CAREER: Enhancing Critical Reflection on Data by Integrating Users' Expectations in Visualization Interaction

职业：通过在可视化交互中整合用户的期望来增强对数据的批判性反思

基本信息

批准号：
1749266
负责人：
Jessica Hullman
金额：
$ 52.35万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-06-01 至 2019-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1749266&HistoricalAwards=false
关键词：
CAREER Enhancing Critical Reflection Data

项目摘要

Interactive graphs, charts, and other visual representations of data are increasingly common in public life. People bring their own expectations and assumptions about the data and situations these visualizations represent, though most visualizations do not take these expectations into account. Comparing these expectations to actual data is a powerful tool for checking those assumptions, developing better understanding of situations, and making better decisions. To support such expectation visualizations, the project team will use a combination of experiments, software development, and design activities to develop toolkits and best practices for developing visualizations that allow viewers to represent, interact with, and see feedback on their own predictions about the data. The work will focus on helping people better understand scientific research and expert analysis around topics such as health decisions that might impact their own lives. The work will also support a broader educational goal of data literacy education, through course modules that can be inserted into introductory informatics and data science courses and a new course on thinking with data, and outreach goals through developing a research and development platform where designers, researchers, and developers can work together to improve expectation visualization techniques. To do this, the project has three main research goals. The first is to develop a suite of empirical findings on the effects of expectation visualization, through a series of experiments on how predicting data, receiving personalized feedback on those predictions, and reflecting on gaps between predictions and data affect people's later memory of the data and future expectations. The second thrust builds on the first, using these empirical results along with design studies and comprehensive reviews of existing tools and literature to build a design space with software examples characterizing key decisions in designing expectation visualizations. These decisions will include a range of techniques for graphically eliciting people's expectations, contextualization techniques that help people learn to use those techniques and constrain their choices appropriately, and feedback or reflection techniques that help call attention to places where expectations did and did not match the underlying data. The third thrust is to put these principles into practice by developing applications to support the communication of uncertainty in experimental results, the reduction of spurious pattern discoveries in data analysis, and the integration of problem context and expert analysis with the visualization itself.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.

数据的交互式图，图表和其他视觉表示形式在公共生活中越来越普遍。人们对这些可视化的数据和情况提出了自己的期望和假设，尽管大多数可视化都没有考虑到这些期望。将这些期望与实际数据进行比较是检查这些假设，更好地理解情况并做出更好决策的强大工具。为了支持这种期望可视化，项目团队将结合实验，软件开发和设计活动的组合来开发工具包和最佳实践，以开发可视化的可视化，从而使观众可以根据自己的数据进行预测，并与他们表示反馈。这项工作将着重于帮助人们更好地了解有关可能影响其生活的健康决策等主题的科学研究和专家分析。这项工作还将通过可以插入入门信息学和数据科学课程中的课程模块来支持数据素养教育的更广泛的教育目标，并通过开发研究和开发平台通过开发研究和开发平台的新课程，并通过开发设计和开发人员来进行外展目标，在该课程中，设计师，研究人员和开发人员可以共同努力，共同努力以提高预期可视化技术。为此，该项目具有三个主要的研究目标。首先是通过一系列实验来开发一系列关于预期可视化影响的经验发现，以了解如何预测数据，对这些预测的个性化反馈以及反思预测和数据之间的差距影响人们以后对数据的记忆和未来期望的差距。第二个推力是在第一个方面建立的，使用这些经验结果以及设计研究以及对现有工具和文献的全面评论，以使用软件示例来构建设计空间，这些示例表征了设计期望可视化的关键决策。这些决定将包括一系列技术，以图形方式引起人们的期望，情境化技术，这些技术有助于人们学习使用这些技术并适当地限制他们的选择，以及适当的反馈或反思技术，这些技术有助于吸引人们对期望确实如此且与潜在数据不匹配的地方。第三个决策是通过开发应用程序来支持实验结果中的不确定性，减少数据分析中的虚假模式发现，以及问题上下文和专家分析与可视化本身的整合。该奖项反映了NSF的法定任务，并认为通过基金会的知识绩效和广泛的影响，该奖项值得通过评估，该奖项反映了NSF的法定任务。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Some Prior(s) Experience Necessary: Templates for Getting Started With Bayesian Analysis

一些必要的先前经验：贝叶斯分析入门模板

DOI：
10.1145/3290605.3300709
发表时间：
2019
期刊：
ACM Conference on Computer Human Interaction
影响因子：
0
作者：
Phelan, Chanda;Hullman, Jessica;Kay, Matthew;Resnick, Paul
通讯作者：
Resnick, Paul

Belief-Driven Data Journalism

信念驱动的数据新闻

DOI：
发表时间：
2019
期刊：
Computation+Journalism
影响因子：
0
作者：
Nguyen, F.
通讯作者：
Nguyen, F.

A Bayesian Cognition Approach to Improve Data Visualization

DOI：
10.1145/3290605.3300912
发表时间：
2019-01-01
期刊：
CHI 2019: PROCEEDINGS OF THE 2019 CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS
影响因子：
0
作者：
Kim, Yea-Seul;AWalls, Logan;Hullman, Jessica
通讯作者：
Hullman, Jessica