CHS: Small: Using Learning Objectives for Visualization Design

信息可视化主要有两种类型：用于发现见解的信息可视化和用于传达见解的信息可视化。虽然交流形式更为常见，但大多数研究都集中在洞察力发现类型上。 这对于那些没有一套很好的工具来测试他们的设计目标是否得到满足的交流可视化设计师来说是有问题的。具体地说，很难回答这个问题：可视化的观众从可视化中学到了什么，还是他们只是“读了它就忘了？”没有更好的方法来描述他们的具体目标和评估成功，沟通可视化设计师往往依赖于关于图表类型，编码，叙述技巧和其他设计元素的通用启发式建议。 该项目旨在创建方法和工具，帮助设计师具体定义他们的学习目标，以及测试和工具，以确定是否达到目标。反过来，这将提高数字和图形素养，并增强对从新闻到金融等领域复杂信息的理解。该研究将支持不断增长的工作类别，包括可视化设计师，计算记者和数据分析师，以及专注于公共传播的组织。 项目活动还将为本科生、研究生和专业人员提供研究和实践培训，而项目成果将被纳入可视化特定课程的无障碍教育材料，并作为相关课程的模块，例如，探索性数据分析、计算新闻和医学传播。 为构建交流可视化提供学习目标和测试框架需要深入了解设计师如何以及为什么构建他们的可视化。具体来说，这个项目的目标是（a）开发一种用于描述交际意图的学习目标“语言”（例如， “观察者将能够描述价格-租金比率数据中的不同种类的趋势”），（B）设计正确和有效的测试机制以确保实现这些目标（例如，“根据房价租金比，以下哪个城市显示出‘泡沫’模式？“），以及（c）提供工具-工作流程和软件-以帮助设计者为读者制定学习目标和测试，以及评估其可视化效果。 通过强调构建可视化的学习目标，设计师将更有信心，他们的预期信息是由设计传达的，指标预测将更成功地传达该信息。即使没有一个设计在所有目标上都是最优的，权衡也会更加突出，更容易理解，让设计师做出更好的决策。虽然这项工作的重点是更广泛的公众消费的静态交流可视化，如与新闻故事相关的数据，但研究可以扩展到其他应用，包括交互式可视化，视觉分析系统，数字和纸质教科书中的解释和教育图形，和专家-该奖项反映了NSF的法定使命，并被认为是值得支持的，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。

There are two main types of information visualizations: those used to find insights, and those used to communicate them. While the communicative form is far more common, most research has focused on the insight-finding type. This is problematic for designers of communicative visualization who do not have a great set of tools to test if their design goals are being met. Specifically, it is difficult to answer the question: did the viewer of the visualization learn anything from the visualization or did they simply "read it and forget it?" Without better ways to describe their specific goals and evaluate success, communicative visualization designers often rely on generic heuristic advice about chart types, encodings, narrative techniques, and other design elements to use. This project seeks to create methods and tools for helping designers concretely define their goals in terms of learning objectives, as well as tests and tools to determine if the objectives are met. This, in turn, should improve numerical and graphical literacy as well as enhanced understanding of complex information in domains from news to finance. The research will support growing job categories including visualization designers, computational journalists, and data analysts, as well as organizations focused on public communication. The project activities will also provide research and practical training for undergraduates, graduates, and professionals, while project results will be integrated into accessible educational materials for both visualization-specific classes and as modules for related courses in, e.g., exploratory data analysis, computational journalism, and medical communication. Providing a learning-objective and testing framework for building communicative visualizations requires a deep understanding of how and why designers build their visualizations. Specifically, the goals of this project are (a) developing a learning-objectives "language" for describing communicative intent (e.g., "the viewer will be able to describe the different kinds of trends in the price-rent ratio data"), (b) designing correct and effective testing mechanisms to ensure that these objectives are achieved (e.g., "Based on the price-rent ratio, which of the following cities is displaying a 'bubble' pattern?"), and (c) providing tools -- both workflows and software -- to help designers create learning objectives and tests for readers, as well as for the evaluation of their visualizations. By emphasizing learning objectives for building visualizations, designers will have more confidence that their intended message is communicated by designs that metrics predict will be more successful in communicating that message. Even when no design is optimal across all objectives, the trade-offs will be more salient and easier to understand, allowing designers to make better decisions. Although the focus of this work is on static communicative visualizations for broader public consumption, such as data associated with news stories, the research can be extended to other applications including interactive visualizations, visual analysis systems, explanatory and educational graphics in digital and paper textbooks, and expert-focused forms such as graphics in scientific documents or corporate reports.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.