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Modeling and evaluating user behavior in exploratory visual analysis

机译：探索性视觉分析中的用户行为建模和评估

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摘要

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This article presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords.
机译：传统上，用于可视化的经验评估方法通常集中于评估视觉分析过程的结果，而不是表征该过程如何进行。只有少数方法可用于系统地研究人们如何使用可视化，这使得研究人员难以捕获和表征用户在可视化分析过程中表现出的认知和互动行为的微妙之处。为了验证和改进可视化设计，研究人员必须能够评估和理解用户在现实情况下如何与可视化系统交互，这一点很重要。本文介绍了一种在探索性视觉分析中建模和评估用户行为的方法。我们使用包括心智状态，交互状态和计算状态之间的转换的马尔可夫链过程对视觉探索进行建模。这些状态以及它们之间的过渡可以从多种来源推论得出，包括言语笔录，视频和音频记录以及日志文件。该模型使评估人员能够表征对探索性视觉分析中的洞察力获取至关重要的认知和计算过程，并重构用户与可视化系统之间交互的动态。我们通过两个示例用户研究来说明此模型，并演示它提供的定性和定量分析工具。

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