Analytics in Education Insights and Applications from Latin America (#304)
Read ArticleDate of Conference
July 15-17, 2026
Published In
"Engineering without Borders: Artificial Intelligence, Knowledge, Innovation, and Alliances for a Future from the Americas"
Location of Conference
Santiago (Chile)
Authors
Boza Torrejon, Pablo Alfredo
Puescas, Zaida
Laurent Lozano, Fernando Javier Jose
Abstract
The rapid expansion of digital technologies, higher education expansion and persistent inequalities have turned data into a strategic asset for education systems in Latin America. Universities and schools generate large volumes of data on enrolment, performance, virtual learning and institutional management, yet the systematic use of these data for decision making remains incipient and uneven across the region. This article examines how educational analytics, and specifically learning analytics, can contribute to improving quality, equity and efficiency while acknowledging structural constraints to their adoption. It draws on a narrative review of recent literature, complemented by descriptive analyses of official connectivity indicators, to situate analytics within contexts marked by deep digital and social divides. Evidence is organized at three levels: system level policies and infrastructure, institutional governance and management, and pedagogical practices in classrooms and virtual environments. Across these levels, analytics are understood as socio technical practices that reconfigure how actors perceive teaching, learning and accountability. The findings reveal a growing but fragmented landscape of projects, dashboards, early warning systems and predictive models, concentrated in institutions with stronger digital capacities and revealing gaps in data literacy, teacher development, ethical frameworks and regulation. The article argues that analytics can reinforce exclusion or become a lever for inclusion, depending on how they are embedded in broader strategies for digital transformation and social justice, and concludes by outlining context sensitive design principles that align data with educational purposes and local inequalities.