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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
Meeting Effectiveness and Inclusiveness in Remote Collabo...
Ross Cutler, Yasaman Hosseinkashi, Jamie Pool, Senja Filipi, Rob · 2021-02-19 · via cs.HC updates on arXiv.org

A primary goal of remote collaboration tools is to provide effective and inclusive meetings for all participants. To study meeting effectiveness and meeting inclusiveness, we first conducted a large-scale email survey (N=4,425; after filtering N=3,290) at a large technology company (pre-COVID-19); using this data we derived a multivariate model of meeting effectiveness and show how it correlates with meeting inclusiveness, participation, and feeling comfortable to contribute. We believe this is the first such model of meeting effectiveness and inclusiveness. The large size of the data provided the opportunity to analyze correlations that are specific to sub-populations such as the impact of video. The model shows the following factors are correlated with inclusiveness, effectiveness, participation, and feeling comfortable to contribute in meetings: sending a pre-meeting communication, sending a post-meeting summary, including a meeting agenda, attendee location, remote-only meeting, audio/video quality and reliability, video usage, and meeting size. The model and survey results give a quantitative understanding of how and where to improve meeting effectiveness and inclusiveness and what the potential returns are. Motivated by the email survey results, we implemented a post-meeting survey into a leading computer-mediated communication (CMC) system to directly measure meeting effectiveness and inclusiveness (during COVID-19). Using initial results based on internal flighting we created a similar model of effectiveness and inclusiveness, with many of the same findings as the email survey. This shows a method of measuring and understanding these metrics which are both practical and useful in a commercial CMC system.