Predicting Confusion in Information Visualization from Eye Tracking and Interaction Data
Predicting Confusion in Information Visualization from Eye Tracking and Interaction Data
复制标题
从眼动追踪和交互数据预测信息可视化中的混乱
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
G. Carenini
中科院分区：
文献类型：
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作者：
Sébastien Lallé;C. Conati;G. Carenini
已经发现混淆阻碍了可视化的用户体验。如果可以预测并在真实的时间内解决混淆，用户体验和满意度将大大提高。在本文中，我们专注于预测混乱的发生在交互过程中与可视化使用眼动跟踪和鼠标数据。这些数据是在使用ValueChart进行用户研究期间收集的，ValueChart是一种支持偏好选择的交互式可视化。我们报告非常有前途的结果的基础上随机森林分类器。
Confusion has been found to hinder user experience with visualizations. If confusion could be predicted and resolved in real time, user experience and satisfaction would greatly improve. In this paper, we focus on predicting occurrences of confusion during the interaction with a visualization using eye tracking and mouse data. The data was collected during a user study with ValueChart, an interactive visualization to support preferential choices. We report very promising results based on Random Forest classifiers.