Fauxvea: Crowdsourcing Gaze Location Estimates for Visualization Analysis Tasks.

We present the design and evaluation of a method for estimating gaze locations during the analysis of static visualizations using crowdsourcing. Understanding gaze patterns is helpful for evaluating visualizations and user behaviors, but traditional eye-tracking studies require specialized hardware and local users. To avoid these constraints, we developed a method called Fauxvea, which crowdsources visualization tasks on the Web and estimates gaze fixations through cursor interactions without eye-tracking hardware. We ran experiments to evaluate how gaze estimates from our method compare with eye-tracking data. First, we evaluated crowdsourced estimates for three common types of information visualizations and basic visualization tasks using Amazon Mechanical Turk (MTurk). In another, we reproduced findings from a previous eye-tracking study on tree layouts using our method on MTurk. Results from these experiments show that fixation estimates using Fauxvea are qualitatively and quantitatively similar to eye tracking on the same stimulus-task pairs. These findings suggest that crowdsourcing visual analysis tasks with static information visualizations could be a viable alternative to traditional eye-tracking studies for visualization research and design.

A Case Study Using Visualization Interaction Logs and Insight Metrics to Understand How Analysts Arrive at Insights.

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.