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Lotse: A Practical Framework for Guidance in Visual Analytics.

Fabian Sperrle, Davide Ceneda, Mennatallah El-Assady

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    Summary
    This summary is machine-generated.

    This study introduces Lotse, a new guidance library, to make human-machine collaboration in visual analytics more efficient. It translates theory into practice, enabling researchers to quickly implement guidance strategies.

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    Area of Science:

    • Human-Computer Interaction
    • Information Visualization
    • Software Engineering

    Background:

    • Co-adaptive guidance is crucial for effective human-machine collaboration in visual analytics.
    • Existing theoretical frameworks lack practical implementation models.
    • Bridging the gap between theory and practice in guidance systems is needed.

    Purpose of the Study:

    • To introduce an accessible model for system-provided guidance.
    • To develop and present the Lotse guidance library, mapping theory into practice.
    • To facilitate the creation of reusable and generalizable guidance strategies.

    Main Methods:

    • Developed a model of system-provided guidance based on design templates and derived strategies.
    • Instantiated the model in the Lotse library, enabling guidance specification via definition files.
    • Generated running code from these specifications.
    • Conducted first-use case studies with visual analytics researchers.

    Main Results:

    • Lotse is the first guidance library to specify strategies in definition files and generate code.
    • Researchers with varying expertise could effectively and quickly implement guidance using Lotse.
    • The framework's cognitive dimensions were analyzed for expressiveness.

    Conclusions:

    • Lotse provides a practical and accessible solution for implementing co-adaptive guidance in visual analytics.
    • The library supports the creation of reusable guidance strategies and general patterns.
    • Further research is needed to align guidance practice with its theoretical underpinnings.