Unrealized Potentials of Self-Regulated Learning in the Era of Learning Analytics
Year: 2025
Type: article
Abstract: Self-regulated learning (SRL) and learning analytics (LA) both aim to enhance learners’autonomy, engagement and performance. However, the integration of Self-regulated learning intolearning analytics practices remains limited. This opinion paper critically examines the unrealizedpotentials of Self-regulated learning model relative to learning analytics. In recent literature, thepaper identifies conceptual, methodological, design and ethical gaps that hinder the support ofSelf-regulated model through analytics. It argues that self-regulated learning model is often appliedusing surface-level indicators which neglect the metacognitive and affective dimensions. In termsof methods, data logs dominate the dynamic and cyclical nature of self-regulated learning.Dashboards reveal past behaviors instead of scaffolding regulation. The ethical issues regardingtransparency and learner agency remain unresolved. This paper advances an agenda for realizingself-regulated learning’s potential through analytics. Multimodal and temporal methodologiesincluding scaffolded dashboard design, and ethical design principles are advocated. Applyinganalytics as scaffolding mechanisms rather than monitoring tools would fully support learneragency, reflection and lifelong learning.
Author: Akinbami, Oluwaseyi
Institution: National Open University of Nigeria
Language: English
Subfield: Computer Science Applications
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W7080231102
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