LEARNING ANALYTICS IN CONTEMPORARY EDUCATION: FOUNDATIONS, APPLICATIONS, AND EPISTEMOLOGICAL AND ETHICAL CHALLENGES

Abstract

The advancement of digital technologies and the consolidation of virtual learning environments have significantly expanded the capacity for collecting and processing educational data, enabling the development of the field known as Learning Analytics. This interdisciplinary area integrates data science, education, and psychometrics to analyze digital traces produced by students, teachers, and educational systems, aiming to understand and optimize learning processes. This study critically examines the conceptual, methodological, and ethical foundations of Learning Analytics in contemporary education, discussing its potential for teaching personalization, dropout prevention, formative assessment improvement, and institutional decision-making support. A systematic literature review with a critical-interpretive approach was adopted, drawing on indexed databases such as Scopus, Web of Science, ERIC, and Google Scholar. It is argued that, when grounded in learning theories and guided by ethical principles of transparency and data protection, Learning Analytics can contribute to improving student performance and developing evidence-based pedagogical interventions. However, challenges emerge regarding inadequate data interpretation, the reduction of learning complexity to quantitative metrics, and the risk of excessive surveillance. It is concluded that Learning Analytics represents a strategic field for educational innovation, provided it is articulated with consistent pedagogical foundations and responsible data governance.