This study investigates how students explored a Minecraft-based science learning environment by analyzing their in-game movement trajectories. We use GPT-4o to identify recurring trajectory patterns from gameplay visualizations and to automatically label trajectory images, with some constructs labeled and reviewed by humans. These patterns are examined in relation to students’ self-reported survey measures. Epistemic Network Analysis is used to compare how different movement behaviors co-occurred across different learner profiles. The findings showed that students with high or improving outcomes engaged in more flexible exploration. They often wandered, changed directions, and alternated between looking around the environment before closely examining specific objects. In contrast, students with low or declining outcomes tended to concentrate on specific areas and frequently backtracked to previously visited locations. These findings highlight the importance of how, not just how much, students explore in open-ended environments as part of the learning process.