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Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method

Cibulski, Lena; Dimara, Evanthia; Hermawati, Setia; Kohlhammer, Jorn

Authors

Lena Cibulski

Evanthia Dimara

Jorn Kohlhammer



Abstract

While domain characterization has become an integral part of visualization design studies, methodological prescriptions are rare. An underrepresented aspect in existing approaches is domain expertise. Knowledge elicitation methods from cognitive science might help but have not yet received much attention for domain characterization. We propose the Critical Decision Method (CDM) to the visualization domain to provide descriptive steps that open up a knowledge-based perspective on domain characterization. The CDM uses retrospective interviews to reveal expert judgment involved in a challenging situation. We apply it to study three domain problems, reflect on our practical experience, and discuss its relevance to domain characterization in visualization research. We found the CDM's realism and subjective nature to be well suited for eliciting cognitive aspects of high-level task performance. Our insights might guide other researchers in conducting domain characterization with a focus on domain knowledge and cognition. With our work, we hope to contribute to the portfolio of meaningful methods used to inform visualization design and to stimulate discussions regarding prescriptive steps for domain characterization.

Citation

Cibulski, L., Dimara, E., Hermawati, S., & Kohlhammer, J. (2022). Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method. . https://doi.org/10.1109/VisGuides57787.2022.00007

Conference Name Proceedings - 2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education, VisGuides 2022
Conference Location Oklahoma
Start Date Oct 17, 2022
Acceptance Date Aug 26, 2022
Online Publication Date Oct 17, 2022
Publication Date Oct 17, 2022
Deposit Date Nov 21, 2022
Publicly Available Date Nov 22, 2022
Publisher Institute of Electrical and Electronics Engineers
Pages 8-15
ISBN 9798350397130
DOI https://doi.org/10.1109/VisGuides57787.2022.00007
Public URL https://nottingham-repository.worktribe.com/output/14029410
Related Public URLs https://ieeevis.org/year/2022/welcome

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