Think-Aloud Computing: Supporting Rich and Low-Effort Knowledge Capture

When users complete computing tasks, knowledge they leverage and their intent is most often lost because it is tedious or challenging to capture. This makes it harder to understand why a colleague designed a component a certain way or remember requirements for software you wrote a year ago. We introduce think-aloud computing, a novel application of think-aloud where users are encouraged to speak while they work to capture rich knowledge with relatively low effort. Through a formative study encouraging think-aloud, we find people shared information about design intent, work processes, problems encountered, to-do items, and other useful information. We developed a prototype that supports think-aloud computing by prompting users to speak and contextualizing speech with labels and context. Our evaluation shows subtler design decisions and process explanations were captured in think-aloud than via traditional documentation. Participants who created slides or 3D models reported think-aloud required similar effort as traditional documentation.

CHI 2021

(conditionally accepted)