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

交互式图形、图表和其他数据的视觉表示在公共生活中越来越普遍。 人们对这些可视化表示的数据和情况有自己的期望和假设，尽管大多数可视化没有考虑到这些期望。将这些预期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。 为了支持这样的期望可视化，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，使观众能够表示，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了解科学研究和专家分析，例如可能影响他们自己生活的健康决策。 这项工作还将通过可插入信息学和数据科学入门课程的课程模块以及关于数据思维的新课程来支持数据素养教育的更广泛的教育目标，并通过开发一个研究和开发平台来实现外联目标，设计师，研究人员和开发人员可以共同努力提高期望可视化技术。 为此，该项目有三个主要研究目标。 第一个是通过一系列实验来研究预测数据、接受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如何影响人们对数据和未来预期的后期记忆，从而开发出一套关于预期可视化效果的实证研究结果。第二个推力建立在第一个，使用这些经验的结果沿着与设计研究和现有的工具和文献的全面审查，以建立一个设计空间与软件的例子，在设计预期可视化的关键决策特征。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限制他们的选择的情境化技术，以及有助于提醒人们注意期望与基本数据匹配或不匹配的地方的反馈或反思技术。 第三个重点是通过开发应用程序来将这些原则付诸实践，以支持实验结果中不确定性的交流，减少数据分析中虚假模式的发现，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响进行评估来支持审查标准。

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.