Learning Analytics in a Non-Linear Virtual Course

Researchers have extensively explored learning analytics in online courses, primarily focusing on linear course structures where students progress sequentially through lessons and assessments. However, non-linear courses, which allow students to complete tasks in any order, present unique challenges for learning analytics due to the variability in course progression among students. This study proposes a method for applying learning analytics to non-linear, self-paced MOOC-style courses, addressing early performance prediction and online learning pattern detection. The novelty of our approach lies in introducing a personalized feature aggregation that adapts to each student's progress rather than being defined at fixed timelines. We evaluated three types of features-engagement, behavior, and performance-using data from a non-linear large-scale Moodle course designed to prepare high school students for a public university entrance exam. Our approach predicted early student performance, achieving an F1-score of 0.73 at a 20% cumulative weight assessment. Feature importance analysis revealed that performance and behavior were the strongest predictors, while engagement features, such as time spent on educational resources, also played a significant role. In addition to performance prediction, we conducted a clustering analysis that identified four distinct online learning patterns recurring across various cumulative weight assessments. These patterns provide valuable insights into student behavior and performance and have practical implications, enabling educators to deliver more personalized feedback and targeted interventions to meet individual student needs.