A Preliminary Study of Novice and Expert Users’ Decision-Making Procedures during Visual Trade Space Exploration

Thanks to advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design and decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support multidimensional data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expertdriven exploration that is being enabled by computational thinking. In this paper, we discuss ongoing research that seeks to transform decades-old decision-making paradigms rooted in operations research by considering how to effectively convert data into knowledge that ultimately leads to better decisions – and better designs. Specifically, we address decisionmaking within the area of trade space exploration by conducting human-computer interaction studies using multi-dimensional data visualization software that we have been developing. We first discuss a pilot study that was conducted to gain insight into expected differences between novice and expert decision-makers using a small test group. We then present the results of two preliminary studies designed to gain insight into procedural differences in how novices and experts use multi-dimensional data visualization and exploration tools and to measure their ability to use these tools effectively when solving an engineering design problem. This work supports our goal of developing training protocols for designers that support efficient and effective trade space exploration.