Supporting Designer Learning and Performance in Design Space Exploration: A Goal-Setting Approach
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AbstractDesign space exploration (DSE) is an important knowledge discovery process in the early design phase of complex systems. The outcomes of this process generally include the performance of the designs generated and designer learning. The latter broadly refers to the designers knowledge of the mapping between the design space and the objective space. Despite the integration of visual and data analytics in DSE, there is a lack of emphasis on a human designers learning as a basis for increasing the effectiveness of DSE. To address this gap, we investigate the use of goal-setting as a motivating factor to improve DSE outcomes. Previous research suggests that the goal of designing (i.e., finding good designs) and the goal of learning (i.e., learning useful knowledge) are inextricably interlinked. We test the hypothesis that giving designers an explicit goal of learning vs an explicit goal of designing generates different learning and performance outcomes, despite the two goals being interlinked. To this hypothesis, we conduct a between-subject experiment in which participants (N = 14) use a DSE tool to explore mechanical metamaterial designs. Subjects in the first conditions are incentivized to maximize the number of correct answers in learning tests administered after using the tool. Subjects in the second condition are incentivized to maximize the performance of designs they generate. The results show that the subjects with the goal of learning perform better on the learning tests, with a large but mildly significant effect. Whereas, the subjects with the designing goal generate better design performance, with a small but significant effect. This study suggests that there may exist a trade-off between the designing and learning goals, despite their interconnections, and designers can target one at the expense of the other through goal-setting.
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Volume 6: 33rd International Conference on Design Theory and Methodology (DTM)
