Interactive simulations encourage students to practice skills essential to understanding and learning sciences. Alas, inquiry learning with interactive simulations is challenging. In this paper, we seek to identify inquiry patterns across topics and evaluate their stability with regard to common behaviors and student membership. Applying a clustering approach, we propose an encoding through which we can model students’ strategies in diverse environments. Specifically, we encode each sequence with three different levels of granularity which range from simulation-specific characteristics to simulation-agnostic features. Using this generalizable encoding, we find two clusters for each of two simulations. The formed groups exhibit similar learning patterns across environments. One systematically cycles through exploring and recording systematically over all variables. The other group explores the simulation more freely. This suggests that our feature encoding captures inherent quality of inquiry with simulations and can be used to characterize learners knowledge of productive exploration.