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

交互式图形、图表和数据的其他可视化表示在公共生活中越来越普遍。人们对这些可视化所代表的数据和情况有自己的期望和假设，尽管大多数可视化并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验这些假设、更好地理解情况和做出更好决策的有力工具。为了支持这种期望的可视化，项目团队将使用实验、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，允许查看者表示、交互并查看他们自己对数据的预测的反馈。这项工作将侧重于帮助人们更好地理解围绕可能影响自己生活的健康决定等主题的科学研究和专家分析。这项工作还将支持数据素养教育的更广泛的教育目标，通过可插入介绍性信息学和数据科学课程的课程模块和关于数据思考的新课程，以及通过开发一个研究和开发平台来扩展目标，设计人员、研究人员和开发人员可以共同努力改进期望可视化技术。为此，该项目有三个主要研究目标。首先是通过一系列实验，研究预测数据、接受个性化的预测反馈、以及预测和数据之间的差距如何影响人们对数据和未来预期的记忆，从而形成一套关于预期可视化效果的实证研究结果。第二个推动力建立在第一个基础上，使用这些经验结果以及设计研究和对现有工具和文献的全面回顾来构建一个设计空间，其中包含设计期望可视化中的关键决策的软件示例。这些决策将包括一系列以图形方式引出人们期望的技术，帮助人们学习使用这些技术并适当约束他们选择的情境化技术，以及帮助人们注意期望与基础数据匹配或不匹配的地方的反馈或反射技术。第三个重点是将这些原则付诸实践，通过开发应用程序来支持实验结果中的不确定性的交流，减少数据分析中的虚假模式发现，以及将问题上下文和专家分析与可视化本身集成在一起。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。

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.