Evaluating Replay Techniques for Asynchronous Task Handover in Immersive Analytics
Zhengtai Gou, Junxiao Long, Tao Lu, Jian Zhao, Yalong Yang
Abstract
Immersive analytics enables collaborative data analysis in shared virtual spaces. While synchronous collaboration in such environments is well-established, real-world analysis often requires an effective task handover—the transfer of knowledge and analytical context between analysts working asynchronously. Traditional handover methods often rely on static annotations that fail to capture the dynamic problem-solving process and spatial context inherent in immersive workflows. To address this handover challenge, we explore session replay as a comprehensive approach for analysts to re-experience a predecessor’s work, facilitating a deeper understanding of both the visual details and the insight formation process. Two phases of studies were conducted to establish design guidelines for such replay systems by investigating the impact of viewing platform (PC vs. VR), perspective (first-person vs. third-person), and navigation control (active vs. passive). Phase 1 identified the optimal replay configurations within each viewing platform, revealing a platform-dependent divergence: PC users favored a guided, first-person perspective for its focused detail, while VR users benefited significantly from the agency afforded by a third-person perspective with active navigation. After refining each condition based on user feedback—including developing a novel hybrid 1PP/3PP format for PC—Phase 2 compared the two optimized systems (PC vs. VR). Our results show that the immersive VR replay led to significantly better task comprehension and workflow reconstruction accuracy, demonstrating the critical role of embodied agency in understanding complex analytical processes.
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