Explaining with Examples Lessons Learned from Crowdsourced Introductory Description of Information Visualizations
Explaining with Examples Lessons Learned from Crowdsourced Introductory Description of Information Visualizations
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DOI：
10.1109/tvcg.2021.3128157
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发表时间：
2021-11
影响因子：
5.2
通讯作者：
Leni Yang;Cindy Xiong;Jason K. Wong;Aoyu Wu;Huamin Qu
中科院分区：
文献类型：
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作者：
Leni Yang;Cindy Xiong;Jason K. Wong;Aoyu Wu;Huamin Qu
数据可视化越来越多地用于口头演示，以向公众传达数据模式。清晰的可视化语言介绍，解释如何解释视觉编码的信息，对于传达要点和避免误解至关重要。我们贡献了一系列的研究，探讨如何有效地介绍可视化的观众不同程度的可视化素养。我们开始了解人们是如何引入可视化的。我们众包了110个可视化的介绍，并根据它们的内容和结构对其进行分类。从这些众包介绍，我们确定不同的介绍策略，并生成一组介绍评估。我们进行了实验，系统地比较了1，080名参与者在四种可视化中不同介绍策略的有效性。我们发现，介绍解释视觉编码与具体的例子是最有效的。我们的研究为如何在演示文稿中构建可视化的有效口头介绍提供了定性和定量的见解，启发了数据故事的进一步研究。
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.