IEEE Trans Vis Comput Graph. 2016 Jan;22(1):51-60. doi: 10.1109/TVCG.2015.2467613.
We present results from an experiment aimed at using logs of interactions with a visual analytics application to better understand how interactions lead to insight generation. We performed an insight-based user study of a visual analytics application and ran post hoc quantitative analyses of participants' measured insight metrics and interaction logs. The quantitative analyses identified features of interaction that were correlated with insight characteristics, and we confirmed these findings using a qualitative analysis of video captured during the user study. Results of the experiment include design guidelines for the visual analytics application aimed at supporting insight generation. Furthermore, we demonstrated an analysis method using interaction logs that identified which interaction patterns led to insights, going beyond insight-based evaluations that only quantify insight characteristics. We also discuss choices and pitfalls encountered when applying this analysis method, such as the benefits and costs of applying an abstraction framework to application-specific actions before further analysis. Our method can be applied to evaluations of other visualization tools to inform the design of insight-promoting interactions and to better understand analyst behaviors.
我们展示了一项实验的结果，该实验旨在利用与视觉分析应用程序的交互日志来更好地了解交互如何导致洞察力的产生。我们对一个视觉分析应用程序进行了基于洞察力的用户研究，并对参与者的测量洞察力指标和交互日志进行了事后的定量分析。定量分析确定了与洞察力特征相关的交互特征，我们通过对用户研究期间捕获的视频进行定性分析，验证了这些发现。实验结果包括针对视觉分析应用程序的设计指南，旨在支持洞察力的产生。此外，我们还展示了一种使用交互日志的分析方法，该方法可以确定哪些交互模式可以产生洞察力，而不仅仅是基于洞察力的评估，这些评估仅量化洞察力特征。我们还讨论了在应用这种分析方法时遇到的选择和陷阱，例如在进一步分析之前，将抽象框架应用于特定于应用程序的操作的好处和成本。我们的方法可以应用于对其他可视化工具的评估，以告知促进洞察力的交互的设计，并更好地了解分析师的行为。
