Self-regulated learning positively influences learning in MOOCs through motivational strategies like self-efficacy, task value, and goal setting.

Systematic literature review on self-regulated learning in massive open online courses

Self-regulated learning involves three layers: processing mode regulation, learning process regulation, and self-regulation.

Self-Regulated Learning.

Goal level, persistence, effort, and self-efficacy are the key self-regulation constructs that influence work-related learning, accounting for 17% of the variance in learning.

A meta-analysis of self-regulated learning in work-related training and educational attainment: what we know and where we need to go.

Self-regulated learning is a critical factor in smart learning environments, with components like motivation, cognitive, metacognitive, and self-efficiency playing key roles.

Review on self-regulated learning in smart learning environment

Self-regulated learning in MOOCs is an emerging study area, with goal setting, help seeking, time management, self-evaluation, and strategic planning being key strategies.

Self-Regulated Learning in Massive Online Open Courses: A State-of-the-Art Review

Self-regulated learning training programs enhance university students' academic performance, self-regulated learning strategies, and motivation.

Self-Regulated Learning Training Programs Enhance University Students’ Academic Performance, Self-Regulated Learning Strategies, and Motivation: A Meta-Analysis

Self-regulated learning training programs effectively enhance academic achievement, cognitive and metacognitive strategy application, and motivation among primary school students, with the most effective characteristics based on grade level, training duration, and subject context.

How can primary school students learn self-regulated learning strategies most effectively? A meta-analysis on self-regulation training programmes

Self-regulated learning (SRL) in classrooms can help students develop strategic, motivated, and independent learning strategies, benefiting both teachers and students.

Classroom Applications of Research on Self-Regulated Learning

Online learning tools support self-regulated learning through goal setting, monitoring, and self-evaluation, but often lack explicit SRL theoretical models and rarely assess their impact on learning performance.

Tools Designed to Support Self-Regulated Learning in Online Learning Environments: A Systematic Review

Supporting self-regulated learning in distance education environments is crucial for academic success, but the distribution of interventions is uneven and emotion regulation and preparatory and appraisal phases are underexplored.

Supporting Self-Regulated Learning in Distance Learning Contexts at Higher Education Level: Systematic Literature Review

Existing learning analytics dashboards are rarely grounded in learning theory, do not support metacognition, and lack effective learning tactics and strategies.

A Systematic Review of Empirical Studies on Learning Analytics Dashboards: A Self-Regulated Learning Perspective

Effective self-regulated learning strategies can enhance medical students' and residents' workplace-based learning.

Self‐regulated learning in the clinical context: a systematic review

Six self-regulated learning models provide an integrative and coherent framework for research, with differential effects based on students' developmental stages or educational levels.

A Review of Self-regulated Learning: Six Models and Four Directions for Research

Self-regulated learning can be fostered by teachers who minimize academic competition, explain appropriate strategies, provide assistance during problem solving, and promote an atmosphere of collaboration in the classroom.

Development Aspects of Self-Regulated Learning

Self-regulated academic learning has distinctive features and impacts students' learning and achievement.

Self-Regulated Learning and Academic Achievement: An Overview

Learning analytics can enhance self-regulated learning by analyzing interactions and learning trajectories, offering opportunities for analyzing and supporting it in interactive learning environments.

Understanding, evaluating, and supporting self-regulated learning using learning analytics

Self-regulated learning involves nondeliberative, knowledge-based elements, which can be further explored in future research on learning effectively by oneself.

Inherent details in self-regulated learning

The social cognitive view of self-regulated academic learning emphasizes the role of academic self-efficacy beliefs and self-observation, self-judgment, and self-reactions in improving student learning and achievement.

A social cognitive view of self-regulated academic learning.

Self-regulated learning has potential in teacher education, but its implementation requires new organizational and curriculum structures, and student teachers have more positive attitudes towards it than teachers.

Self-regulated learning in the context of teacher education

Self-regulated learning is crucial for students to be responsible for their own learning, and teaching them strategies like analysing, monitoring, and self-reflection can significantly improve their learning outcomes.

Self-regulated Learning

These studies suggest self-regulated learning positively influences learning through motivational strategies, cognitive and metacognitive processes, and strategic planning, and is critical in various educational environments including MOOCs, smart learning environments, and traditional classrooms.