IEEE Trans Vis Comput Graph. 2021 Feb;27(2):946-956. doi: 10.1109/TVCG.2020.3030375. Epub 2021 Jan 28.
Significant research has provided robust task and evaluation languages for the analysis of exploratory visualizations. Unfortunately, these taxonomies fail when applied to communicative visualizations. Instead, designers often resort to evaluating communicative visualizations from the cognitive efficiency perspective: "can the recipient accurately decode my message/insight?" However, designers are unlikely to be satisfied if the message went 'in one ear and out the other.' The consequence of this inconsistency is that it is difficult to design or select between competing options in a principled way. The problem we address is the fundamental mismatch between how designers want to describe their intent, and the language they have. We argue that visualization designers can address this limitation through a learning lens: that the recipient is a student and the designer a teacher. By using learning objectives, designers can better define, assess, and compare communicative visualizations. We illustrate how the learning-based approach provides a framework for understanding a wide array of communicative goals. To understand how the framework can be applied (and its limitations), we surveyed and interviewed members of the Data Visualization Society using their own visualizations as a probe. Through this study we identified the broad range of objectives in communicative visualizations and the prevalence of certain objective types.
大量研究为探索性可视化分析提供了强大的任务和评估语言。不幸的是，当这些分类法应用于交际性可视化时就失效了。相反，设计师通常倾向于从认知效率的角度评估交际性可视化：“接收者能否准确地解码我的信息/见解？”然而，如果信息“左耳进右耳出”，设计师不太可能满意。这种不一致的后果是，很难以一种有原则的方式在竞争选项之间进行设计或选择。我们要解决的问题是设计师想要描述其意图的方式与他们所拥有的语言之间存在根本不匹配。我们认为，可视化设计师可以通过学习视角来解决这个限制：接收者是学生，而设计师是教师。通过使用学习目标，设计师可以更好地定义、评估和比较交际性可视化。我们说明了基于学习的方法如何为理解广泛的交际目标提供框架。为了了解框架如何应用（及其局限性），我们使用他们自己的可视化作品作为探针，对数据可视化协会的成员进行了调查和访谈。通过这项研究，我们确定了交际性可视化中的广泛目标以及某些目标类型的普遍性。
