Existing research on sensemaking in immersive analytics systems primarily focuses on understanding how users complete analysis within these systems with quantitative and qualitative datasets. However, these user studies mainly concentrate on understanding analysis styles and methodologies from a predominantly novice user study population. While this approach provides excellent initial insights into what users may do within IA systems, it fails to address how professionals may utilize an immersive analytic system for analysis tasks. In our work, we build upon an existing immersive analytics concept - "Immersive Space to Think" to understand how professional user populations differ from novice users in immersive analytic system usage. We conducted a user study with 11 professional intelligence analysts who completed three analysis sessions each. Using our results from this study, we provide deep analysis into how professional users complete sensemaking within immersive analytic systems, compare our findings to previously published findings with a novice user population, and provide insights into how to develop better IA systems to support the professional analyst's strategies within these systems.

Future augmented reality (AR) glasses empower users to view personal applications and services anytime and anywhere without being restricted by physical locations and the availability of physical screens. In typical everyday activities, people move around to carry out different tasks and need a variety of information on the go. Existing interfaces in AR do not support these use cases well, especially when the number of applications increases. We explore the usability of three world-referenced approaches that move AR applications with users as they transition among different locations, featuring different levels of AR app availability: (1) always using a menu to manually open an app when needed; (2) automatically suggesting a relevant subset of all apps; and (3) carrying all apps with the users to the new location. Through a controlled study and a relatively more ecologically-valid study in AR, we reached better understandings on the performance trade-offs and observed the impact of various everyday contextual factors on these interfaces in more realistic AR settings. Our results shed light on how to better support the mobile information needs of users in everyday life in future AR interfaces.

As immersive analytics research becomes more popular, user studies have been aimed at evaluating the strategies and layouts of users’ sensemaking during a single focused analysis task. However, approaches to sensemaking strategies and layouts are likely to change as users become more familiar/proficient with the immersive analytics tool. In our work, we build upon an existing immersive analytics approach-Immersive Space to Think-to understand how schemas and strategies for sensemaking change across multiple analysis tasks. We conducted a user study with 14 participants who completed three different sensemaking tasks during three separate sessions. We found significant differences in the use of space and strategies for sensemaking across these sessions and correlations between participants’ strategies and the quality of their sensemaking. Using these findings, we propose guidelines for effective analysis approaches within immersive analytics systems for document-based sensemaking.

Analysts need to process large amounts of data in order to extract concepts, themes, and plans of action based upon their findings. Different display technologies offer varying levels of space and interaction methods that change the way users can process data using them. In a comparative study, we investigated how the use of single traditional monitor, a large, high-resolution two-dimensional monitor, and immersive three-dimensional space using the Immersive Space to Think approach impact the sensemaking process. We found that user satisfaction grows and frustration decreases as available space increases. We observed specific strategies users employ in the various conditions to assist with the processing of datasets. We also found an increased usage of spatial memory as space increased, which increases performance in artifact position recall tasks. In future systems supporting sensemaking, we recommend using display technologies that provide users with large amounts of space to organize information and analysis artifacts.

Eye gaze patterns vary based on reading purpose and complexity, and can provide insights into a reader’s perception of the content. We hypothesize that during a complex sensemaking task with many text-based documents, we will be able to use eye-tracking data to predict the importance of documents and words, which could be the basis for intelligent suggestions made by the system to an analyst. We introduce a novel eye-gaze metric called ‘GazeScore’ that predicts an analyst’s perception of the relevance of each document and word when they perform a sensemaking task. We conducted a user study to assess the effectiveness of this metric and found strong evidence that documents and words with high GazeScores are perceived as more relevant, while those with low GazeScores were considered less relevant. We explore potential real-time applications of this metric to facilitate immersive sensemaking tasks by offering relevant suggestions.

Existing research on immersive analytics to support the sensemaking process focuses on single-session sensemaking tasks. However, in the wild, sensemaking can take days or months to complete. In order to understand the full benefits of immersive analytic systems, we need to understand how immersive analytic systems provide flexibility for the dynamic nature of the sensemaking process. In our work, we build upon an existing immersive analytic system - Immersive Space to Think, to evaluate how immersive analytic systems can support sensemaking tasks over time. We conducted a user study with eight participants with three separate analysis sessions each. We found significant differences between analysis strategies between sessions one, two, and three, which suggest that immersive space to think can benefit analysts during multiple stages in the sensemaking process.

For touch typists, using a physical keyboard ensures optimal text entry task performance in immersive virtual environments. However, successful typing depends on the user’s ability to accurately position their hands on the keyboard after performing other, non-keyboard tasks. Finding the correct hand position depends on sensory feedback, including visual information. We designed and conducted a user study where we investigated the impact of visual representations of the keyboard and users’ hands on the time required to place hands on the homing bars of a keyboard after performing other tasks. We found that this keyboard homing time decreased as the fidelity of visual representations of the keyboard and hands increased, with a video pass-through condition providing the best performance. We discuss additional impacts of visual representations of a user’s hands and the keyboard on typing performance and user experience in virtual reality.

Immersive spaces have great potential to support analysts in complex sensemaking tasks, but the use of only manual interactions for organizing data elements can become tedious. We analyzed the user interactions to support cluster formation in an immersive sensemaking system, and we designed a semi-automated cluster creation technique that determines the user’s intent to create a cluster based on object proximity. We present the results of a user study comparing this proximity-based technique with a manual clustering technique and a baseline immersive workspace with no explicit clustering support. We found that semi-automated clustering was faster and preferred, while manual clustering gave greater control to users. These results provide support for the approach of adding intelligent semantic interactions to aid the users of immersive analytics systems.

The process of sensemaking involves foraging through and extracting information from large sets of documents, and it can be a cognitively intensive task. A recent approach, the Immersive Space to Think (IST), allows analysts to browse, read, mark up documents, and use immersive 3D space to organize and label collections of documents. In this study, we observed seventeen novice analysts perform a historical analysis task in order to understand how users utilize the features of IST to extract meaning from large text-based datasets. We found three different layout strategies they employed to create meaning with the documents we provided. We further found patterns of interaction and organization that can inform future improvements to the IST approach.

Augmented reality head-worn displays (AR HWDs) have the potential to assist personal computing and the acquisition of everyday information. In this research, we propose Glanceable AR, an interaction paradigm for accessing information in AR HWDs. In Glanceable AR, secondary information resides at the periphery of vision to stay unobtrusive and can be accessed by a quick glance whenever needed. We propose two novel hands-free interfaces: "head-glance", in which virtual contents are fixed to the user’s body and can be accessed by head rotation, and "gaze-summon" in which contents can be "summoned" into central vision by eye-tracked gazing at the periphery. We compared these techniques with a baseline heads-up display (HUD), which we call "eye-glance" interface in two dual-task scenarios. We found that the head-glance and eye-glance interfaces are more preferred and more efficient than the gaze-summon interface for discretionary information access. For a continuous monitoring task, the eye-glance interface was preferred. We discuss the implications of our findings for designing Glanceable AR interfaces in AR HWDs.

Specifying points in three-dimensional (3D) space is an essential function in many augmented reality (AR) applications. When an environment model is not available, a straightforward solution is to perform geometric triangulation using two rays. However, naïve implementations suffer from low accuracy caused by technical limitations of AR devices and human motor constraints. To overcome these issues, we designed and evaluated two enhanced geometric techniques for 3D point marking. VectorCloud uses multiple rays to reduce the effects of pointing jitter, and ImageRefinement improves the accuracy by allowing users to refine the 3D direction of the two rays. We conducted studies to understand the characteristics of these techniques in both ecologically valid outdoor settings using a mobile AR display and in more controlled setting using virtual reality simulation. Our experiments demonstrate that both techniques improve the precision of 3D point marking, and that ImageRefinement is superior to VectorCloud overall. These results are particularly relevant in the design of mobile AR systems intended for use in large outdoor areas.

This paper presents our solution to the 2019 3DUI Contest challenge, which focuses on passive haptics. We aimed to provide a compelling user experience in virtual reality while overcoming the limitations of physical space and current tracking devices. To meet these goals, we designed a time travel scenario that incorporates several novel features. A time machine increases efficiency in the use of physical space. A passive haptic camera prop provides a help system that is integrated into the storyline. Finally, the concept of a “temporal stabilizer” provides a plausible way to reuse a single tracking device to track multiple passive haptic props.

Specifying points in three-dimensional space is essential in AR applications. Geometric triangulation is a straightforward way to specify points, but its naïve implementation has low precision. We designed two enhanced geometric techniques for 3D point marking: VectorCloud, which uses multiple rays to reduce jittering, and ImageRefinement, which allows 3D ray refinement to improve precision. Our experiments, conducted in both simulated and real AR, demonstrate that both techniques improve the precision of 3D point marking, and that ImageRefinement is superior to VectorCloud overall. These results are particularly relevant in the design of mobile AR systems for large outdoor areas.

Augmented reality (AR) head-up displays (HUD) have previously been explored as a potential information delivery system for drivers. In driving scenarios, correct perception of virtual object distance assists with effective use of AR HUDs in safety-critical applications (e.g., collision warnings). AR volumetric displays purportedly offer increased accuracy of distance perception through consistent presentation of oculomotor cues such as vergence and accommodation over traditional displays. For this paper, we investigated volumetric AR displays as a mean of enhancing perception of virtual objects registered to the real world, specifically in terms of distance perception as a result of binocular cues. We designed and ran an experiment where participants controlled and placed virtual objects next to real-world counterparts at ranges between 7-12 meters. We found that the volumetric AR display outperformed a traditional fixed focal plane AR HUD when considering distances 5 meters greater than the traditional display's focal depth.

Biologists often perform experiments whose results generate large quantities of data, such as interactions between molecules in a cell, that are best represented as networks (graphs). To visualize these networks and communicate them in publications, biologists must manually position the nodes and edges of each network to reflect their real-world physical structure. This process does not scale well, and graph layout algorithms lack the biological underpinnings to offer a viable alternative. In this paper, we present CrowdLayout, a crowdsourcing system that leverages human intelligence and creativity to design layouts of biological network visualizations. CrowdLayout provides design guidelines, abstractions, and editing tools to help novice workers perform like experts. We evaluated CrowdLayout in two experiments with paid crowd workers and real biological network data, finding that crowds could both create and evaluate meaningful, high-quality layouts. We also discuss implications for crowdsourced design and network visualizations in other domains.