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Exploring the effect of a visual constraint on students’ design cognition

Published online by Cambridge University Press:  04 December 2020

Mohammadali Ashrafganjouei Open the ORCID record for Mohammadali Ashrafganjouei [Opens in a new window]  and
John S. Gero Open the ORCID record for John S. Gero [Opens in a new window]
Mohammadali Ashrafganjouei*
Affiliation:
Saba Faculty of Arts and School of Architecture, Shahid Bahonar University of Kerman, Kerman, Iran
John S. Gero
Affiliation:
Department of Computer Science and School of Architecture, University of North Carolina, Charlotte, NC28223, USA
*
Author for correspondence: Mohammadali Ashrafganjouei, E-mail: m_aganjouei@uk.ac.ir
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Abstract

This paper presents the results of a study that explores the effect of a visual constraint on design behaviors of architecture students. To examine this effect, 24 second-year architecture students volunteered to participate. Each of them undertook similar conceptual design briefs in two different conditions, one with and another without a visual constraint. Retrospective reporting was used to collect the verbalization of participants. The FBS ontology was used to model the design cognition of the participants by coding their design protocols. A dynamic analysis was used to study the differences between the two conditions based on the problem–solution index. A further index, the pre-structure–post-structure index, was proposed to measure design behavior differences between the two conditions. The correspondence analysis was used to explore the effect of gender. There were statistically significant differences in the distributions of cognitive effort between the two groups. These differences include in the visual constraint group a decrease in the focus on behavior before structure and in the processes related to it, compared to the non-visual constraint group. The non-visual constraint group changed their focus on problem framing and solving while adding a visual constraint led participants to focus simultaneously on both framing and solving. The visual constraint group had a different attention temporally to pre- and post-structure design processes during designing than the non-visual constraint group. The order of experiencing the two design sessions had only a small effect. The results of correspondence analysis demonstrate that there are categorical gender differences not found using statistical testing.

Keywords

Design cognitiondesign studentsFBS ontologyvisual constraint
Type
Research Article
Information
AI EDAM , Volume 35 , Issue 1 , February 2021 , pp. 3 - 19
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Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Alelyani, T, Yang, Y and Grogan, PT (2017) Understanding designers behavior in parameter design activities. ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, August 6–9. Cleveland, Ohio: American Society of Mechanical Engineers, pp. V007T06A030–V007T06A030.CrossRefGoogle Scholar
Atman, CJ and Bursic, KM (1998) Verbal protocol analysis as a method to document engineering student design processes. Journal of Engineering Education 87, 121–132.CrossRefGoogle Scholar
Ball, LJ, Ormerod, TC and Morley, NJ (2004) Spontaneous analogising in engineering design: a comparative analysis of experts and novices. Design Studies 25, 495–508.CrossRefGoogle Scholar
Bannerot, R (2006) How women perform on individual design projects compared to men. Proc. from the 2006 ASEE Annual Conference & Exposition, Chicago, IL.Google Scholar
Bilda, Z and Gero, JS (2008) Idea development can occur using imagery only. In Gero, JS and Goel, AK (eds), Design Computing and Cognition’08. New York: Springer, pp. 303–320.CrossRefGoogle Scholar
Biskjaer, MM, Christensen, BT, Friis-Olivarius, M, Abildgaard, SJ, Lundqvist, C and Halskov, K (2019) How task constraints affect inspiration search strategies. International Journal of Technology and Design Education. doi:10.1007/s10798-019-09496-7.Google Scholar
Bott, MJ and Mesmer, B (2019). Determination of function-behavior-structure model transition probabilities from real-world data. AIAA SciTech 2019 Forum, San Diego, CA. doi:10.2514/6.2019-1030.CrossRefGoogle Scholar
Byrnes, JP, Miller, DC and Schafer, WD (1999) Gender differences in risk taking: a meta-analysis. Psychological Bulletin 125, 367–383.CrossRefGoogle Scholar
Camba, JD, Kimbrough, M and Kwon, E (2018) Conceptual product design in digital and traditional sketching environments: a comparative exploratory study. Journal of Design Research 16, 131–154.CrossRefGoogle Scholar
Cardoso, C and Badke-Schaub, P (2011) The influence of different pictorial representations during idea generation. Journal of Creative Behavior 45, 130–146.CrossRefGoogle Scholar
Casakin, H (2010) Visual analogy, visual displays, and the nature of design problems: the effect of expertise. Environment and Planning B: Planning and Design 37, 170–188.CrossRefGoogle Scholar
Casakin, H and Goldschmidt, G (2000) Reasoning by visual analogy in design problem-solving: the role of guidance. Environment and Planning B: Planning and Design 27, 105–119.CrossRefGoogle Scholar
Chu, PY, Hung, HY, Wu, CF and Te Liu, Y (2017) Effects of various sketching tools on visual thinking in idea development. International Journal of Technology and Design Education 27, 291–306.CrossRefGoogle Scholar
Clinton, G and Hokanson, B (2012) Creativity in the training and practice of instructional designers: the Design/Creativity Loops model. Educational Technology Research and Development 60, 111–130.CrossRefGoogle Scholar
Cohen, J (1960) A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20, 37–46.CrossRefGoogle Scholar
Eastman, C (2001) New directions in design cognition: studies of representation and recall. In Eastman, C (ed.), Knowing and Learning to Design: Cognition in Design Education. Oxford: Elsevier, pp. 1–46.Google Scholar
Ericsson, KA and Simon, HA (1993) Protocol Analysis (Revised version). Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Felder, RM, Felder, GN, Mauney, M, Hamrin, CE Jr and Dietz, EJ (1995) A longitudinal study of engineering student performance and retention. III. Gender differences in student performance and attitudes. Journal of Engineering Education 84, 151–163.CrossRefGoogle Scholar
Fu, K, Cagan, J and Kotovsky, K (2010) Design team convergence: the influence of example solution quality. Journal of Mechanical Design 132, 111005–111011.CrossRefGoogle Scholar
Gero, JS (1990) Design prototypes: a knowledge representation schema for design. AI Magazine 11, 26–36.Google Scholar
Gero, JS and Kannengiesser, U (2004) The situated function–behaviour–structure framework. Design Studies 25, 373–391.CrossRefGoogle Scholar
Gero, JS and Kannengiesser, U (2014) The function-behaviour-structure ontology of design. In Chakrabarti, A and Blessing, L (eds), An Anthology of Theories and Models of Design. Netherlands: Springer, pp. 263–283.CrossRefGoogle Scholar
Gero, JS and Tang, HH (2001) The differences between retrospective and concurrent protocols in revealing the process-oriented aspects of the design process. Design Studies 22, 283–295.CrossRefGoogle Scholar
Goel, V (1995) Sketches of Thought. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Goldschmidt, G (2011) Avoiding design fixation: transformation and abstraction in mapping from source to target. The Journal of creative behavior 45, 92–100.CrossRefGoogle Scholar
Goldschmidt, G and Sever, AL (2011) Inspiring design ideas with texts. Design Studies 32, 139–155.CrossRefGoogle Scholar
Goldschmidt, G and Smolkov, M (2006) Variances in the impact of visual stimuli on design problem solving performance. Design Studies 27, 549–569.CrossRefGoogle Scholar
Gonçalves, M, Cardoso, C and Badke-Schaub, PG (2012) Find your inspiration: Exploring different levels of abstraction in textual stimuli. Proceedings of the 2nd International Conference on Design Creativity. Glasgow, UK: The Design Society, p. ICDC2012.Google Scholar
Gonçalves, M, Cardoso, C and Badke-Schaub, PG (2016) Inspiration choices that matter: the selection of external stimuli during ideation. Design Science 2, 1–31.CrossRefGoogle Scholar
Hamraz, B, Caldwell, NHM, Ridgman, TW and Clarkson, PJ (2015) FBS linkage ontology and technique to support engineering change management. Research in Engineering Design 26, 3–35.CrossRefGoogle Scholar
Heylighen, A and Verstijnen, IM (2003) Close encounters of the architectural kind. Design Studies 24, 313–326.CrossRefGoogle Scholar
Hocking, D (2014) The brief in art and design education: a multi-perspectival and mixed-methodological study (Unpublished PhD thesis). Macquarie University, Sydney, Australia.Google Scholar
Hofmeister, C, Kruchten, P, Nord, RL, Obbink, H, Ran, A and America, P (2007) A general model of software architecture design derived from five industrial approaches. Journal of Systems and Software 80, 106–126.CrossRefGoogle Scholar
Jiang, H, Gero, JS and Yen, CC (2014) Exploring designing styles using a problem–solution division. In Gero, JS (ed.), Design Computing and Cognition’12. Netherlands: Springer, pp. 85–101.Google Scholar
Kan, JWT and Gero, JS (2009) Using the FBS ontology to capture semantic design information in design protocol studies. In McDonnell, J and Lloyd, P (eds), About Designing: Analyzing Design Meetings. Abingdon: Taylor and Francis, pp. 213–229.Google Scholar
Kan, JWT and Gero, JS (2017) Quantitative Methods for Studying Design Protocols. Netherlands: Springer.CrossRefGoogle Scholar
Lawson, B (2006) How Designers Think: The Design Process Demystified. London: Routledge.CrossRefGoogle Scholar
Linsey, JS, Wood, KL and Markman, AB (2008) Modality and representation in analogy. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 22, 85–100.CrossRefGoogle Scholar
Lu, CC (2015) The relationship between student design cognition types and creative design outcomes. Design Studies 36, 59–76.CrossRefGoogle Scholar
Magolda, MBB (1992) Knowing and Reasoning in College: Gender-Related Patterns in Students’ Intellectual Development. San Francisco: Jossey Bass.Google Scholar
Masclet, C and Boujut, JF (2010) Using situated FBS to model design interactions in a distant synchronous collaborative situation. DESIGN2010, Dubrovnik, Croatia, pp. 1585–1594.Google Scholar
Milovanovic, J and Gero, JS (2018) Exploration of cognitive design behavior during design critiques, DESIGN 2018. In Marjanovic, D, Clarkson, PJ, Lindemann, U, McAloone, T and Weber, C (eds), Human Behavior in Design, Vol. 5. Dubrovnik, Croatia: Design Society, pp. 2099–2110.Google Scholar
Milovanovic, J and Gero, JS (2019) Exploration of gender diversity effects on design team dynamics. In Eriksson, Y and Paetzold, K (eds), Human Behavior in Design. Germany: University of Munich, pp. 101–112.Google Scholar
Mitchell, ML and Jolley, JM (2012) Research Design Explained, 8th Edn. Balmont, CA: Wadsworth Publishing.Google Scholar
Muth, C, Hesslinger, VM and Carbon, C-C (2015) The appeal of challenge in the perception of art: how ambiguity, solvability of ambiguity, and the opportunity for insight affect appreciation. Psychology of Aesthetics, Creativity, and the Arts 9, 206–216.CrossRefGoogle Scholar
Newcombe, N, Bandura, MM and Taylor, DG (1983) Sex differences in spatial ability and spatial activities. Sex Roles 9, 377–386.Google Scholar
Onarheim, B (2012) Creativity from constraints in engineering design: lessons learned at Coloplast. Journal of Engineering Design 23, 323–336.CrossRefGoogle Scholar
Ozkan, O and Dogan, F (2013) Cognitive strategies of analogical reasoning in design: differences between expert and novice designers. Design Studies 34, 161–192.CrossRefGoogle Scholar
Pauwels, P, Strobbe, T and De Meyer, R (2015) Analysing how constraints impact architectural decision-making. International Journal of Design Sciences and Technology 21, 83–111.Google Scholar
Pourmohamadi, M and Gero, JS (2011) LINKOgrapher: an analysis tool to study design protocols based on FBS coding scheme. In Culley, S, Hicks, B, McAloone, T, Howard, T and Reich, Y (eds), Design Theory and Methodology. Glasgow, UK: Design Society, pp. 294–303.Google Scholar
Purcell, AT and Gero, JS (1992) Effects of examples on the results of a design activity. Knowledge-Based Systems 5, 82–91.CrossRefGoogle Scholar
Schon, DA and Wiggins, G (1992) Kinds of seeing and their functions in designing. Design Studies 13, 135–156.CrossRefGoogle Scholar
Self, J, Evans, MA and Kim, E (2016) A comparison of digital and conventional sketching: implications for conceptual design ideation. Journal of Design Research 14, 171–202.CrossRefGoogle Scholar
Sio, UN, Kotovsky, K and Cagan, J (2015) Fixation or inspiration? A meta-analytic review of the role of examples on design processes. Design Studies 39, 70–99.CrossRefGoogle Scholar
Stones, C and Cassidy, T (2010) Seeing and discovering: how do student designers reinterpret sketches and digital marks during graphic design ideation? Design Studies 31, 439–460.CrossRefGoogle Scholar
Strimel, GJ, Kim, E, Bartholomew, SR and Cantu, D (2018) Examining engineering design cognition with respect to student experience and performance. International Journal of Engineering Education 34, 1910–1929.Google Scholar
Suits, JP and Lagowski, JJ (1994) Chemistry problem-solving abilities: gender, reasoning level and computer-simulated experiments. Annual Meeting of the National Association for Research in Science Teaching, Anaheim, CA, pp. 26–29.Google Scholar
Suwa, M and Tversky, B (1997) What do architects and students perceive in their design sketches? A protocol analysis. Design Studies 18, 385–403.CrossRefGoogle Scholar
Suwa, M, Purcell, T and Gero, J (1998) Macroscopic analysis of design processes based on a scheme for coding designers’ cognitive actions. Design Studies, 19, 455–483.CrossRefGoogle Scholar
Tversky, B and Chou, JY (2011) Creativity: depth and breadth. In Y. Nagai (Ed.). Design Creativity 2010, November 29- December 1. Dordrecht, Netherlands: Springer, pp. 209–214.CrossRefGoogle Scholar
Webster, MA (2015) Visual adaptation. Annual Review of Vision Science 1, 547–567.CrossRefGoogle ScholarPubMed
Whigham, MA (1988) Gender-related differences in engineering students. NACADA Journal 8, 35–45.CrossRefGoogle Scholar
Williams, CB, Lee, Y, Gero, JS and Paretti, MC (2013) Exploring the effects of the design prompt on students' design cognition. ASME 2013 IDETC, August 4–7, Portland, OR, pp. V001T04A016–V001T04A016.Google Scholar
Xie, C, Zhang, Z, Nourian, S, Pallant, A and Hazzard, E (2014) A time series analysis method for assessing engineering design processes using a CAD tool. International Journal of Engineering Education 30, 218–230.Google Scholar
Yilmaz, S, Daly, SR, Seifert, CM and Gonzalez, R (2015) How do designers generate new ideas? Design heuristics across two disciplines. Design Science 1, 1–29.CrossRefGoogle Scholar
Zahner, D, Nickerson, JV, Tversky, B, Corter, JE and Ma, J (2010) A fix for fixation? Representing and abstracting as creative processes in the design of information systems. AI EDAM 24, 231–244.Google Scholar