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cs.HC updates on arXiv.org

Quantitative Movement Testing: Measuring Patient Movements from a Single Smartphone Video Vision-Language Models Suppress Female Representations Under Ambiguous Input The New Social Image: How AI Competency and AI Proactivity Influence Self- and Peer-Perceptions in the Workplace TUX: Measuring Human--AI Tacit Understanding LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback VideoFDB: Evaluating Full-Duplex Vision-Speech Capabilities in Conversational Agents Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs Inform, Coach, Relate, Listen: Auditing LLM Caregiving Support Roles How Coding Agents Fail Their Users: A Large-Scale Analysis of Developer-Agent Misalignment in 20,574 Real-World Sessions MetaRanker: Human-in-the-loop Active Ranking for Metalens Image Quality Analyzing Persona Effects in Generated Explanations from Multimodal LLM Agents in Urban Perception First head-to-head comparison of agentic AI applied to the analysis of simulated data of the Einstein Telescope Granuscore: A Reference-Free Measure of Granularity for Text Analysis and Question Answering The Timing Dependencies of Trust: Speed, Accuracy, and cBCI Neuro-Decoupling in Human-AI Teams Bayesian Distributional Models of Executive Functioning Visual Matters: Connecting Aesthetic Appeal and Production Quality of Photos, Infographics and Data Visualizations to Credibility of Social Media Posts Data-driven Head Motion Generation through Natural Gaze-Head Coordination Agreement Metrics for LLM-as-Judge Evaluation: What to Report and Why Perceptually Lossless Tactile Texture Synthesis with Compact Spectral Envelope Models MambaGaze: Bidirectional Mamba with Explicit Missing Data Modeling for Cognitive Load Assessment from Eye-Gaze Tracking Data CogAdapt: Transferring Clinical ECG Foundation Models to Wearable Cognitive Load Assessment via Lead Adaptation Augmented Analytics and Decision Quality: The Role of Trust among Non-Technical BI Users Faster Completion, Less Learning: Generative AI Reduced Study Time on Math Problems and the Knowledge They Build PaintCopilot: Modeling Painting as Autonomous Artistic Continuation Personality Engineering with AI Agents: A New Methodology for Negotiation Research PULSE: Agentic Investigation with Passive Sensing for Proactive Intervention in Cancer Survivorship Access Timing as Scaffolding: A Reinforcement Learning Approach to GenAI in Education Conversations in Space: Structuring Non-Linear LLM Interactions on a Canvas MAPLE: Self-Supervised Learning-Enhanced Nonlinear Dimensionality Reduction for Visual Analysis nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
Evaluating User Experiences in Mixed Reality
Dmitry Alexandrovsky, Susanne Putze, Valentin Schwind, Elisa D. · 2021-01-16 · via cs.HC updates on arXiv.org

Measure user experience in MR (i.e., AR/VR) user studies is essential. Researchers apply a wide range of measuring methods using objective (e.g., biosignals, time logging), behavioral (e.g., gaze direction, movement amplitude), and subjective (e.g., standardized questionnaires) metrics. Many of these measurement instruments were adapted from use-cases outside of MR but have not been validated for usage in MR experiments. However, researchers are faced with various challenges and design alternatives when measuring immersive experiences. These challenges become even more diverse when running out-of-the lab studies. Measurement methods of VR experience recently received much attention. For example, research has started embedding questionnaires in the VE for various applications, allowing users to stay closer to the ongoing experience while filling out the survey. However, there is a diversity in the interaction methods and practices on how the assessment procedure is conducted. This diversity in methods underlines a missing shared agreement of standardized measurement tools for VR experiences. AR research strongly orients on the research methods from VR, e.g., using the same type of subjective questionnaires. However, some crucial technical differences require careful considerations during the evaluation. This workshop at CHI 2021 provides a foundation to exchange expertise and address challenges and opportunities of research methods in MR user studies. By this, our workshop launches a discussion of research methods that should lead to standardizing assessment methods in MR user studies. The outcomes of the workshop will be aggregated into a collective special issue journal article.