Sensemaking in Complex Multidata Immersive Analytics Systems

Abstract

Sensemaking is the process through which individuals build meaningful explanations of complex situations. As described in Pirolli and Card’s sensemaking model, it is a cognitively demanding activity characterized by iterative cycles of information foraging, schematization, and hypothesis testing. In today’s data-rich and rapidly evolving world, there is a pressing need for tools that help users synthesize qualitative and quantitative information across multiple sources. When integrated with inquiry-based learning (IBL)—an educational model that emphasizes exploration, question-driven investigation, and active knowledge construction—sensemaking can become a powerful mechanism for deeper learning and understanding.This study investigates how SEEe, an immersive analytics system built in virtual reality (VR), facilitates sensemaking and inquiry-based learning as individuals explore multi-modal data related to systemic issues. Drawing from the Pirolli and Card sensemaking model and the Pedaste et al. IBL framework, the research analyzes user strategies and learning behaviors to determine how participants interact with and make meaning from data in VR. Unlike previous studies that have focused on either qualitative or quantitative data in isolation, this work examines how users simultaneously navigate, integrate, and reason across both data types in an immersive environment. The findings offer new insights into how immersive analytics systems can support holistic analytical thinking, promote question-driven exploration, and enable learners to engage more critically with complex real-world problems.