CHS: Small: Collaborative Research: Validating and Communiciating Model-Based Approaches for Data Visualization Ability Assessment

CHS：小型：协作研究：验证和交流基于模型的数据可视化能力评估方法

• 批准号: 2120750
• 负责人: Matthew Kay
• 金额: $ 23.88万
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-10-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2120750&HistoricalAwards=false
• 关键词: CHS; Small; Collaborative; Research; Validating

People are encountering graphs, charts, and other visual representations of data now more than ever before. Yet creators of these visualizations currently must reason with sparse and conflicting evidence on how well people can read the visualizations they publish. Current guidelines do not take into account the possibility that different people have different strengths and weaknesses when interpreting visual data. This project will use studies of visualization effectiveness to inform our understanding of the abilities and biases of viewers, both individually and collectively. To do this, the project team will use a combination of experiments, statistical modeling, and interview studies to both challenge long-standing assumptions about visualization effectiveness, and to lay a foundation for future experiments that account for differences in visualization reading ability. The work will also support a broader educational goal of using robust statistical modeling techniques in experimentation, through course modules that can be integrated into existing data visualization courses, and through outreach activities that allow individuals to see how well they perform visualization tasks compared to others who have taken the experiments.This work seeks to answer three primary research questions. The first is to determine the extent to which individuals differ in their ability to perform basic tasks with data visualizations, through large-scale crowdsourced experiments that use transparent statistical methodologies to establish individual differences in data visualization performance. The second question evaluates the relationship between low-level visualization performance and higher-level assessments such as visualization literacy and cognitive abilities, recruiting both expert and novice populations to evaluate the extent to which these hypothesized measures of visualization literacy correlate with each other. The third question determines how alternative ways of presenting visualization experiment results shape the design recommendations researchers and designers draw from them, through a comparative evaluation of longstanding ways of presenting visualization experiment results, and by designing new ways of presenting results that may lead to more mature interpretation of experiment results by broader visualization community. The work will provide new perspectives on visualization literacy by augmenting chart reading experiments with novel measures of visualization ability, and by studying how creators currently make use of existing visualization design guidelines in their design process.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人们现在比以往任何时候都更遇到数据的图形，图表和其他视觉表示。然而，目前，这些可视化的创建者必须通过稀疏而相互矛盾的证据来理解人们如何阅读他们发布的可视化。 当前的指南没有考虑到不同人在解释视觉数据时具有不同优势和劣势的可能性。该项目将使用可视化有效性的研究来了解我们对观众的能力和偏见的理解，无论是单独还是集体的。 为此，项目团队将使用实验，统计建模和访谈研究的组合来挑战有关可视化效果的长期假设，并为未来的实验奠定基础，以说明可视化阅读能力的差异。这项工作还将支持一个更广泛的教育目标，即通过可以将可以集成到现有数据可视化课程中的课程模块进行实验中，并通过宣传活动使个人看到与其他实验的其他人相比执行可视化任务的能力。这些工作人员寻求三个主要研究问题。首先是通过大规模众包实验使用透明的统计方法来确定数据可视化性能的个体差异，从而确定个人通过数据可视化执行基本任务的能力的程度。第二个问题评估了低级可视化绩效与高级评估（例如可视化素养和认知能力）之间的关系，招募了专家和新手人群，以评估这些假设的可视化素养指标相互关联的程度。第三个问题确定了展示可视化实验结果的替代方法如何塑造设计建议研究人员从他们那里汲取的设计建议，并通过对展示可视化实验结果的长期评估以及设计新的方法来提出可能导致更成熟的结果来通过更广泛的可视化社区来对实验结果进行更成熟的解释。这项工作将通过以新颖的可视化能力度量来增强图表阅读实验，并研究创作者目前如何利用现有的可视化设计指南，以通过其设计过程中使用现有的可视化设计指南来提供有关可视化素养的新观点。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和广泛的影响来评估CRETERIA的评估。