Abstract
We present an analysis of user gaze data to understand if and how user characteristics impact visual processing of bar charts in the presence of different highlighting interventions designed to facilitate visualization usage. We then link these results to task performance in order to provide insights on how to design user-adaptive information visualization systems. Our results show how the least effective intervention manifests itself as a distractor based on gaze patterns. The results also identify specific visualization regions that cause poor task performance in users with low values of certain cognitive measures, and should therefore be the target of personalized visualization support.
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© 2014 Springer International Publishing Switzerland
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Toker, D., Conati, C. (2014). Eye Tracking to Understand User Differences in Visualization Processing with Highlighting Interventions. In: Dimitrova, V., Kuflik, T., Chin, D., Ricci, F., Dolog, P., Houben, GJ. (eds) User Modeling, Adaptation, and Personalization. UMAP 2014. Lecture Notes in Computer Science, vol 8538. Springer, Cham. https://doi.org/10.1007/978-3-319-08786-3_19
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DOI: https://doi.org/10.1007/978-3-319-08786-3_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08785-6
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