Explaining with Examples: Lessons Learned from Crowdsourced Introductory Description of Information Visualizations
Autor: | Huamin Qu, Aoyu Wu, Leni Yang, Cindy Xiong, Jason Kwok Wing Wong |
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Rok vydání: | 2021 |
Předmět: |
FOS: Computer and information sciences
Computer science business.industry media_common.quotation_subject Computer Science - Human-Computer Interaction Computer Graphics and Computer-Aided Design Data science Literacy Visualization Human-Computer Interaction (cs.HC) Crowdsource Data visualization Categorization Signal Processing Computer Vision and Pattern Recognition business Set (psychology) Construct (philosophy) Software media_common Storytelling |
DOI: | 10.48550/arxiv.2112.12364 |
Popis: | Data visualizations have been increasingly used in oral presentations to communicate data patterns to the general public. Clear verbal introductions of visualizations to explain how to interpret the visually encoded information are essential to convey the takeaways and avoid misunderstandings. We contribute a series of studies to investigate how to effectively introduce visualizations to the audience with varying degrees of visualization literacy. We begin with understanding how people are introducing visualizations. We crowdsource 110 introductions of visualizations and categorize them based on their content and structures. From these crowdsourced introductions, we identify different introduction strategies and generate a set of introductions for evaluation. We conduct experiments to systematically compare the effectiveness of different introduction strategies across four visualizations with 1,080 participants. We find that introductions explaining visual encodings with concrete examples are the most effective. Our study provides both qualitative and quantitative insights into how to construct effective verbal introductions of visualizations in presentations, inspiring further research in data storytelling. Comment: 12 pages, 5 figures, accepted to IEEE Transaction on Visualization and Graphics |
Databáze: | OpenAIRE |
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