Research Investigating the relation between problem formulation and creativity in design, and finding differences among designers in adopting problem formulation strategies

Investigating the relation between problem formulation and creativity in design, and finding differences among designers in adopting problem formulation strategies

Design problem formulation is believed to influence creativity. Because of this unknown influence, the main objective of my research was to understand how problem formulation affects creative outcome. I had to investigated three research areas: development of a model which facilitates capturing the differences among designers’ problem formulation; representation and implication of those differences; the relation between problem formulation and creativity. I created the Problem Map (P-maps) ontological framework. P-maps represent designers’ problem formulation in terms of six groups of entities (requirement, use scenario, function, artifact, behavior, and issue). Entities have hierarchies within each group and links among groups. Variables extracted from P-maps characterize problem formulation.

I conducted three experiments for a scientific answer to the research question. The first experiment was to study the similarities and differences between novice and expert designers. As a result, I found that experts used more abstraction than novices did and novices were more likely to add entities in a specific order. Experts also discovered more issues. The second experiment was to see how problem formulation relates to creativity. Ideation metrics were used to characterize creative outcome. I discovered the patterns of creative design from multiple linear regression models as well as association rule mining and decision trees. The third experiment was to see if problem formulation can predict creative outcome. Models based on one problem were used to predict the creativity of another. Predicted scores were compared to assessments of independent judges. Quality and novelty were predicted more accurately than variety, and quantity. I used a data mining method in feature selection, backward elimination, which improved model fit, though reduced prediction accuracy. My ontological and computational representation, P-maps, provided a novel and theoretical framework for formalizing, tracing, and quantifying conceptual design strategies.

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