Understanding User Experience of COVID-19 Maps through Remote Elicitation Interviews

September 03, 2020 Β· Declared Dead Β· πŸ› Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization

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Authors Damla Γ‡ay, Till Nagel, AsΔ±m Evren YantaΓ§ arXiv ID 2009.01465 Category cs.HC: Human-Computer Interaction Citations 21 Venue Workshop on Beyond Time and Errors: Novel Evaluation Methods for Visualization Last Checked 4 months ago
Abstract
During the coronavirus pandemic, visualizations gained a new level of popularity and meaning for a wider audience. People were bombarded with a wide set of public health visualizations ranging from simple graphs to complex interactive dashboards. In a pandemic setting, where large amounts of the world population are socially distancing themselves, it becomes an urgent need to refine existing user experience evaluation methods for remote settings to understand how people make sense out of COVID-19 related visualizations. When evaluating visualizations aimed towards the general public with vastly different socio-demographic backgrounds and varying levels of technical savviness and data literacy, it is important to understand user feedback beyond aspects such as speed, task accuracy, or usability problems. As a part of this wider evaluation perspective, micro-phenomenology has been used to evaluate static and narrative visualizations to reveal the lived experience in a detailed way. Building upon these studies, we conducted a user study to understand how to employ Elicitation (aka Micro-phenomenological) interviews in remote settings. In a case study, we investigated what experiences the participants had with map-based interactive visualizations. Our findings reveal positive and negative aspects of conducting Elicitation interviews remotely. Our results can inform the process of planning and executing remote Elicitation interviews to evaluate interactive visualizations. In addition, we share recommendations regarding visualization techniques and interaction design about public health data.
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