Modeling Team-Level Multimodal Dynamics During Multiparty Collaboration

We adopt a multimodal approach to investigating team interactions in the context of remote collaborative problem solving (CPS). Our goal is to understand multimodal patterns that emerge and their relation with collaborative outcomes. We measured speech rate, body movement, and galvanic skin response from 101 triads (303 participants) who used video conferencing software to collaboratively solve challenging levels in an educational physics game. We use multi-dimensional recurrence quantification analysis (MdRQA) to quantify patterns of team-level regularity, or repeated patterns of activity in these three modalities. We found that teams exhibit significant regularity above chance baselines. Regularity was unaffected by task factors. but had a quadratic relationship with session time in that it initially increased but then decreased as the session progressed. Importantly, teams that produce more varied behavioral patterns (irregularity) reported higher emotional valence and performed better on a subset of the problem solving tasks. Regularity did not predict arousal or subjective perceptions of the collaboration. We discuss implications of our findings for the design of systems that aim to improve collaborative outcomes by monitoring the ongoing collaboration and intervening accordingly.