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    GroupTrackVis enhances online education by providing instructors with visual tools to analyze group discussions, track student performance, and understand interaction dynamics effectively.

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    Area of Science:

    • Educational Technology
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Online group discussions are crucial for collaborative learning but pose challenges for instructors in monitoring progress and interaction dynamics.
    • Overlapping conversations and hidden patterns complicate the analysis of student engagement and learning outcomes.

    Purpose of the Study:

    • To introduce GroupTrackVis, an interactive visual analytics system designed to assist instructors in analyzing online group discussions.
    • To address challenges in monitoring discussion progress, student performance, and interaction dynamics.

    Main Methods:

    • Developed an enhanced topic segmentation algorithm using word vector weighting and reply relationship analysis.
    • Extracted six key behavioral attributes from multimodal educational data for performance analysis.
    • Implemented a multi-layer tree network with edge bundling for visualizing interaction dynamics.

    Main Results:

    • GroupTrackVis effectively disentangles overlapping discussions and provides a comprehensive view of student performance.
    • The system visualizes dynamic student interactions, aiding instructors in identifying engaged and disengaged students.
    • Demonstrated effectiveness through case studies, a user study, and expert interviews.

    Conclusions:

    • GroupTrackVis empowers instructors to efficiently analyze online discussions and gain insights into student learning and interaction.
    • The system facilitates dynamic adjustment of analysis based on evolving discussion patterns.
    • Supports instructors in identifying key factors influencing learning outcomes and managing group dynamics.