Ordered Networked Analysis of Multimodal Data in Healthcare Simulations: Dissecting Team Communication Tactics

In highly immersive, team-based healthcare simulations, students must collaborate effectively to complete open-ended learning tasks. These often require students to move among multiple locations within the physical learning space, requiring the development of effective communication tactics to manage complex team dynamics and multiple concurrent dialogue segments. Analysing such distributed team dialogue using only audio data poses significant challenges as it is hard to distinguish how teams temporarily break into smaller groups. This study investigates the integration of audio and spatial data to model embodied team communication tactics in highly-dynamic healthcare simulation scenarios. We employ ordered network analysis (ONA) to examine the order of communication behaviours across coded co-located dialogue segments of teams of nursing students. We also explore the potential of ONA to enhance teaching through a qualitative assessment of its usability with teachers. Results reveal that high-performing teams employ effective team tactics by prioritising primary tasks (i.e., deteriorating patients) and performing critical tasks timely and correctly, while low-performing teams struggle to develop prioritisation tactics (i.e., focusing on stable patients) and with team coordination. Interviews with teachers found high-level information in ONA networks interpretable but faced challenges with sequential details and contextualisation. This research offers insights into improving team-based learning and assessment in healthcare education.