Learning from performance errors.

Stellan Ohlsson·1996·Psychological Review

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  3. Toward an instance theory of automatization.
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  4. Principles of Artificial Intelligence
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  5. Categorization of action slips
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  6. Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
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  7. A Closed-Loop Theory of Motor Learning
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  8. New Conceptualizations of Practice: Common Principles in Three Paradigms Suggest New Concepts for Training
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  9. YOU CAN'T PLAY 20 QUESTIONS WITH NATURE AND WIN: PROJECTIVE COMMENTS ON THE PAPERS OF THIS SYMPOSIUM
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  10. Historical review and appraisal of research on the learning, retention, and transfer of human motor skills.
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Cited by (14)

  1. The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning
    Koedinger, Kenneth R., Corbett, Albert T., Perfetti, Charles · 2012 · Cognitive Science
  2. Exploring the Assistance Dilemma in Experiments with Cognitive Tutors
    Koedinger, Kenneth R., Aleven, Vincent · 2007 · Educational Psychology Review
  3. Examining Productive Failure, Productive Success, Unproductive Failure, and Unproductive Success in Learning
    Kapur, Manu · 2016 · Educational Psychologist
  4. Detection of errors during speech production: a review of speech monitoring models.
    Postma, Albert · 2000 · Cognition
  5. A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning.
    Steenbergen-Hu, Saiying, Cooper, Harris · 2014 · Journal of Educational Psychology

See all 387

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Learning from performance errors. https://app.cochat.ai/papers/10.1037//0033-295x.103.2.241
The paper
Ohlsson, S. (1996). Learning from performance errors. Psychological Review. https://doi.org/10.1037//0033-295x.103.2.241

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.