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Learning management systems (LMS) provide a rich source of data about the engagement of students with courses and their materials that tends to be underutilized in practice. In this paper, we use data collected from the LMS to uncover learning strategies adopted by students and compare their effectiveness. Starting from a sample of over 11,000 enrollments at a Portuguese information management school, we extracted features indicative of self-regulated learning (SRL) behavior from the associated interactions. Then, we employed an unsupervised machine learning algorithm (k-means) to group students according to the similarity of their patterns of interaction. This process was conducted separately for undergraduate and graduate students. Our analysis uncovered five distinct learning strategy profiles at both the undergraduate and graduate levels: 1) active, prolonged and frequent engagement; 2) mildly frequent and task-focused engagement; 3) mildly frequent, mild activity in short sessions engagement; 4) likely procrastinators; and 5) inactive. Mapping strategies with the students' final grades, we found that students at both levels who accessed the LMS early and frequently had better outcomes. Conversely, students who exhibited procrastinating behavior had worse end-of-course grades. Interestingly, the relative effectiveness of the various learning strategies was consistent across instruction levels. Despite the LMS offering an incomplete and partial view of the learning processes students employ, these findings suggest potentially generalizable relationships between online student behaviors and learning outcomes. While further validation with new data is necessary, these connections between online behaviors and performance could guide the development of personalized, adaptive learning experiences.
Santos et al. (2024) studied this question.