Conceptual Framework for the Relationship between Learning Analytics Dashboard, Adaptive Environments, and Self-Regulated Learning

The digital transformation of higher education has created an urgent demand for intelligent systems that move beyond digital content delivery toward adaptive, data-driven ecosystems. This paper introduces a conceptual framework integrating three foundational pillars: Learning Analytics Dashboard (LAD), Adaptive Environments (AE), and Self-Regulated Learning (SRL). While earlier studies examined these elements individually or in pairs, their comprehensive integration remains limited. Through a conceptual–analytical review of 55 international and regional studies (2019–2024), the paper proposes the Self-Generating Adaptive Learning Loop: a cyclical model where LAD capture and visualize real-time learner data, AE translate analytics into adaptive pathways, and SRL provides the pedagogical anchor for goal-setting, monitoring, and reflection. The study contributes theoretically by extending dual models into a unified framework and practically by offering implementation guidelines for LMS design—emphasizing LAD integration, adaptive sequencing, and SRL scaffolds. Pedagogical implications include enhanced autonomy, persistence, and reduced dropout, while technological implications highlight the importance of interoperable data pipelines and enriched learner models. Despite its conceptual focus, the study outlines a clear agenda for empirical validation, longitudinal outcome assessments, and design-based refinements of adaptive dashboards. Overall, the framework advances both scholarship and practice by providing a credible pathway toward learner-centered ecosystems that are personalized, reflective, and data-driven.