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Extensive research underscores the importance of stimulating students' interest in learning, as it can improve key educational outcomes such as self-regulation, collaboration, problem-solving, and overall enjoyment. Yet, the mechanisms through which interest manifests and impacts learning remain less explored, particularly in open-ended game-based learning environments like Minecraft. The unstructured nature of gameplay data in such settings poses analytical challenges. This study employed advanced data mining techniques, including changepoint detection and clustering, to extract meaningful patterns from students' movement data. Changepoint detection allows us to pinpoint significant shifts in behavior and segment unstructured gameplay data into distinct phases characterized by unique movement patterns. This research goes beyond traditional session-level analysis, offering a dynamic view of the learning process as it captures changes in student behaviors while they navigate challenges and interact with the environment. Three distinct exploration patterns emerged: surface-level exploration, in-depth exploration, and dynamic exploration. Notably, we found a negative correlation between surface-level exploration and interest development, whereas dynamic exploration positively correlated with interest development, regardless of initial interest levels. In addition to providing insights into how interest can manifest in Minecraft gameplay behavior, this paper makes significant methodological contributions by showcasing innovative approaches for extracting meaningful patterns from unstructured behavioral data within game-based learning environments. The implications of our research extend beyond Minecraft, offering valuable insights into the applications of changepoint detection in educational research to investigate student behavior in open-ended and complex learning settings.