Sensemaking is the way in which we understand the world around us. Pirolli and Card developed a sensemaking model related to intelligence analysis, which involves taking raw, unstructured data, analyzing it, and presenting a report of the findings. With lower-cost immersive technologies becoming more popular, new opportunities exist to leverage embodied and distributed cognition to better support sensemaking by providing vast, immersive space for creating meaningful schemas (organizational structures) during an analysis task. This work builds on prior work in immersive analytics on the concept of Immersive Space to Think (IST), which provides analysts with immersive space to physically navigate and use to organize information during a sensemaking task. In this work, we performed several studies that aimed to understand how IST supports sensemaking and how we can develop additional features to better aid analysts while they complete sensemaking in immersive analytics systems, focusing on non-quantitative data analysis. In a series of exploratory user studies, we aimed to understand how users' sensemaking process evolves during multiple session analyses, which identified how the participants refined their use of the immersive space into later stages of the sensemaking process. Another exploratory user study highlighted how professional analysts and novice users share many similarities in immersive analytic tool usage during sensemaking within IST. In addition to looking at multi-session analysis tasks, we also explored how sensemaking strategies change as users become more familiar with the immersive analytics tool usage in an exploratory study that utilized multiple analysis tasks completed over a series of three user study sessions. Lastly, we conducted a comparative user study to evaluate how the addition of new organizational features, clustering, and linking affect sensemaking within IST. Overall, our studies expanded the IST tool set and gathered an enhanced understanding of how immersive space is utilized during analysis tasks within IST. / Doctor of Philosophy / Sensemaking is a process we do in our daily lives. It is how we understand the world around us, make decisions, and complete complex analyses, like journalists writing stories or detectives solving cases. Sensemaking involves gathering information, making sense of it, developing hypotheses, and drawing conclusions, similar to writing a report. This work builds on prior work in Immersive Space to Think (IST), which is a concept of using immersive technologies (Virtual /Augmented Reality) to support sensemaking by providing vast 3D space for organizing the data used in a sensemaking task. Additionally, using these technologies to support sensemaking provides benefits such as increased space for analysis, increased engagement, and natural user interaction, which allow us to interact with information used during sensemaking tasks in new ways. In IST, users are able to move virtual documents around in the space around them to support their analysis process. In this work, we ran a study focused on multi-session analysis within IST, revealing how users refined their document placements over time while completing sensemaking tasks within IST. We also ran a study to understand how professional analysts' and novice users' analysis with IST differed in the IST tool usage. In another user study, we explored how users' strategies for sensemaking and document layouts changed as they became more familiar with the IST tool. Lastly, we conducted a comparative user study to evaluate how new features like clustering and linking affected analysis within IST. Overall, our work contributed to an enhanced understanding of how immersive space is utilized during analysis tasks within IST.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/120974 |
| Date | 20 August 2024 |
| Creators | Davidson, Kylie Marie |
| Contributors | Computer Science and#38; Applications, North, Christopher L., Bowman, Douglas Andrew, Polys, Nicholas Fearing, Whitley, Kirsten, David-John, Brendan Matthew |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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