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    Area of Science:

    • Human-Computer Interaction
    • Design Studies
    • Computer-Supported Cooperative Work

    Background:

    • Analyzing collaborative design processes is crucial for understanding outcomes and decision-making.
    • Current methods often rely on consolidated textual data from interviews or observations, facing challenges with data volume and heterogeneity.
    • Integrating diverse data sources from workshops remains a significant challenge in design research.

    Purpose of the Study:

    • To propose a practical, modular, and adaptable framework for workshop analysis.
    • To introduce an interactive visual analysis system, reCAPit, for multimodal data integration.
    • To enhance the methodology for data-rich acquisition, AI-based extraction, and visual analysis of collaborative design workshops.

    Main Methods:

    • Developed a framework encompassing workshop setup, multimodal data acquisition, AI-based artifact extraction, and visual analysis.
    • Created the reCAPit system, integrating modalities like video, audio, notes, and gaze data.
    • Conducted six workshops across various themes and analyzed two case studies in detail.

    Main Results:

    • The reCAPit system enables flexible combination of multimodal data for analysis.
    • A multimodal streamgraph visualizes activity and attention, while topic cards summarize discussions.
    • Drill-down techniques allow inspection of raw data from various sources.

    Conclusions:

    • The proposed framework extends collaborative design workshop methodology with data-rich acquisition.
    • Combined AI-based extraction and interactive visual analysis offer transparent results dissemination.
    • This approach facilitates a deeper understanding of collaboration and decision-making in design processes.