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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Prompted self-explanation supports monitoring of understanding, identification of knowledge gaps, and integration of new information with prior knowledge.
“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
The Analyses of Explanations section reports that students who detected gaps in their understanding often went on to explain the material to themselves. The opening passage and Discussion describe self-explanations linking example steps to principles and building understanding. But this study examines students’ self-generated explanations, not prompted self-explanation as an instructional technique; it also describes monitoring as guiding explanations rather than showing that prompting improves monitoring. Page numbers were not provided in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThe self-explanation effect was demonstrated independently of cognitive load theory.
“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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- 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
- 24
- 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
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