A study of designer approaches to parametric engineering design and a descriptive model of effective strategies

Parameter design involves assigning values to interconnected design parameters that must satisfy requirements and constraints while remaining consistent. This is a cognitively challenging activity in engineering design. Yet little empirical research has examined how designers approach this task. We conducted a think-aloud study with 14 designers solving a photovoltaic system design problem. To analyze their behavior, we used a descriptive model comprising five elements: focusing on a parameter, collecting information, assigning a value, handling violations, and learning. The key finding is that designers do not treat all parameters equally—contrary to assumptions in prior research. Instead, they adapt their approaches based on each parameter’s feedback complexity, using rapid trial-and-error when feedback on changes can be understood directly, but shifting to more deliberate analysis when understanding feedback requires multiple reasoning steps. Observed behavioral patterns were formalized using network-based metrics derived from problem structure and knowledge state. For example, effective parameter focusing correlates with readiness of relevant information and with the potential to reveal new information by solving the parameter. Information sourcing, strategy choice, and violation prioritization also correlate with specific structural properties of the parameter network. These findings may inform the development of computational design support tools that provide context-sensitive guidance based on parameter network structure and enable design educators to teach adaptive problem-solving strategies.