Virtual Learning Environments and Educational Analytics: Identifying Learning Patterns to Improve Pedagogical Decision-Making

Abstract

The incorporation of virtual learning environments has expanded the possibilities for recording and analysing academic activity through data derived from navigation, resource use, task completion, interaction and assessment. The study analysed academic and documentary contributions related to virtual learning environments and educational analytics, with emphasis on identifying learning patterns and their relationship to pedagogical decision-making. A qualitative approach was adopted, with a descriptive scope and documentary design, using the PRISMA 2020 method as a reference for organising the selection of specialised sources. The findings enabled the identification of patterns associated with participation, interaction, continuity, pace of work and academic performance, whose combined interpretation provided a broader understanding of learning trajectories. Furthermore, the relationship between different indicators was found to move beyond the isolated interpretation of records and support actions related to monitoring, academic support and pedagogical adjustment. The data analysed showed that access frequency, resource use, task submission, collaborative participation and assessment performance acquire greater meaning when interpreted in an integrated manner. It is concluded that integrating virtual learning environments, educational analytics and learning pattern recognition strengthens understanding of academic behaviour and supports data-informed pedagogical decision-making.