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Collaborative virtual environment (CVE)-based teamwork training offers a promising avenue for inclusive teamwork training. The incorporation of a feedback mechanism within virtual training environments can enhance the training experience by scaffolding learning and promoting active collaboration. However, an effective feedback mechanism requires a robust prediction model of collaborative behaviors. This paper presents a novel approach using hidden Markov models (HMMs) to predict human behavior in collaborative interactions based on multimodal signals collected from a CVE-based teamwork training simulator. The HMM was trained using k-fold cross-validation, achieving an accuracy of 97.77%. The HMM was evaluated against expert-labeled data and compared against a rule-based prediction model, demonstrating the superior predictive capabilities of the HMM, with the HMM achieving 90.59% accuracy compared to 76.53% for the rule-based model. These results highlight the potential of HMMs to predict collaborative behaviors that could be used in a feedback mechanism to enhance teamwork training experiences despite the complexity of these behaviors. This research contributes to advancing inclusive and supportive virtual learning environments, bridging gaps in cross-neurotype collaborations.

Synchronous collaborative virtual environments (CVEs) allow distributed teams to interact and work. CVEs afford a sense of presence, or "being there" in the workspace, as well as the opportunity to "do there" via interactions within the environment. However, there has been limited empirical evidence to support the link of presence and team performance, especially for CVE work. We identified multiple dimensions of presence that reflect relationships known to be essential to collaborative work and conducted a CVE experiment with 80 teams. Our results suggest certain aspects of presence are more important than others in driving virtual team performance. [ABSTRACT FROM AUTHOR]