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While existing student modeling methods focus on predicting students’ knowledge states, they often overlook the underlying cognitive processes contributing to learning. In this work, we integrate cognitive processes, specifically phases of rule learning, into student modeling, drawing inspiration from cognitive science. Rule learning involves rule search, discovery, and following, providing a systematic framework for understanding how individuals acquire and apply knowledge. We conduct two studies to explore rule learning phases in a real-world learning context. Moreover, we present a two-step approach to first predict the phases of rule learning students experience during problem solving with an intelligent tutoring system and then estimate the time spent on each predicted phase. Furthermore, we identify the relationships between the time spent on specific phases of rule learning and student performance. Our findings underscore the importance of integrating cognitive processes into student modeling for more targeted interventions and personalized support.