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，这些可视化的创建者目前必须用稀疏且相互矛盾的证据来推理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的人在解释视觉数据时有不同的优势和劣势的可能性。该项目将利用可视化效果的研究来帮助我们了解观众个人和集体的能力和偏见。 为此，项目团队将结合实验、统计建模和访谈研究来挑战长期以来关于可视化有效性的假设，并为未来解释可视化阅读能力差异的实验奠定基础。这项工作还将支持更广泛的教育目标，即在实验中使用强大的统计建模技术，通过可以集成到现有数据可视化课程中的课程模块，并通过外展活动让个人了解与其他人相比，他们执行可视化任务的情况如何。进行了实验。这项工作旨在回答三个主要研究问题。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，从而确定个体在执行数据可视化基本任务的能力方面的差异程度。第二个问题评估低水平的可视化表现和更高水平的评估（例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测量相互关联的程度。第三个问题确定了呈现可视化实验结果的替代方式如何通过对长期以来呈现可视化实验结果的方式进行比较评估，并通过设计可能导致更成熟的结果呈现方式来形成研究人员和设计师从中得出的设计建议。更广泛的可视化社区对实验结果的解释。这项工作将通过用新颖的可视化能力衡量标准来增强图表阅读实验，并研究创作者目前如何在设计过程中利用现有的可视化设计指南，从而为可视化素养提供新的视角。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来获得支持。