Simulations and games offer interactive tasks that can elicit rich data, providing evidence of complex skills that are difficult to measure with more conventional items and tests. However, one notable challenge in using such technologies is making sense of the data generated in order to make claims about individuals or groups. This article presents a novel methodological approach that uses the process data and performance outcomes from a simulation-based collaborative science assessment to explore the propensities of dyads to interact in accordance with certain interaction patterns. Further exploratory analyses examine how the approach can be used to answer important questions in collaboration research regarding gender and cultural differences in collaborative behavior and how interaction patterns relate to performance outcomes.