Self-regulated learning (SRL) is a complex phenomenon with many interacting components and phases that evolve over time. As such, it requires an analytical lens that takes into account those characteristics. In this study, we investigate how the dynamics between the different SRL components evolve throughout four blended-learning courses. We use psychological networks —a computational method commonly used to model complex systems— to map the interaction between SRL components at the beginning and at the end of the courses. Our results show significant differences in students’ SRL processes at the two time points. These findings have implications that can inform interventions that target students’ SRL.