Abstract
Extended reality (XR)-based simulation training offers unique features that facilitate collection of dynamic behavioral data and increased immersion/realism while providing opportunities for training health care professionals on critical events that are difficult to recreate in real life. Sequential analysis can be used to summarize learning behaviors by discovering hidden learning patterns in terms of common learning or clinical decision-making sequences. This project describes the use of sequential analysis to examine differential patterns of clinical decision-making behaviors in observed XR scenarios, allowing for new insights when using XR as a method to train for critical events and to trace clinical decision making.