The widespread adoption of distance education (DE) has increased the use of Virtual Learning Environments (VLEs), resulting in large educational data repositories. This study explores applying the PrefixSpan algorithm for sequential pattern mining (SPM) in educational data to identify patterns in student interactions that predict academic success or failure. Using Python and the SPMF framework, we analyzed log data from students at the Federal University of Santa Catarina across three courses—Programming, Data Structures, and Algorithms—over four years. Our findings show that successful students engage more frequently within the VLE, demonstrating higher interaction levels. The study also highlights challenges in mining educational data, such as significant memory requirements and difficulties in identifying patterns in smaller datasets or early in the semester. Integrating SPM techniques in VLEs can offer insights for early intervention to support at-risk students. Future research should focus on enhancing real-time applications and improving result visualization for better usability by educators and learners.