Browsing by Subject "sensemaking"

Now showing 1 - 5 of 5
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    Concept-Driven Visual Analytics: an Exploratory Study of Model- and Hypothesis-Based Reasoning with Visualizations
    (ACM, 2019-05) Choi, In Kwon; Childers, Taylor; Raveendranath, Nirmal Kumar; Mishra, Swati; Harris, Kyle; Reda, Khairi; Human-Centered Computing, School of Informatics and Computing
    Visualization tools facilitate exploratory data analysis, but fall short at supporting hypothesis-based reasoning. We conducted an exploratory study to investigate how visualizations might support a concept-driven analysis style, where users can optionally share their hypotheses and conceptual models in natural language, and receive customized plots depicting the fit of their models to the data. We report on how participants leveraged these unique affordances for visual analysis. We found that a majority of participants articulated meaningful models and predictions, utilizing them as entry points to sensemaking. We contribute an abstract typology representing the types of models participants held and externalized as data expectations. Our findings suggest ways for rearchitecting visual analytics tools to better support hypothesis- and model-based reasoning, in addition to their traditional role in exploratory analysis. We discuss the design implications and reflect on the potential benefits and challenges involved.
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    Data Prophecy: Exploring the Effects of Belief Elicitation in Visual Analytics
    (2021) Koonchanok, Ratanond; Baser, Parul; Sikharam, Abhinav; Raveendranath, Nirmal Kumar; Reda, Khairi; Human-Centered Computing, School of Informatics and Computing
    Interactive visualizations are widely used in exploratory data analysis, but existing systems provide limited support for confirmatory analysis. We introduce PredictMe, a tool for belief-driven visual analysis, enabling users to draw and test their beliefs against data, as an alternative to data-driven exploration. PredictMe combines belief elicitation with traditional visualization interactions to support mixed analysis styles. In a comparative study, we investigated how these affordances impact participants' cognition. Results show that PredictMe prompts participants to incorporate their working knowledge more frequently in queries. Participants were more likely to attend to discrepancies between their mental models and the data. However, those same participants were also less likely to engage in interactions associated with exploration, and ultimately inspected fewer visualizations and made fewer discoveries. The results suggest that belief elicitation may moderate exploratory behaviors, instead nudging users to be more deliberate in their analysis. We discuss the implications for visualization design.
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    "This Girl is on Fire": Sensemaking in an Online Health Community for Vulvodynia
    (ACM, 2019-05) Young, Alyson L.; Miller, Andrew D.; Human-Centered Computing, School of Informatics and Computing
    Online health communities (OHCs) allow people living with a shared diagnosis or medical condition to connect with peers for social support and advice. OHCs have been well studied in conditions like diabetes and cancer, but less is known about their role in enigmatic diseases with unknown or complex causal mechanisms. In this paper, we study one such condition: Vulvodynia, a chronic pain syndrome of the vulvar region. Through observations of and interviews with members of a vulvodynia Facebook group, we found that while the interaction types are broadly similar to those found in other OHCs, the women spent more time seeking basic information and building individualized management plans. They also encounter significant emotional and interpersonal challenges, which they discuss with each other. We use this study to extend the field's understanding of OHCs, and to propose implications for the design of self-tracking tools to support sensemaking in enigmatic conditions.
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    Understanding how primary care clinicians make sense of chronic pain
    (Springer, 2018-11) Militello, Laura G.; Anders, Shilo; Downs, Sarah M.; Diiulio, Julie; Danielson, Elizabeth C.; Hurley, Robert W.; Harle, Christopher A.; Health Policy and Management, School of Public Health
    Chronic pain leads to reduced quality of life for patients, and strains health systems worldwide. In the US and some other countries, the complexities of caring for chronic pain are exacerbated by individual and public health risks associated with commonly used opioid analgesics. To help understand and improve pain care, this article uses the data frame theory of sensemaking to explore how primary care clinicians in the US manage their patients with chronic noncancer pain. We conducted Critical Decision Method interviews with ten primary care clinicians about 30 individual patients with chronic pain. In these interviews, we identified several patients, social/environmental, and clinician factors that influence the frames clinicians use to assess their patients and determine a pain management plan. Findings suggest significant ambiguity and uncertainty in clinical pain management decision making. Therefore, interventions to improve pain care might focus on supporting sensemaking in the context of clinical evidence rather than attempting to provide clinicians with decontextualized and/or algorithm-based decision rules. Interventions might focus on delivering convenient and easily interpreted patient and social/environmental information in the context of clinical practice guidelines.
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    Visual (dis)Confirmation: Validating Models and Hypotheses with Visualizations
    (IEEE, 2019-07) Choi, In Kwon; Raveendranath, Nirmal Kumar; Westerfield, Jared; Reda, Khairi; Human-Centered Computing, School of Informatics and Computing
    Data visualization provides a powerful way for analysts to explore and make data-driven discoveries. However, current visual analytic tools provide only limited support for hypothesis-driven inquiry, as their built-in interactions and workflows are primarily intended for exploratory analysis. Visualization tools notably lack capabilities that would allow users to visually and incrementally test the fit of their conceptual models and provisional hypotheses against the data. This imbalance could bias users to overly rely on exploratory analysis as the principal mode of inquiry, which can be detrimental to discovery. In this paper, we introduce Visual (dis) Confirmation, a tool for conducting confirmatory, hypothesis-driven analyses with visualizations. Users interact by framing hypotheses and data expectations in natural language. The system then selects conceptually relevant data features and automatically generates visualizations to validate the underlying expectations. Distinctively, the resulting visualizations also highlight places where one's mental model disagrees with the data, so as to stimulate reflection. The proposed tool represents a new class of interactive data systems capable of supporting confirmatory visual analysis, and responding more intelligently by spotlighting gaps between one's knowledge and the data. We describe the algorithmic techniques behind this workflow. We also demonstrate the utility of the tool through a case study.