Computational Measurement of Team-Process Phase Dynamics in Collaborative Virtual Reality

2026-08-19

AI Summary

本文提出一种计算框架，通过时间戳对话检测协作虚拟现实中的团队过程阶段变化，使用上下文感知的转录表示和高斯核变点检测等方法识别沟通转变。

Abstract

Collaborative virtual reality (VR) environments make team communication observable as it unfolds, but conventional transcript analyses often summarize entire trials or divide them into fixed temporal windows. Such approaches can obscure changes in team communication and coordination over time. This article presents a computational framework for detecting and interpreting dynamic team-process phases from timestamped dialogue in a collaborative VR game. The framework uses late chunking to generate context-aware transcript representations, aggregates them into temporal chunks, and applies penalized Gaussian-kernel change-point detection to identify semantic transitions in team communication. After boundary detection, term frequency--inverse document frequency (TF-IDF), non-negative matrix factorization (NMF), and representative transcript segments provide structured evidence for phase interpretation. A locally deployed large language model (LLM) uses in-context learning to generate initial interpretations that are subsequently reviewed by humans. Independently recorded interaction logs are then aligned with the detected phases to examine corresponding task-action patterns. The evaluation compares representations, pooling strategies, segmentation methods, parameter settings, reviewed phase interpretations, and phase-aligned interaction profiles. The results show that the framework identifies coherent and interpretable phase structures while preserving traceability to the underlying transcript evidence. The correspondence between transcript-derived phases and interaction behavior further supports their relevance for analyzing collaborative activity. The framework therefore offers a transparent and transferable approach for studying temporal changes in teamwork from timestamped transcripts across collaborative task settings.

Problem

• Collaborative Virtual Reality
• Team-Process Phase Dynamics
• Timestamped Dialogue
• Change-Point Detection
• Phase Interpretation

Innovation

• late chunking
• Gaussian-kernel change-point detection
• large language model (LLM)