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An integrated model of learning from errors
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PRE
AI
DOI：10.1080/00461520.2022.2149525.png)
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摘要
En 中文
Errors are inevitable in most learning contexts, but under the right conditions, they can be beneficial for learning. Prior research indicates that generating and learning from errors can promote retention of knowledge, higher-level learning, and self-regulation. The present review proposes an integrated theoretical model to explain two major phases of learning from self-generated errors: the Generating Errors (GE) phase, which contributes to learning via semantically related prior knowledge activation, and the Detecting and Correcting Errors (DCE) phase, which contributes to learning via self-explanation when processing and comparing one's responses with provided reference information to promote high-quality internal feedback. Our model identifies general design principles that support each phase based on prior empirical research. We conclude by identifying research gaps and future directions regarding specific design features of the GE and DCE phases and the role of students' emotion, motivation, and individual differences in learning from errors.
Keyword:
SELF-ASSESSMENT
CORRECTIVE FEEDBACK
GENERATING ERRORS
PERFORMANCE
FAILURE
PREQUESTIONS
EFFICACY
OUTCOMES
TERM
HYPERCORRECTION
期刊
E
IF:
11.4
论文数:
898
被引数:
8.7K
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