GroupTrackVis: A Visual Analytics Approach for Online Group Discussion-Based Teaching.

Abstract

Online group discussions play an important role in education reform by facilitating collaborative learning and knowledge sharing among participants. However, instructors face significant challenges in monitoring discussion progress, tracking student performance and understanding interaction dynamics due to overlapping conversations, time-varying participant behaviors, and hidden interaction patterns. To address these challenges, we propose GroupTrackVis, an interactive visual analytics system that incorporates both advanced algorithms and novel visualization designs, to help instructors analyze group discussions mainly from three perspectives: topic evolution, student performance, and interaction. GroupTrackVis proposes an enhanced topic segmentation algorithm by incorporating word vector weighting and reply relationship analysis, effectively disentangling overlapping discussions. It also extracts six key behavioral attributes from multimodal educational data, offering a comprehensive view of student performance and providing insights into the key factors driving learning outcomes. Additionally, a multi-layer tree network with edge bundling techniques is implemented to clearly visualize the dynamic evolution of student interactions. The integration of algorithms with interactive visualizations enables instructors to explore discussions quickly and dynamically adjust their analysis as the discussion evolves. The effectiveness of GroupTrackVis is demonstrated through two case studies, a user study, and expert interviews, highlighting its ability to support instructors in identifying engaged and disengaged students, and tracking discussion dynamics.