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多地遇到图形、图表和其他数据的可视化表示。然而，这些可视化的创作者目前必须根据稀疏和相互矛盾的证据来推断人们能在多大程度上阅读他们发布的可视化。目前的指导方针在解释视觉数据时没有考虑到不同的人有不同的优势和劣势的可能性。这个项目将使用可视化效果的研究来告知我们对观众的能力和偏见的理解，无论是个人还是集体。为了做到这一点，项目团队将结合实验、统计建模和访谈研究来挑战长期存在的关于可视化效果的假设，并为未来解释可视化阅读能力差异的实验奠定基础。这项工作还将支持一个更广泛的教育目标，即在实验中使用健壮的统计建模技术，通过将课程模块集成到现有的数据可视化课程中，并通过外展活动，让个人看到他们与其他参加过实验的人相比，他们在可视化任务中的表现如何。这项工作试图回答三个主要的研究问题。首先，通过大规模的众包实验，使用透明的统计方法来确定个人在数据可视化执行基本任务方面的差异程度。第二个问题评估低水平可视化表现与高水平评估（如可视化素养和认知能力）之间的关系，招募专家和新手群体来评估这些假设的可视化素养指标之间的相互关联程度。第三个问题决定了展示可视化实验结果的替代方式如何塑造研究人员和设计师从中得出的设计建议，通过对长期以来展示可视化实验结果的方式进行比较评估，并通过设计新的展示结果的方式，可能导致更广泛的可视化社区对实验结果进行更成熟的解释。这项工作将通过增加图表阅读实验和可视化能力的新测量，以及研究创作者目前如何在设计过程中使用现有的可视化设计指南，为可视化素养提供新的视角。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。