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A framework for self-regulated digital learning (SRDL)
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DOI：10.1111/jcal.12264.png)
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Abstract
En 中文
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.
Keywords:
e-learning
feedback
inquiry
metacognition
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