Musings on telepresence and virtual presence

Surprisingly, distance education is quite an old concept. Its origins date back to the first correspondence-based course, which took place via the postal service in Boston, USA, in the 18th century. Rapid technological developments, especially in video and audio streaming, have increased the availability of such courses and moved learning into the virtual world. Due to the ongoing COVID-19 pandemic, we are witnessing an accelerated revolution in the learning process, as nearly all forms of education have been shifted online. Will this have a destructive effect on the human psyche? Is humanity sufficiently aware and ready for such a dramatic change? Will we return to physical in-classroom studies, or is remote distance education set to become the new norm? In particular, in medicine, computer science, fine arts, or architectural design, such a rapid change in the way students learn can be quite challenging. In this paper, we provide an overview of the history of distance learning, taking into account teachers’ and students’ points of view in both secondary and higher education.

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A Worldwide Journey through Distance Education—From the Post Office to Virtual, Augmented and Mixed Realities, and Education during the COVID-19 Pandemic

Nanowire-Based Soft Wearable Human–Machine Interfaces for Future Virtual and Augmented Reality Applications

We examined associations between self-reported sleep measures and cognitive level and change (age 70-76 years) in a longitudinal, same-year-of-birth cohort study (baseline N = 1091; longitudinal N = 664). We also leveraged GWAS summary data to ascertain whether polygenic scores (PGS) of chronotype and sleep duration related to self-reported sleep, and to cognitive level and change. Shorter sleep latency was associated with significantly higher levels of visuospatial ability, processing speed, and verbal memory (β ≥ |0.184|, SE ≤ 0.075, p ≤ 0.003). Longer daytime sleep duration was significantly associated slower processing speed (β = -0.085, SE = 0.027, p = 0.001), and with steeper 6-year decline in visuospatial reasoning (β = -0.009, SE = 0.003, p = 0.008), and processing speed (β = -0.009, SE = 0.002, p < 0.001). Only longitudinal associations between longer daytime sleeping and steeper cognitive declines survived correction for important health covariates and false discovery rate (FDR). PGS of chronotype and sleep duration were nominally associated with specific self-reported sleep characteristics for most SNP thresholds (standardized β range = |0.123 to 0.082|, p range = 0.003 to 0.046), but neither PGS predicted cognitive level or change following FDR. Daytime sleep duration is a potentially important correlate of cognitive decline in visuospatial reasoning and processing speed in older age, whereas cross-sectional associations are partially confounded by important health factors. A genetic propensity toward morningness and sleep duration were weakly, but consistently, related to self-reported sleep characteristics, and did not relate to cognitive level or change.

Sleep and cognitive aging in the eighth decade of life

The visualization of information is a widely used tool to improve comprehension and, ultimately, decision-making in strategic management decisions as well as in a diverse array of other domains. Across social science research, many findings have supported this rationale. However, empirical results vary significantly in terms of the variables and mechanisms studied as well as their resulting conclusion. Despite the ubiquity of information visualization with modern software, there is little effort to create a comprehensive understanding of the powers and limitations of its use. The purpose of this article is therefore to review, systematize, and integrate extant research on the effects of information visualization on decision-making and to provide a future research agenda with a particular focus on the context of strategic management decisions. The study shows that information visualization can improve decision quality as well as speed, with more mixed effects on other variables, for instance, decision confidence. Several moderators such as user and task characteristics have been investigated as part of this interaction, along with cognitive aspects as mediating processes. The article presents integrative insights based on research spanning multiple domains across the social and information sciences and provides impulses for prospective applications in the realm of managerial decision-making.

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The effects of visualization on judgment and decision-making: a systematic literature review

For decades, virtual reality (VR) and augmented reality (AR) technologies have remained absent from science and engineering workflows due to the custom-built nature of the hardware and software and oftentimes highly task-specific design. In addition to high cost, these characteristics create a critical entry barrier for most users to invest in VR and AR applications. Recent developments in both VR and AR technologies in the commercial sector have resulted in the availability of affordable consumer-grade and reliable VR and AR hardware and software tools. However, significant research is needed to adapt VR and AR technology use for data-intensive scientific and engineering applications such as network modeling research, human sciences research, and visualization of complex simulated environments. Our VR and AR applications provide examples of how scientists and engineers can use advanced visualization technologies to perform data analysis and assessment, thus transforming scientific discovery.

Virtual and Augmented Reality Applications to Support Data Analysis and Assessment of Science and Engineering

Social media data are amenable to representation by directed graphs. A node represents an entity in the social network such as a person, organization, location, or event. A link between two nodes represents a relationship such as communication, participation, or financial support. When stored in a database, these graphs can be searched and analyzed for occurrences of various subgraph patterns of nodes and links. This paper describes an interactive visual interface for constructing subgraph patterns called the Graph Matching Toolkit (GMT). GMT searches for subgraph patterns using the Truncated Search Tree (TruST) graph matching algorithm. GMT enables an analyst to draw a subgraph pattern and assign labels to nodes and links using a mouse and drop-down menus. GMT then executes the TruST algorithm to find subgraph pattern occurrences within the directed graph. Preliminary results using GMT to analyze a simulated collection of text communications containing a terrorist plot are reported.

Discovering patterns in social networks with graph matching algorithms

We investigated human understanding of different network visualizations in a large-scale online experiment. Three types of network visualizations were examined: node-link and two different sorting variants of matrix representations on a representative social network of either 20 or 50 nodes. Understanding of the network was quantified using task time and accuracy metrics on questions that were derived from an established task taxonomy. The sample size in our experiment was more than an order of magnitude larger (N = 600) than in previous research, leading to high statistical power and thus more precise estimation of detailed effects. Specifically, high statistical power allowed us to consider modern interaction capabilities as part of the evaluated visualizations, and to evaluate overall learning rates as well as ambient (implicit) learning. Findings indicate that participant understanding was best for the node-link visualization, with higher accuracy and faster task times than the two matrix visualizations. Analysis of participant learning indicated a large initial difference in task time between the node-link and matrix visualizations, with matrix performance steadily approaching that of the node-link visualization over the course of the experiment. This research is reproducible as the web-based module and results have been made available at: https://osf.io/qct84/.

Understanding node-link and matrix visualizations of networks: A large-scale online experiment

Refinement of the Ali Baba Data Set

The proliferation of immersive technologies has accentuated their potential utilization across a broad range of operational situations from strategic planning to the tactical edge. However, little is known about individual and team behavior associated with immersive technologies. We begin to address this challenge by conducting a user-based experiment comparing a 3D augmented reality (AR) device (HoloLens) to a traditional 2D flat screen display. Participant dyads used both technologies to interactively plan a mission to retrieve a repository of intelligence documents located within enemy-held territory. Survey and performance data were collected during the mission planning task. A survey instrument called the Shared Augmented Reality Experience (SARE) survey was developed to measure some aspects of “presence” as defined by factor categories from early virtual environment research. Tested during the mission planning task, a correlation analysis reveals several significant relationships between the survey items and the SARE total score. All relationships between the SARE total score and performance metrics are in the predicted direction; however, only one relationship is significant. Based on the limited data collected to date, experimental outcomes indicate that the level of shared AR experience should be associated with better performance and larger samples should be pursued.

An Augmented Reality Shared Mission Planning Scenario: Observations on Shared Experience

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