Learner Demographics and Course Enrollment Behavior Analysis on EduPro

This study investigates the demographic profiles and enrollment behavior of learners on EduPro, an online learning management platform, with the objective of identifying statistically significant patterns that influence course selection, completion rates, and engagement trajectories. Using a mixed-methods analytical framework, the research draws on anonymized user data encompassing variables such as age group, geographic region, educational background, employment status, and prior learning history, cross-referenced with enrollment metadata including course category, enrollment timing, session frequency, dropout rates, and certification outcomes. The analysis reveals distinct behavioral clusters across demographic segments — notably divergent enrollment motivations between learners in the 18–24 and 35–50 age cohorts, regional disparities in course type preferences, and correlations between prior educational attainment and course completion likelihood. Temporal enrollment patterns further indicate that peak registration periods are strongly associated with external labor market events and academic calendars, suggesting that learners approach self-directed education as a complement to formal or professional transitions rather than a standalone activity. Using descriptive statistics, clustering algorithms (k-means and hierarchical), and logistic regression modeling, this paper quantifies the relationship between learner attributes and enrollment outcomes. Findings contribute to the literature on learning analytics and personalized education systems, offering actionable implications for platform designers, curriculum developers, and institutional policy makers seeking to improve learner retention, reduce dropout rates, and optimize course recommendations through demographic-aware design.