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Decipher: An Interactive Visualization Tool for Interpreting Unstructured Design Feedback from Multiple Providers
Yu-Chun Grace Yen, Joy O. Kim, Brian P. Bailey

CHI '20: ACM CHI Conference on Human Factors in Computing Systems
Session: Coordination support

Abstract
Feedback from diverse audiences can vary in focus, differ in structure, and contradict each other, making it hard to interpret and act on. While prior work has explored generating quality feedback, our work helps a designer interpret that feedback. Through a formative study with professional designers (N=10), we discovered that the interpretation process includes categorizing feedback, identifying valuable feedback, and prioritizing which feedback to incorporate in a revision. We also found that designers leverage feedback topic and sentiment, and the status of the provider to aid interpretation. Based on the findings, we created a new tool (Decipher) that enables designers to visualize and navigate a collection of feedback using its topic and sentiment structure. In a preliminary evaluation (N=20), we found that Decipher helped users feel less overwhelmed during feedback interpretation tasks and better attend to critical issues and conflicting opinions compared to using a typical document-editing tool.

DOI:: https://doi.org/10.1145/3313831.3376380
WEB:: https://chi2020.acm.org/

Remote Presentations for the ACM CHI Conference on Human Factors in Computing Systems 2020.