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Title: Finding Waldo: Learning about Users from their Interactions

Visual analytics is inherently a collaboration between human and computer. However, in current visual analytics systems, the computer has limited means of knowing about its users and their analysis processes. While existing research has shown that a user’s interactions with a system reflect a large amount of the user’s reasoning process, there has been limited advancement in developing automated, real-time techniques that mine interactions to learn about the user. In this paper, we demonstrate that we can accurately predict a user’s task performance and infer some user personality traits by using machine learning techniques to analyze interaction data. Specifically, we conduct an experiment in which participants perform a visual search task and we apply well-known machine learning algorithms to three encodings of the users interaction data. We achieve, depending on algorithm and encoding, between 62% and 96% accuracy at predicting whether each user will be fast or slow at completing the task. Beyond predicting performance, we demonstrate that using the same techniques, we can infer aspects of the user’s personality factors, including locus of control, extraversion, and neuroticism. Further analyses show that strong results can be attained with limited observation time, in some cases, 82% of the final accuracy ismore » gained after a quarter of the average task completion time. Overall, our findings show that interactions can provide information to the computer about its human collaborator, and establish a foundation for realizing mixed- initiative visual analytics systems.« less
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Publication Date:
OSTI Identifier:
Report Number(s):
Journal ID: ISSN 1077-2626
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: IEEE Transactions on Visualization and Computer Graphics; Journal Volume: 20; Journal Issue: 12
Research Org:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Org:
Country of Publication:
United States
97 MATHEMATICS AND COMPUTING user interaction; analytic provenance; visualization