Observing Architectural Engineering Graduate Students’ Design Optimization Behaviors Using Eye-Tracking Methods

Parametric optimization techniques allow building designers to pursue multiple performance objectives, which can benefit the overall design. However, the strategies used by architecture and engineering graduate students when working with optimization tools are unclear, and ineffective computational design procedures may limit their success as future designers. In response, this research identifies several designerly behaviors of graduate students when responding to a conceptual building design optimization task. It uses eye-tracking, screen recording, and empirical methods to code their behaviors following the situated function-behaviour-structure (FBS) framework. From these data streams, three different types of design iterations emerge: one by the designer alone, one by the optimizer alone, and one by the designer incorporating feedback from the optimizer. Based on the timing and frequency of these loops, student participants were characterized as completing partial, crude, or complete optimization cycles while developing their designs. This organization of optimization techniques establishes reoccurring strategies employed by developing designers, which can encourage future pedagogical approaches that empower students to incorporate complete optimization cycles while improving their designs. It can also be used in future research studies to establish clear links between types of design optimization behavior and design quality.

Increasingly, building designers use digital, optimization tools to explore and improve designs. This research identifies and categorizes several distinct design behaviors when using optimization tools that have not been previously recognized. Applying these categories to describe graduate student designer behavior allows educators to find opportunities for improving design education. While there is no set standard for how optimization tools should be used, different strategies range in the potential they create for simulation feedback to improve the design. Although all study participants were able to implement an optimization feature, they did not all fully integrate the feedback into their design decisions. From this research we observe that it is not enough to explain algorithms and show a student how to run an optimization tool, but these tools must be taught in the context of robust design approaches. Educators wishing to identify their students’ design strategies can use the methods and language established in this paper to assess student comprehension of optimization techniques. Future work can apply the behaviors that investigate other dimensions of optimization in design, such as design quality and comparing categories of designers.