<p>This study explores the relationship between academic performance and study progression among first-year students enrolled at the Italian IUL Telematic University during the 2023/2024 academic year. Drawing on the methodological framework of Learning Analytics, the research focuses on two pivotal indicators of academic success: Grade Point Average, representing the quality of students’ academic outcomes, and credit accumulation, reflecting the speed at which students’ progress through their courses. Taken together, these two variables offer a comprehensive measure of academic success, combining both effectiveness and efficiency in higher education trajectories. The analysis employs a two-part model: a logistic regression to account for students who earned zero credits, followed by a Quantile Regression Coefficients Modeling approach for count data to examine the distributional characteristics of credit accumulation. In parallel, a Bayesian quantile regression model with Laplace priors is applied to study the variation in Grade Point Average. To assess the association between high academic achievement and rapid progression, both variables are transformed into binary outcomes and analyzed through a bivariate logistic regression model. The results show the value of Grade Point Average and credit accumulation as complementary indicators of academic success. Furthermore, the study highlights how Learning Analytics can be effectively leveraged to identify at-risk students early and to inform the design of personalized educational interventions, particularly in the context of online and distance learning environments.</p>