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

L. Cibulski, Evanthia Dimara, Setia Hermawati, J. Kohlhammer
{"title":"Supporting Domain Characterization in Visualization Design Studies With the Critical Decision Method","authors":"L. Cibulski, Evanthia Dimara, Setia Hermawati, J. Kohlhammer","doi":"10.1109/VisGuides57787.2022.00007","DOIUrl":null,"url":null,"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.","PeriodicalId":188844,"journal":{"name":"2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE 4th Workshop on Visualization Guidelines in Research, Design, and Education (VisGuides)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/VisGuides57787.2022.00007","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2

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.
关键决策方法在可视化设计研究中的支持域表征
虽然领域表征已经成为可视化设计研究的一个组成部分,但方法论上的规定却很少。在现有方法中代表性不足的方面是领域专门知识。来自认知科学的知识启发方法可能会有所帮助,但尚未得到很多关注。我们提出了关键决策方法(CDM)的可视化领域,以提供描述性的步骤,开辟了一个基于知识的视角的领域表征。清洁发展机制使用回顾性访谈来揭示专家在具有挑战性情况下的判断。我们将其应用于三个领域问题的研究,反思我们的实践经验,并讨论其与可视化研究中的领域表征的相关性。我们发现CDM的现实性和主观性非常适合于引出高层次任务绩效的认知方面。我们的见解可能会指导其他研究人员进行领域表征,重点关注领域知识和认知。通过我们的工作,我们希望为用于可视化设计的有意义的方法组合做出贡献,并激发有关领域表征的规定性步骤的讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信