A framework for self-regulated digital learning (SRDL)

M. H. Yen, S. Chen*, Chia-Yu Wang, H. L. Chen, Y. S. Hsu, T. C. Liu

*Corresponding author for this work

Research output: Contribution to journalArticle

5 Scopus citations

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
StatePublished - 1 Oct 2018

Keywords

  • e-learning
  • feedback
  • inquiry
  • metacognition

Fingerprint Dive into the research topics of 'A framework for self-regulated digital learning (SRDL)'. Together they form a unique fingerprint.

  • Cite this

    Yen, M. H., Chen, S., Wang, C-Y., Chen, H. L., Hsu, Y. S., & Liu, T. C. (2018). A framework for self-regulated digital learning (SRDL). Journal of Computer Assisted Learning, 34(5), 580-589. https://doi.org/10.1111/jcal.12264