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What questions reveal about novices’ attempts to make sense of data visualizations: patterns and misconceptions
Computers & Graphics ( IF 2.5 ) Pub Date : 2021-02-01 , DOI: 10.1016/j.cag.2020.09.015
Ariane Moraes Bueno Rodrigues , Gabriel Diniz Junqueira Barbosa , Hélio Côrtes Vieira Lopes , Simone Diniz Junqueira Barbosa

Abstract Data visualization literacy has attracted widespread interest due to the urgent need to analyze unprecedented volumes of data we have nowadays. Much work on visualization literacy focuses on asking people to answer specific questions about the data depicted in a visual representation, in an attempt to try to understand how people make sense of the underlying data. In this work, we investigate, through a user survey, the initial questions people pose when first encountering a visualization. We analyzed a set of 1058 questions that 22 participants created about 20 different visualizations, deriving templates for the recurring types of questions that emerged as information-seeking patterns, and classifying the various kinds of errors they introduced in the questions. By understanding the common mistakes they made when asking data-related questions, we now feel better equipped to inform further research on producing and consuming data visualization concepts. The results of the study reported in this paper can be used in teaching data visualization, as they uncover and classify frequent errors people make when trying to make sense of data represented visually. The study may also contribute to the design of visualization recommender systems, as the question patterns revealed what people expect to answer with each visualization.

中文翻译:

什么问题揭示了新手试图理解数据可视化的意义:模式和误解

摘要 由于迫切需要分析我们当今拥有的前所未有的数据量,数据可视化素养引起了广泛的兴趣。许多关于可视化素养的工作都集中在要求人们回答有关以可视化表示形式描述的数据的特定问题,试图了解人们如何理解底层数据。在这项工作中,我们通过用户调查调查了人们第​​一次遇到可视化时提出的初始问题。我们分析了一组 1058 个问题,22 名参与者创建了大约 20 种不同的可视化,为作为信息搜索模式出现的重复问题类型派生模板,并对他们在问题中引入的各种错误进行分类。通过了解他们在询问与数据相关的问题时所犯的常见错误,我们现在感觉更有能力为有关生产和消费数据可视化概念的进一步研究提供信息。本文报告的研究结果可用于教学数据可视化,因为它们发现和分类人们在试图理解以视觉方式表示的数据时经常犯的错误。该研究还可能有助于可视化推荐系统的设计,因为问题模式揭示了人们希望通过每个可视化来回答什么。当他们发现和分类人们在试图理解以视觉方式表示的数据时经常犯的错误。该研究还可能有助于可视化推荐系统的设计,因为问题模式揭示了人们希望通过每个可视化来回答什么。当他们发现和分类人们在试图理解以视觉方式表示的数据时经常犯的错误。该研究还可能有助于可视化推荐系统的设计,因为问题模式揭示了人们希望通过每个可视化来回答什么。
更新日期:2021-02-01
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