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    This study introduces a visual analytics approach for integrating user evaluation data, such as eye tracking and thinking aloud, to better understand visualization tool usage and identify common patterns.

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    Area of Science:

    • Visualization research
    • Human-computer interaction
    • Visual analytics

    Background:

    • User evaluation in visualization research commonly uses methods from human-computer interaction, including task performance, thinking aloud, and interaction logs.
    • Eye tracking has emerged as a popular method for analyzing user visual strategies, adding another data modality.
    • Existing approaches often lack integrated analysis of multiple concurrent evaluation procedures, necessitating new methods for complex, multi-source data streams.

    Purpose of the Study:

    • To develop a visual analytics approach for the integrated analysis of multiple, concurrent user evaluation data streams.
    • To provide an interactive environment for displaying and analyzing thinking aloud, interaction, and eye movement data in conjunction.
    • To support researchers in identifying and verifying common eye-interaction-thinking patterns among participants.

    Main Methods:

    • Development of a highly interactive visualization environment.
    • Integration of automatic pattern-finding algorithms for exploratory search.
    • Implementation of mechanisms for searching and verifying expected usage patterns.
    • Application and evaluation of the approach using a user study with a visual analytics application.

    Main Results:

    • Demonstration of an integrated analysis of thinking aloud, interaction, and eye movement data.
    • Facilitation of exploratory search for common user behavior patterns.
    • Support for researchers in verifying hypotheses about user interaction.
    • Gained insights from a user study applying the developed approach.

    Conclusions:

    • The proposed visual analytics approach enables the integrated analysis of complex, multi-source user evaluation data.
    • The approach aids in deriving and verifying common eye-interaction-thinking patterns, enhancing understanding of user behavior.
    • The methodology is adaptable to other combinations of evaluation techniques and various visualization applications.