What Engineering Designers Leave Behind: Developing a Cognitive Coding Scheme for Student Design Journals

Westmoreland, Sophoria; Schmidt, Linda C.

doi:10.1115/imece2010-38997

Cited by 5 publications

(2 citation statements)

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“…The assessment of design representations is the foundation for outcome-based studies of design creativity. Various approaches have been developed for the assessment of individual sketches [17,68] and design reports and journals [72,[172][173][174][175]. Some studies employ expert judges [10,108,[176][177][178][179].…”

Section: Discussionmentioning

confidence: 99%

Empirical Studies of Designer Thinking: Past, Present, and Future

Dinar

Shah

Cagan

et al. 2015

Journal of Mechanical Design

100

121

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Understanding how designers think is core to advancing design methods, tools, and outcomes. Engineering researchers have effectively turned to cognitive science approaches to studying the engineering design process. Empirical methods used for studying designer thinking have included verbal protocols, case studies, and controlled experiments. Studies have looked at the role of design methods, strategies, tools, environment, experience, and group dynamics. Early empirical studies were casual and exploratory with loosely defined objectives and informal analysis methods. Current studies have become more formal, factor controlled, aiming at hypothesis testing, using statistical design of experiments (DOE) and analysis methods such as analysis of variations (ANOVA). Popular pursuits include comparison of experts and novices, identifying and overcoming fixation, role of analogies, effectiveness of ideation methods, and other various tools. This paper first reviews a snapshot of the different approaches to study designers and their processes. Once the current basis is established, the paper explores directions for future or expanded research in this rich and critical area of designer thinking. A variety of data may be collected, and related to both the process and the outcome (designs). But there are still no standards for designing, collecting and analyzing data, partly due to the lack of cognitive models and theories of designer thinking. Data analysis is tedious and the rate of discoveries has been slow. Future studies may need to develop computer based data collection and automated analyses, which may facilitate collection of massive amounts of data with the potential of rapid advancement of the rate of discoveries and development of designer thinking cognitive models. The purpose of this paper is to provide a roadmap to the vast literature for the benefit of new researchers, and also a retrospective for the community.

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Section: Discussionmentioning

confidence: 99%

Empirical Studies of Designer Thinking: Past, Present, and Future

Dinar

Shah

Cagan

et al. 2015

Journal of Mechanical Design

100

121

View full text Add to dashboard Cite

show abstract

“…A very recent study by Aurigemma et al (2013) uses a novel combination of ethnographic studies and cognitive-historical analysis to study various representations and artifacts employed in the iterative development of a lab-on-a-chip device. In a similar way, Westmoreland (2012) uses a mixed-method approach to understand cognitive patterns in the design process by examining design journals by students.…”

Section: Introductionmentioning

confidence: 99%

Spanning the complexity chasm: A research approach to move from simple to complex engineering systems

Viswanathan

Linsey

2014

AIEDAM

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This paper presents a multi-study approach that allows design thinking of complex systems to be studied by triangulating causal controlled lab findings with coded data from more complex products. A case-study illustration of this approach is presented here. During the conceptual design of engineering systems, designers face many cognitive challenges including design fixation, errors in their mental models and the Sunk Cost Effect. These factors need to be mitigated for the generation of effective ideas.Understanding the effects of these challenges in a realistic and complex engineering system is especially difficult due to a variety of factors influencing the results. Studying the design of such systems in a controlled environment is extremely challenging due to the scale and complexity of such systems and the time needed to design the systems. Considering said challenges, this paper presents a mixed method approach for studying the design thinking of complex engineering systems. This approach includes a controlled experiment with a simple system and a qualitative cognitive-artifacts study on more complex engineering systems followed by the triangulation of results. The triangulated results provide more generalizable information for complex system design thinking. This method combines the advantages of quantitative and qualitative study methods, making them more powerful while studying complex engineering systems. This paper illustrates the proposed method further using an illustrative study on the cognitive effects of physical models during the design of engineering systems.

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