L'Yi Sehi, van den Brandt Astrid, Adams Etowah, Nguyen Huyen N, Gehlenborg Nils
IEEE Trans Vis Comput Graph. 2025 Jan;31(1):459-469. doi: 10.1109/TVCG.2024.3456598. Epub 2024 Dec 3.
A wide range of visualization authoring interfaces enable the creation of highly customized visualizations. However, prioritizing expressiveness often impedes the learnability of the authoring interface. The diversity of users, such as varying computational skills and prior experiences in user interfaces, makes it even more challenging for a single authoring interface to satisfy the needs of a broad audience. In this paper, we introduce a framework to balance learnability and expressivity in a visualization authoring system. Adopting insights from learnability studies, such as multimodal interaction and visualization literacy, we explore the design space of blending multiple visualization authoring interfaces for supporting authoring tasks in a complementary and flexible manner. To evaluate the effectiveness of blending interfaces, we implemented a proof-of-concept system, Blace, that combines four common visualization authoring interfaces-template-based, shelf configuration, natural language, and code editor-that are tightly linked to one another to help users easily relate unfamiliar interfaces to more familiar ones. Using the system, we conducted a user study with 12 domain experts who regularly visualize genomics data as part of their analysis workflow. Participants with varied visualization and programming backgrounds were able to successfully reproduce unfamiliar visualization examples without a guided tutorial in the study. Feedback from a post-study qualitative questionnaire further suggests that blending interfaces enabled participants to learn the system easily and assisted them in confidently editing unfamiliar visualization grammar in the code editor, enabling expressive customization. Reflecting on our study results and the design of our system, we discuss the different interaction patterns that we identified and design implications for blending visualization authoring interfaces.
各种各样的可视化创作界面能够创建高度定制化的可视化效果。然而，将表达性置于优先地位往往会妨碍创作界面的可学习性。用户的多样性，比如不同的计算技能以及在用户界面方面的先前经验，使得单一的创作界面更难满足广大用户群体的需求。在本文中，我们介绍了一个在可视化创作系统中平衡可学习性和表达性的框架。我们借鉴了可学习性研究中的见解，如多模态交互和可视化素养，探索混合多个可视化创作界面的设计空间，以互补且灵活的方式支持创作任务。为了评估混合界面的有效性，我们实现了一个概念验证系统Blace，它结合了四种常见的可视化创作界面——基于模板的、架子配置、自然语言和代码编辑器——这些界面彼此紧密相连，以帮助用户轻松地将不熟悉的界面与更熟悉的界面联系起来。使用该系统，我们对12位领域专家进行了一项用户研究，他们在分析工作流程中经常可视化基因组数据。具有不同可视化和编程背景的参与者在没有指导性教程的情况下，能够在研究中成功重现不熟悉的可视化示例。研究后的定性问卷调查反馈进一步表明，混合界面使参与者能够轻松学习该系统，并帮助他们自信地在代码编辑器中编辑不熟悉的可视化语法，实现富有表现力的定制。通过反思我们的研究结果和系统设计，我们讨论了所确定的不同交互模式以及混合可视化创作界面的设计影响。
