| Author | Cibulski, Lena; Dimara, Evanthia; Hermawati, Setia; Kohlhammer, Jörn |
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| Date | 2022 |
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| Type | Conference Paper |
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| 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. |
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| Conference | Workshop on Visualization Guidelines in Research, Design, and Education 2022 |
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| Isbn | 979-8-3503-9712-3 |
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| Publisher | IEEE Computer Society |
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| Project | Cloudification of Production Engineering for Predictive Digital Manufacturing |
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| Url | https://publica.fraunhofer.de/handle/publica/434541 |
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