Abstract

This article develops a framework for self-regulated digital learning, which supports for self-regulated learning (SRL) in e-learning systems. The framework emphasizes 8 features: learning plan, records/e-portfolio and sharing, evaluation, human feedback, machine feedback, visualization of goals/procedures/concepts, scaffolding, and agents. Each feature facilitates or supports one or more SRL skills, including planning, monitoring and evaluating learning, applying appropriate cognitive strategies, and setting standards of products or performance. The implementation in domain-general and -specific systems as illustrated by web-based inquiry and problem-solving are discussed. Examples and learning effects are elicited from the literature to demonstrate various designs. Approaches for designing SRL systems, educational implications, and new directions for future research incorporating SRL into digital learning are presented.

Original languageEnglish
Pages (from-to)580-589
Number of pages10
JournalJournal of Computer Assisted Learning
Volume34
Issue number5
DOIs
Publication statusPublished - 2018 Oct 1

    Fingerprint

Keywords

  • e-learning
  • feedback
  • inquiry
  • metacognition

ASJC Scopus subject areas

  • Education
  • Computer Science Applications

Cite this