Effective communication is essential in high-stress environments but stress often disrupts the flow of information and leads to miscommunication. While scenario-based training exercises are widely used, post-hoc reflection and analysis of verbal interactions remain challenging due to overlapping speech, limited analysis time, and the dynamic nature of these situations. This paper introduces ROGER, a novel visual analytics interface designed to support after-action reviews of communication during high-stress training scenarios. Developed in collaboration with police trainers through an iterative design study, ROGER integrates emotional voice metrics, heart rate variability, and spoken language content to provide a comprehensive analysis of team communication. The system enables a flexible in-depth exploration of communication patterns through motifs—repeated sequences or content elements—including those generated by a large language model (LLM) as well as predefined ones. Our approach addresses the limitations of existing tools, which focus primarily on content summarization or voice replays without incorporating emotional and stress-related voice data. We validated the utility through interviews with police trainers and conducted a workshop with medical first responders to investigate the potential for cross-domain applicability. Our findings provide preliminary evidence that ROGER supports effective team performance analysis in diverse high-stress environments. See also supplemental material at https://osf.io/pc6un

Virtual Reality (VR) promises to benefit training and education by offering ubiquitous risk-free access to handson experiential learning. This paper presents an evaluation of a VR-based occupational safety training application for forestry work, assessing the VR system’s impact on training effectiveness, including skill transfer to real world settings. 73 participants experienced two learning scenarios covering different task types and settings. They were either assigned to the VR training or relied on traditional forestry training materials only.Our results confirm the positive effects of VR-based training on participants’ experience as well as successful skill transfer to real-world challenges, as evidenced by participant feedback on a broad range of experience measures. In addition, our study extends existing work by examining how varying VR training quality affects both subjective experience and objective performance outcomes in terms of learning transfer. Our findings indicate that better technical quality of the VR training indeed can translate to significantly improved performance during skill application in subsequent test situations. Surprisingly, we could not detect a similarly pronounced effect when analyzing participants’ subjective experience ratings. We reflect on these findings and discuss methodological implications for experience assessment in the context of VR-based training and education.

Visualization collections, accessed by platforms such as Tableau Online or Power Bl, are used by millions of people to share and access diverse analytical knowledge in the form of interactive visualization bundles. Result snippets, compact previews of these bundles, are presented to users to help them identify relevant content when browsing collections. Our engagement with Tableau product teams and review of existing snippet designs on five platforms showed us that current practices fail to help people judge the relevance of bundles because they include only the title and one image. Users frequently need to undertake the time-consuming endeavour of opening a bundle within its visualization system to examine its many views and dashboards. In response, we contribute the first systematic approach to visualization snippet design. We propose a framework for snippet design that addresses eight key challenges that we identify. We present a computational pipeline to compress the visual and textual content of bundles into representative previews that is adaptive to a provided pixel budget and provides high information density with multiple images and carefully chosen keywords. We also reflect on the method of visual inspection through random sampling to gain confidence in model and parameter choices.

Cloud-based visualization services have made visual analytics accessible to a much wider audience than ever before. Systems such as Tableau have started to amass increasingly large repositories of analytical knowledge in the form of interactive visualization workbooks. When shared, these collections can form a visual analytic knowledge base. However, as the size of a collection increases, so does the difficulty in finding relevant information. Content-based recommendation (CBR) systems could help analysts in finding and managing workbooks relevant to their interests. Toward this goal, we focus on text-based content that is representative of the subject matter of visualizations rather than the visual encodings and style. We discuss the challenges associated with creating a CBR based on visualization specifications and explore more concretely how to implement the relevance measures required using Tableau workbook specifications as the source of content data. We also demonstrate what information can be extracted from these visualization specifications and how various natural language processing techniques can be used to compute similarity between workbooks as one way to measure relevance. We report on a crowd-sourced user study to determine if our similarity measure mimics human judgement. Finally, we choose latent Dirichl et al.ocation (LDA) as a specific model and instantiate it in a proof-of-concept recommender tool to demonstrate the basic function of our similarity measure.

Humans rely on categories to mentally organize and understand sets of complex objects. One such set, haptic devices, has myriad technical attributes that affect user experience in complex ways. Seeking an effective navigation structure for a large online collection, we elicited expert mental categories for grounded force-feedback haptic devices: 18 experts (9 device creators, 9 interaction designers) reviewed, grouped, and described 75 devices according to their similarity in a custom card-sorting study. From the resulting quantitative and qualitative data, we identify prominent patterns of tagging versus binning, and we report 6 uber-attributes that the experts used to group the devices, favoring affordances over device specifications. Finally, we derive 7 device categories and 9 subcategories that reflect the imperfect yet semantic nature of the expert mental models. We visualize these device categories and similarities in the online haptic collection, and we offer insights for studying expert understanding of other human-centered technology.

Abstract Understanding how spaces in buildings are being used is vital for optimizing space utilization, for improving resource allocation, and for the design of new facilities. We present a multi‐year design study that resulted in Ocupado, a set of visual decision‐support tools centered around occupancy data for stakeholders in facilities management and planning. Ocupado uses WiFi devices as a proxy for human presence, capturing location‐based counts that preserve privacy without trajectories. We contribute data and task abstractions for studying space utilization for combinations of data granularities in both space and time. In addition, we contribute generalizable design choices for visualizing location‐based counts relating to indoor environments. We provide evidence of Ocupado's utility through multiple analysis scenarios with real‐world data refined through extensive stakeholder feedback, and discussion of its take‐up by our industry partner.

Creating haptic experiences often entails inventing, modifying, or selecting specialized hardware. However, interaction designers are rarely engineers, and 30 years of haptic inventions are buried in a fragmented literature that describes devices mechanically rather than by potential purpose. We conceived of Haptipedia to unlock this trove of examples: Haptipedia presents a device corpus for exploration through metadata that matter to both device and interaction designers. It is a taxonomy of device attributes that go beyond physical description to capture potential utility, applied to a growing database of 105 grounded force-feedback devices, and accessed through a public visualization that links utility to morphology. Haptipedia's design was driven by both systematic review of the haptic device literature and rich input from diverse haptic designers. We describe Haptipedia's reception (including hopes it will redefine device reporting standards) and our plans for its sustainability through community participation.