Journal article
Exploring the effect of a visual constraint on students’ design cognition
Artificial intelligence for engineering design, analysis and manufacturing, v 35(1), pp 3-19
Feb 2021
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.
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Details
- Title
- Exploring the effect of a visual constraint on students’ design cognition
- Creators
- Mohammadali Ashrafganjouei - Shahid Bahonar University of KermanJohn S. Gero - University of North Carolina at Charlotte
- Publication Details
- Artificial intelligence for engineering design, analysis and manufacturing, v 35(1), pp 3-19
- Publisher
- Cambridge University Press
- Number of pages
- 17
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Psychological and Brain Sciences (Psychology)
- Web of Science ID
- WOS:000621807500002
- Scopus ID
- 2-s2.0-85097367457
- Other Identifier
- 991022157478204721