Log-based learning analytics of gamified Moodle activities: Quantifying student engagement

This study presents a log-based learning analytics pipeline for quantifying user engagement in Moodle, demonstrating how event log data can be transformed into analyzable interaction patterns through extraction, anonymization, categorization, and statistical modeling. The approach applies distributional testing using chi-square statistics and Cramér’s V to identify structural differences in user activity. As a case study, the method was implemented in an English for Specific Purposes (ESP) course, comparing a control group following a traditional LMS configuration (N = 40) and an experimental group using a gamified Moodle environment (N = 40). Results indicated that the gamified configuration generated significantly higher frequencies of system-level and assessment-related interactions, as well as more sustained activity across the instructional period, while the control group relied primarily on static content access and exhibited declining participation over time. Engagement was operationalized through event frequencies, capturing observable behavioral differences rather than cognitive or learning outcomes. Beyond the educational setting, the study illustrates how reproducible event-log analytics can be used to detect behavioral shifts in technology-supported environments, offering a methodological template that is potentially transferable to similar contexts in applied computer science.