The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
Open accessAbstract
The present paper analyzes the self‐generated explanations (from talk‐aloud protocols) that “Good” and “Poor” students produce while studying worked‐out examples of mechanics problems, and their subsequent reliance on examples during problem solving. We find that “Good” students learn with understanding: They generate many explanations which refine and expand the conditions for the action parts of the example solutions, and relate these actions to principles in the text. These self‐explanations are guided by accurate monitoring of their own understanding and misunderstanding. Such learning results in example‐independent knowledge and in a better understanding of the principles presented in the text. “Poor” students do not generate sufficient self‐explanations, monitor their learning inaccurately, and subsequently rely heavily on examples. We then discuss the role of self‐explanations in facilitating problem solving, as well as the adequacy of current AI models of explanation‐based learning to account for these psychological findings.
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“This mechanism resembles prompted self-explanation, an instructional technique that encourages learners to articulate their reasoning and has been shown to support monitoring of understanding, identification of knowledge gaps, and integration of new information with prior knowledge (Bisra et al., 2018;Chi et al., 1989).”
From Think Before You Accept: Can Written Justification Reduce Uncritical Uptake of AI Writing Suggestions? (2026) · cites this paper as Chi et al., 1989
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“The self-explanation effect was demonstrated independently of cognitive load theory (Chi et al. 1989) but can be explained by the theory.”
From Cognitive Architecture and Instructional Design: 20 Years Later (2019) · cites this paper as Chi et al. 1989
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“Moreover, researchers have advocated active learning because it enables learners to connect new information with the knowledge they already possess (Chi et al. 1989;Mayer 2001;Wittrock 1974).”
From Interactivity in Video-based Models (2007) · cites this paper as Chi et al. 1989
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“Asking learners to explain to themselves aloud what they have understood from a learning task has proved to be an effective instructional method (Chi et al. 1989;Renkl 1997;Renkl and Atkinson 2002).”
From Interactivity in Video-based Models (2007) · cites this paper as Chi et al. 1989
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Metadata
- Authors
- Chi, M. T. H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R.
- Affiliations
- RAND Corporation (US)
- TU Bergakademie Freiberg (DE)
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1989-04
- Volume
- 13
- Issue
- 2
- Pages
- 145-182
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W4239816899
- Cited by
- 1,714
- References
- 33
- Citations by year
- 2026: 54 · 2025: 49 · 2024: 41 · 2023: 41 · 2022: 65 · 2021: 80 · 2020: 103 · 2019: 99 · 2018: 58 · 2017: 53 · 2016: 77 · 2015: 69 · 2014: 60 · 2013: 58 · 2012: 59
- FWCI
- 27.12
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cognitive Science (publishedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Social Sciences › Psychology › Developmental and Educational Psychology › Educational Strategies and Epistemologies
- Keywords
- self-explanation, worked examples, problem solving, mechanics problems, self-monitoring, understanding-based learning, explanation-based learning, student differences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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