Computational Measurement of Team-Process Phase Dynamics in Collaborative Virtual Reality
Read the original on arXiv Machine Learning →The paper introduces a computational framework that detects dynamic team‑process phases in collaborative virtual reality (VR) by analyzing timestamped dialogue. It uses late chunking, penalized Gaussian‑kernel change‑point detection, TF‑IDF, NMF, and a locally deployed large language model to identify semantic transitions and generate interpretable phase descriptions. The detected phases are aligned with interaction logs, demonstrating that transcript‑derived phases correspond to task‑action patterns and thus provide a transparent, transferable method for studying temporal changes in teamwork.
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