arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2407.14908cs.HCcs.GR
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PREVis:可视化感知可读性评估

PREVis: Perceived Readability Evaluation for Visualizations

Anne-Flore Cabouat, Tingying He, Petra Isenberg, Tobias Isenberg

AI总结:

本研究开发了首个用于测量数据可视化感知可读性的验证工具PREVis,包含4个维度11个条目,并提供了实施指南与相关因素分析。

摘要:

我们开发并验证了一个用于测量数据可视化中感知可读性的工具:PREVis。研究人员和从业者可以轻松地将该工具作为其评估的一部分,用于比较不同视觉数据表示的感知可读性。我们的工具可以补充用户任务性能对照实验的结果,或者在开发新技术的过程中,为设计迭代等深入定性工作提供额外数据。尽管可读性被认为是数据可视化的一个重要质量指标,但迄今为止,在视觉表示的背景下,尚未对该构念有统一的定义。因此,研究人员在确定如何要求人们对可视化的感知可读性进行评分时,往往缺乏指导。为了解决这个问题,我们经过严格的过程,开发了首个针对视觉数据表示主观可读性的经过验证的工具。我们最终的工具包含跨4个维度的11个条目:可理解性、布局清晰度、数据值可读性和数据模式可读性。我们在 osf.io/9cg8j 上提供了包含实施指南的问卷文档。除了该工具外,我们还贡献了对研究人员以往如何评估可视化可读性的讨论,以及对视觉数据表示中感知可读性潜在因素的分析。

英文摘要:

We developed and validated an instrument to measure the perceived readability in data visualization: PREVis. Researchers and practitioners can easily use this instrument as part of their evaluations to compare the perceived readability of different visual data representations. Our instrument can complement results from controlled experiments on user task performance or provide additional data during in-depth qualitative work such as design iterations when developing a new technique. Although readability is recognized as an essential quality of data visualizations, so far there has not been a unified definition of the construct in the context of visual representations. As a result, researchers often lack guidance for determining how to ask people to rate their perceived readability of a visualization. To address this issue, we engaged in a rigorous process to develop the first validated instrument targeted at the subjective readability of visual data representations. Our final instrument consists of 11 items across 4 dimensions: understandability, layout clarity, readability of data values, and readability of data patterns. We provide the questionnaire as a document with implementation guidelines on osf.io/9cg8j. Beyond this instrument, we contribute a discussion of how researchers have previously assessed visualization readability, and an analysis of the factors underlying perceived readability in visual data representations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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