This paper presents a systematic review of the application of Sequential Pattern Mining (SPM) techniques in Virtual Learning Environments (VLEs) to improve learning analytics. The authors used the PRISMA-ScR methodology to explore studies that analyze log data from VLEs, particularly focusing on how SPM can identify student behaviors and predict academic performance. Out of 22 initially selected studies, 9 met the criteria and were fully analyzed. These studies primarily applied SPM in high school and undergraduate settings using algorithms like GSP, PrefixSpan, and Apriori. The review highlights challenges such as data granularity, the lack of real-time implementations, and the effectiveness of SPM in predicting student success and risk. The findings emphasize the need for better data preprocessing and integration of SPM into educational systems for early identification of at-risk students. Future work suggests enhancing SPM algorithms with real-time data analysis to support timely interventions in students’ learning processes.