Dissecting Visual Analytics: Comparing Frameworks for Interpreting and Modelling Observed Visual Analytics Behavior

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dc.contributor.authorBrown, Vanessaen_US
dc.contributor.authorTurkay, Cagatayen_US
dc.contributor.authorJianu, Raduen_US
dc.contributor.editorKerren, Andreas and Garth, Christoph and Marai, G. Elisabetaen_US
dc.date.accessioned2020-05-24T13:51:58Z
dc.date.available2020-05-24T13:51:58Z
dc.date.issued2020
dc.description.abstractThis paper provides an empirical, comparative exploration of the role of analytic frameworks in interpreting and modelling visual analytics behavior through data gathered in observational studies. The crucial research on understanding the complex and multi-faceted interplay between visual analytics tools and their users is often done through controlled or naturalistic observations of analysts engaging in the visual analytic process, followed by the interpretation of the observation data. The researchers in Human Computer Interaction and Cognitive Sciences have long used structured analytic frameworks for such analyses, where a guiding set of principles and questions direct attention to relevant aspects of the studied behavior, eventually leading to more complete and consistent analyses. Such frameworks are rarely applied in the visualization domain however, and information about how to apply them and their benefits is scarce. With this paper, we contribute a comparative account, grounded in empirical data collected in a user study with 10 participants using Tableau to analyze domain-specific data, of the types of insights we can glean from interpreting observational data using three different frameworks: Joint Action Theory, Distributed Cognition, and Situated Cognition.en_US
dc.description.sectionheadersAnalytics and Evaluation
dc.description.seriesinformationEuroVis 2020 - Short Papers
dc.identifier.doi10.2312/evs.20201045
dc.identifier.isbn978-3-03868-106-9
dc.identifier.pages37-41
dc.identifier.urihttps://doi.org/10.2312/evs.20201045
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/evs20201045
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectHuman centered computing
dc.subjectEmpirical studies in visualization
dc.subjectVisualization theory
dc.subjectconcepts and paradigms
dc.titleDissecting Visual Analytics: Comparing Frameworks for Interpreting and Modelling Observed Visual Analytics Behavioren_US
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