Immersive Space to Think: Immersive Analytics for Sensemaking with Non-Quantitative Datasets

Lisle, Lorance Richard

Immersive Space to Think: Immersive Analytics for Sensemaking with Non-Quantitative Datasets

dc.contributor.author	Lisle, Lorance Richard	en
dc.contributor.committeechair	Bowman, Douglas Andrew	en
dc.contributor.committeemember	Gitre, Edward Joseph Khair	en
dc.contributor.committeemember	Hollerer, Tobias	en
dc.contributor.committeemember	Polys, Nicholas F.	en
dc.contributor.committeemember	Gabbard, Joseph L.	en
dc.contributor.committeemember	North, Christopher L.	en
dc.contributor.department	Computer Science and Applications	en
dc.date.accessioned	2023-02-10T09:00:20Z	en
dc.date.available	2023-02-10T09:00:20Z	en
dc.date.issued	2023-02-09	en
dc.description.abstract	Analysts often work with large complex non-quantitative datasets in order to better understand concepts, themes, and other forms of insight contained within them. As defined by Pirolli and Card, this act of sensemaking is cognitively difficult, and is performed iteratively and repetitively through various stages of understanding. Immersive analytics has purported to assist with this process through putting users in virtual environments that allows them to sift through and explore data in three-dimensional interactive settings. Most previous research, however, has focused on quantitative data, where users are interacting with mostly numerical representations of data. We designed Immersive Space to Think, an immersive analytics approach to assist users perform the act of sensemaking with non-quantitative datasets, affording analysts the ability to manipulate data artifacts, annotate them, search through them, and present their findings. We performed several studies to understand and refine our approach and how it affects users sensemaking strategies. An exploratory virtual reality study found that users place documents in 2.5-dimensional structures, where we saw semicircular, environmental, and planar layouts. The environmental layout, in particular, used features of the environment as scaffolding for users' sensemaking process. In a study comparing levels of mixed reality as defined by Milgram-Kishino's Reality-Virtuality Continuum, we found that an augmented virtuality solution best fits users' preferences while still supporting external tools. Lastly, we explored how users deal with varying amounts of space and three-dimensional user interaction techniques in a comparative study comparing small virtual monitors, large virtual monitors, and a seated-version implementation of Immersive Space to Think. Our participants found IST best supported the task of sensemaking, with evidence that users leveraged spatial memory and utilized depth to denote additional meaning in the immersive condition. Overall, Immersive Space to Think affords an effective sensemaking three-dimensional space using 3D user interaction techniques that can leverage embodied cognition and spatial memory which aids the users understanding.	en
dc.description.abstractgeneral	Humans are constantly trying to make sense of the world around them. Whether they're a detective trying to understand what happened at a crime scene or a shopper trying to find the best office chair, people are consuming vast quantities of data to assist them with their choices. This process can be difficult, and people are often returning to various pieces of data repeatedly to remember why they are making the choice they decided upon. With the advent of cheap virtual reality products, researchers have pursued the technology as a way for people to better understand large sets of data. However, most mixed reality applications looking into this problem focus on numerical data, whereas a lot of the data people process is multimedia or text-based in nature. We designed and developed a mixed reality approach for analyzing this type of data called Immersive Space to Think. Our approach allows users to look at and move various documents around in a virtual environment, take notes or highlight those documents, search those documents, and create reports that summarize what they've learned. We also performed several studies to investigate and evolve our design. First, we ran a study in virtual reality to understand how users interact with documents using Immersive Space to Think. We found users arranging documents around themselves in a semicircular or flat plane pattern, or using various cues in the virtual environment as a way to organize the document set. Furthermore, we performed a study to understand user preferences with augmented and virtual reality. We found a mix of the two, also known as augmented virtuality, would best support user preferences and ability. Lastly, we ran two comparative studies to understand how three dimensional space and interaction affects user strategies. We ran a small user study looking at how a single student uses a desktop computer with a single display as well as immersive space to think to write essays. We found that they wrote essays with a better understanding of the source data with Immersive Space to Think than the desktop setup. We conducted a larger study where we compared a small virtual monitor simulating a traditional desktop screen, a large virtual monitor simulating a monitor 8 times the size of traditional desktop monitors, and immersive space to think. We found participants engaged with documents more in Immersive Space to Think, and used the space to denote importance for documents. Overall, Immersive Space to Think provides a compelling environment that assists users in understanding sets of documents.	en
dc.description.degree	Doctor of Philosophy	en
dc.format.medium	ETD	en
dc.identifier.other	vt_gsexam:36292	en
dc.identifier.uri	http://hdl.handle.net/10919/113759	en
dc.language.iso	en	en
dc.publisher	Virginia Tech	en
dc.rights	In Copyright	en
dc.rights.uri	http://rightsstatements.org/vocab/InC/1.0/	en
dc.subject	Sensemaking	en
dc.subject	Virtual Reality	en
dc.subject	Augmented Reality	en
dc.subject	Immersive Analytics	en
dc.title	Immersive Space to Think: Immersive Analytics for Sensemaking with Non-Quantitative Datasets	en
dc.type	Dissertation	en
thesis.degree.discipline	Computer Science and Applications	en
thesis.degree.grantor	Virginia Polytechnic Institute and State University	en
thesis.degree.level	doctoral	en
thesis.degree.name	Doctor of Philosophy	en