Hierarchical thinking: a cognitive tool for guiding coherent decision making in design problem solving

Abstract

This paper builds on two concepts, the first of which is the extended information processing model of expert design cognition. This proposes twelve internal psychological characteristics interacting with the external world of expert designers during the early phases of the design process. Here, I explore one of the characteristics, hierarchical abstraction, and adapt it into an alternative ontological model of decision making. The model serves as an in-depth descriptor of how designers from different domains transform their mental states using judgment and decision making through hierarchical abstraction. The second concept entails an expansion of the idea of synergistic vertical transformation as a framework for mapping expert designers’ design process. Here, I focus on hierarchical decision making as multi-directional, and inter-relating the internal and external world of designers. In doing so, I provide a coding tool for researchers interested in exploring designers’ complex decision making processes. Concurrently, the model serves as decision making tool in design and technology education classrooms. As such, the paper focuses on the ontology of conceptual structures that support the early phases of the design process. This was based on empirical research.

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References

  1. Anderson, M. L. (2003). Embodied cognition: A field guide. Artificial Intelligence, 149(1), 91–130.

    Article  Google Scholar 

  2. Anthony, W. S. (1973). Learning to discover rules by discovery. Journal of Educational Psychology, 64, 325–328.

    Article  Google Scholar 

  3. Basden, A. (2000). The aspectual framework of meaning. Retrieved from The Dooyeweerd Pages website. http://www.dooy.salford.ac.uk/contact.html.

  4. Brandstatter, V., Heimbeck, D., Malzacher, J. T., & Frese, M. (2003). Goals need implementation intentions: The model of action phases tested in the applied setting of continuing education. European Journal of Work and Organizational Psychology, 12(1), 37–59.

    Article  Google Scholar 

  5. Buzan, T. (2005). Mind map handbook. London: Thorsons.

    Google Scholar 

  6. Cascetta, E. (2001). Transportation systems engineering: Theory and methods. Dordrecht: Springer.

    Google Scholar 

  7. Collins, A., Brown, J. S., & Newman, S. E. (1987). Cognitive apprenticeship: Teaching the craft of reading, writing, and mathematics. Illinois: University of Illinois at Urbana-Champaign.

    Google Scholar 

  8. Conlan, T. (2006). Formative assessment of classroom concept maps: The reasonable fallible analyser. Journal of Interactive Learning Research, 17(1), 15–36.

    Google Scholar 

  9. Cross, N. (2001). Design cognition: Results from protocol and other empirical studies of design activity. In C. Eastman, M. McCracken, & W. Newstetter (Eds.), Design knowing and learning: Cognition in design education. Oxford: Elsevier.

    Google Scholar 

  10. de Miranda, M. A. (2004). The grounding of a discipline: Cognition and instruction in technology education. International Journal of Technology and Design Education, 14, 61–77.

    Article  Google Scholar 

  11. de Vries, M. J. (2006). Technological knowledge and artifacts: An analytical view. In J. R. Dakers (Ed.), Defining technological literacy. Towards an epistemological framework. New York: Pelgrave MacMillan.

    Google Scholar 

  12. De Vries, M., Custer, R. L., Dakers, J. R., & Martin, G. (2007). Analyzing best practices in technology educatiion. Rotterdam: Sense Publishers.

    Google Scholar 

  13. Edelson, D. C., Gordin, D. N., & Pea, R. D. (1999). Addressing the challenges of inquiry-based learning through technology and curriculum design. The Journal of the Learning Sciences, 8(3&4), 391–450.

    Article  Google Scholar 

  14. Eder, W. E. (2012). Comparisons of several design theories and methods with the legacy of Vladimir Hubka.

  15. Epley, N., & Gilovich, T. (2006). The anchoring-and-adjustment heuristic. Why the adjustments are insufficient. Psychological Science, 17(4), 311–318.

    Article  Google Scholar 

  16. Ertmer, P. A., & Newby, T. J. (2013). Behaviorism, cognitivism, constructivism: Comparing critical features from an instructional design perspective. Performance Improvement Quarterly, 26(2), 43–71.

    Article  Google Scholar 

  17. Fox, J., Cooper, R. P., & Glasspool, D. W. (2013). A canonical theory of dynamic decision-making. Frontiers in Psychology, 4(150), 1–19.

    Google Scholar 

  18. Gavrilova, T., Leshcheva, I., & Strakhovich, E. (2015). Gestalt principles of creating learning business ontologies for knowledge codification. Knowledge Management Research & Practice, 13(4), 418–428.

    Article  Google Scholar 

  19. Gero, J. S., & Kannengieser, U. (2004). The situated function-behaviour-structure framework. Design Studies, 25, 373–391.

    Article  Google Scholar 

  20. Gibson, J. J. (1986). The ecological approach to perception. Hillside, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  21. Gigerenzer, G., & Goldstein, D. G. (1996). Reasoning the fast and frugal way: Models of bounded rationality. Psychological Review, 103(4), 650–669.

    Article  Google Scholar 

  22. Goel, V. (1995). Sketches of thought. Cambridge: MIT Press.

    Google Scholar 

  23. Goldstein, W. M., & Hogarth, R. M. (1997). Judgment and decision research: Some historical context. In W. M. Goldstein & R. M. Hogarth (Eds.), Research on judgment and decision making: Currents, connections and controversies (pp. 3–68). Cambridge: Cambridge University Press.

    Google Scholar 

  24. Gollwitzer, P. M., & Schaal, B. (1998). Metacognition in action: The importance of implementation intentions. Personality and Social Psychology Review, 2(2), 124–136.

    Article  Google Scholar 

  25. Hastie, R. (2001). Problems for judgment and decision making. Annual Review Psychology, 52, 653–683.

    Article  Google Scholar 

  26. Haupt, G. (2013). The cognitive dynamics of socio-technological thinking in the early phases of expert designers’ design process. Unpublished PhD, University of Pretoria, Pretoria.

  27. Haupt, G. (2015). Learning from experts: Fostering extended thinking in the early phases of the design process. International Journal of Technology and Design Education, 25(4), 483–520.

    Article  Google Scholar 

  28. Hennessy, S. (1993). Situated cognition and cognitive apprenticeship: Implications for classroom learning. Studies in Science Education, 22(1), 1–44.

    Article  Google Scholar 

  29. Hofweber, T. (2014). Logic and ontology. In E. N. Zalta (Ed.), The stanford Encyclopedia of philosophy (Vol. Fall 2014 ed.). Stanford: Standford University.

  30. Johnson, S. D., & Daugherty, J. (2008). Quality and characteristics of recent research in technology education. Journal of Technology Education, 20(1), 16–31.

    Article  Google Scholar 

  31. Jonassen, D. (1998). Designing constructivist learning environments. In C. M. Reigeluth (Ed.), Instructional design models and strategies (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  32. Katsikopoulos, K. V. (2009). Coherence and correspondence in engineering design: Informing the conversation and connecting with judgment and decision-making research. Judgment and Decision Making, 4(2), 147–153.

    Google Scholar 

  33. Kirschner, P. A., Sweller, J., & Clark, R. E. (2006). Why minimal guidance during instruction does not work: An analysis of the failure of constructivist, discover, problem-based, experiential, and inquiry-based teaching. Educational Psychologist, 41(2), 75–86.

    Article  Google Scholar 

  34. Kluge, P., & Malan, D. F. (2011). The application of the analytical hierarchical process in complex mining engineering design problems. The Journal of the South African Institute of Mining and Metallurgy, 111(December), 847–855.

    Google Scholar 

  35. Kroes, P. A. (2002). Design methodology and the nature of technical artefacts. Design Studies, 23, 287–302.

    Article  Google Scholar 

  36. Kroes, P. A., & Meijers, A. (2002). The dual nature of technical artifacts. Techné, 6(2), 4–8.

  37. Lawson, B. (2006). How designers think. Boston: Elsevier.

    Google Scholar 

  38. Mitcham, C. (2002). Do artifacts have dual natures? Two points of commentary on the delft project. Techné, 6(2), 93–95.

    Google Scholar 

  39. Mitcham, C., & Holbrook, J. B. (2006). Understanding technological design. In J. S. Dakers (Ed.), Defining technological literacy. Towards an epistemological framework. New York: Palgrave MacMillan.

    Google Scholar 

  40. Oxman, R. (2002). The thinking eye: Visual re-cognition in design emergence. Design Studies, 23(2), 135–164.

    Article  Google Scholar 

  41. Oxman, R. (2004). Think-maps: Teaching design thinking in design education. Design Studies, 25(1), 63–91.

    Article  Google Scholar 

  42. Petrina, S. (2007). Advanced teaching methods for the technology classroom. London: Information Science Publishing.

    Google Scholar 

  43. Robbins, P. (2009). The Cambridge handbook of situated cognition. Cambridge: Cambridge University Press.

    Google Scholar 

  44. Savin-Baden, M. (2007). Challenging PBL models and perspectives. In E. de Graaf & A. Kolmos (Eds.), Management of change: Implementation of problem-based and project-based learning in engineering. Rotterdam: Sense Publishers.

    Google Scholar 

  45. Schön, D. (1984). Problems, frames and perspectives on designing. Design Studies, 5(3), 135–156.

    Google Scholar 

  46. Seram, N. (2013). Decision making in product development—a review of the literature. International Journal of Engineering and Applied Sciences, 2(4), 1–11.

    Google Scholar 

  47. Simon, H. A. (1996). The sciences of the artificial (3rd ed.). Cambridge, MA: MIT Press.

    Google Scholar 

  48. Sowa, J. F. (1984). Conceptual structures: Information processing in mind and machine. Reading, MA: Addison-Wesley.

    Google Scholar 

  49. Suwa, M., Purcell, T., & Gero, J. (1998). Macroscopic analysis of design processes based on a scheme for coding designers’ cognitive actions. Design Studies, 19(4), 455–483.

    Article  Google Scholar 

  50. Suwa, M., & Tversky, B. (1996). What architects see in their design sketches: Implications for design tools. Paper presented at the Human Factors in Computing Systems. ACM, New York.

  51. Tversky, A., & Kahneman, D. (1974). Judgement under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.

    Article  Google Scholar 

  52. Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions. The Journal of Business, 59(4), S251–S278.

    Article  Google Scholar 

  53. Tversky, A., & Simonson, I. (1993). Context-dependent preferences. Management Science, 39(10), 1179–1189.

    Article  Google Scholar 

  54. Verkerk, M. J., Hoogland, J., van der Stoep, J., & de Vries, M. J. (2007). Denken Ontwerpen Maken. Basisboek Techniekfolosofie. Amsterdam: Boom.

    Google Scholar 

  55. Wagemans, J., Elder, J. H., Kubovv, M., Palmer, S. E., Peterson, M. A., Singh, M., & van der Heydt, R. (2012). A century of Gestalt psychology in visual perception 1. Perceptual grouping and figure-ground organisation. Psychology Bulletin, 138(6), 1172–1217.

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Correspondence to Grietjie Haupt.

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Haupt, G. Hierarchical thinking: a cognitive tool for guiding coherent decision making in design problem solving. Int J Technol Des Educ 28, 207–237 (2018). https://doi.org/10.1007/s10798-016-9381-0

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Keywords

  • Design cognition
  • Decision making
  • Hierarchical thinking
  • 4-Level decision making tool
  • Problem solving
  • Intentions
  • Multi-directional transformation