd thesis). Guangdong University of Technology.

Lammi, M. D., Wells, J. G., & Gero, J. S. (2020). High school pre-engineering students’ engineering design cognition. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-020-09614-wCrossRef

Lawson, B. R. (1979). Cognitive strategies in architectural design. Ergonomics, 22(1), 59–68. https://doi.org/10.1080/00140137908924589CrossRef

Lloyd, P., & Scott, P. (1994). Discovering the design problem. Design Studies, 15(2), 125–140. https://doi.org/10.1016/0142-694X(94)90020-5CrossRef

Lu, C. C. (2015). The relationship between student design cognition types and creative design outcomes. Design Studies, 36, 59–76. https://doi.org/10.1016/j.destud.2014.08.002CrossRef

Lu, C. C. (2017). Interactive effects of environmental experience and innovative cognitive style on student creativity in product design. International Journal of Technology and Design Education, 27(4), 577–594.CrossRef

Maher, M. L., & Poon, J. (1996). Modeling design exploration as co-evolution. Computer-Aided Civil and Infrastructure Engineering, 11(3), 195–209. https://doi.org/10.1111/j.1467-8667.1996.tb00323.xCrossRef

Maher, M., & Tang, H. H. (2003). Co-evolution as a computational and cognitive model of design. Research in Engineering design, 14(1), 47–64.

Menold, J., & Jablokow, K. (2019). Exploring the effects of cognition type diversity and self-efficacy beliefs on final design attributes in student design teams. Design Studies, 60, 71–102. https://doi.org/10.1016/j.destud.2018.08.001CrossRef

Miron-Spektor, E., Erez, M., & Naveh, E. (2011). The effect of conformist and attentive-to-detail members on team innovation: Reconciling the innovation paradox. Academy of Management Journal, 54(4), 740–760. https://doi.org/10.5465/amj.2011.64870100CrossRef

Onarheim, B. (2012). Creativity from constraints in engineering design: Lessons learned at Coloplast. Journal of Engineering Design, 23(4), 323–336.CrossRef

Poon, J., & Maher, M. L. (1997). Co-evolution and emergence in design. Artificial Intelligence in Engineering, 11(3), 319–327. https://doi.org/10.1016/S0954-1810(96)00047-7CrossRef

Popovic, V. (2004). Expertise development in product design-strategic and domain-specific knowledge connections. Design Studies, 25(5), 527–545. https://doi.org/10.1016/j.destud.2004.05.006CrossRef

Savage, J. C., Moore, C. J., Miles, J. C., & Miles, C. (1998). The interaction of time and cost constraints on the design process. Design Studies, 19(2), 217–233.CrossRef

Schon, D. A. (1983). The reflective practitioner: How professionals think in action. Administrative Science Quarterly, 32(4), 614. https://doi.org/10.2307/2392894CrossRef

Sternberg, R. J., & Grigorenko, E. L. (1997). Are cognition types still in types. American Psychologist, 52(7), 700–712. https://doi.org/10.1037/0003-066X.52.7.700CrossRef

Sun, L. Y., Xiang, W., & Chai, C. L. (2014). Creative segment: a descriptive theory applied to computer-aided sketching. Design Studies, 35, 54–79. https://doi.org/10.1016/j.destud.2013.10.003CrossRef

Valkenburg, R., & Dorst, K. (1998). The reflective practice of design teams. Design Studies, 19(3), 249–271. https://doi.org/10.1016/S0142-694X(98)00011-8CrossRef

Van, S. M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994). The think aloud method: A practical approach to modelling cognitive. Academic Press.

WDO-World Design Organization. (2015). At the 29th general assembly in Gwangju (South Korea). Retrieved March 22, 2022 from https://wdo.org/about/definition/

Xia, H. X. (2006). On Dunn’s learning type model and its teaching implication. 6, 1–7.

Yang, Z., Xiang, W., You, W., & Sun, L. (2020). The influence of distributed collaboration in design processes: An analysis of design activity on information, problem, and solution. International Journal of Technology and Design Education. https://doi.org/10.1007/s10798-020-09565-2CrossRef

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. AI EDAM, 24(2), 231–244.

Titel: Comparing the design cognitive process between problem-driven and solution-driven industrial design students
verfasst von: Guodong Chen
Qixun Zhao
Pan Rong
Zuting Li
Kong Bei
Publikationsdatum: 22.03.2022
Verlag: Springer Netherlands
Erschienen in: International Journal of Technology and Design Education / Ausgabe 2/2023
Print ISSN: 0957-7572
Elektronische ISSN: 1573-1804
DOI: https://doi.org/10.1007/s10798-022-09740-7

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