This study explores student engagement in a blended university course through the application of process mining techniques to event log data extracted from a Moodle-based Learning Management System (LMS). Using the ProM framework and multiple process discovery algorithms, process models are constructed to represent the behavioral patterns of students across varying levels of engagement. The event logs capture a range of learning activities, including quiz attempts, forum participation, and access to instructional resources. Comparative analysis of the resulting models reveals distinctions in the frequency, order, and timing of activities between more and less engaged learners. The findings provide data-driven insights into student interaction with online learning environments and underscore the value of process mining as a methodological approach within learning analytics.