Study time allocation in self-regulated learning: A metacognitive perspective and theoretical advances

Yuekun Li ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 7, 812-822

Abstract: This review explores the theoretical foundations and recent advances in study time allocation (AST) from a metacognitive perspective. It traces the conceptual pathway from metacognition to self-regulation and ultimately to self-regulated learning (SRL), highlighting key models such as discrepancy reduction, the region of proximal learning, and agenda-based regulation. Findings show that effective study time allocation relies on accurate monitoring and strategic control, yet learners often do not allocate time optimally, especially under time constraints or inaccurate self-assessment. Factors like item difficulty, reward structure, motivation, and learning goals interact to shape AST decisions. Recent research underscores the need to test whether learners’ strategies truly enhance memory performance in real contexts and how online learning environments affect self-regulation. Future directions call for interdisciplinary integration with decision-making and economics, clarifying the role of learning strategies, and promoting practical interventions to improve monitoring accuracy and self-regulation skills.

Keywords: Learning strategies; Memory monitoring; Metacognition; Self-regulated learning; Study time allocation. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://learning-gate.com/index.php/2576-8484/article/view/8729/2924 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ajp:edwast:v:9:y:2025:i:7:p:812-822:id:8729

Access Statistics for this article

More articles in Edelweiss Applied Science and Technology from Learning Gate
Bibliographic data for series maintained by Melissa Fernandes ().