The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Eliciting Self‐Explanations Improves Understanding
Abstract
Learning involves the integration of new information into existing knowledge. Generating explanations to oneself (self‐explaining) facilitates that integration process. Previously, self‐explanation has been shown to improve the acquisition of problem‐solving skills when studying worked‐out examples. This study extends that finding, showing that self‐explanation can also be facilitative when it is explicitly promoted, in the context of learning declarative knowledge from an expository text. Without any extensive training, 14 eighth‐grade students were merely asked to self‐explain after reading each line of a passage on the human circulatory system. Ten students in the control group read the same text twice, but were not prompted to self‐explain. All of the students were tested for their circulatory system knowledge before and after reading the text. The prompted group had a greater gain from the pretest to the posttest. Moreover, prompted students who generated a large number of self‐explanations (the high explainers) learned with greater understanding than low explainers. Understanding was assessed by answering very complex questions and inducing the function of a component when it was only implicitly stated. Understanding was further captured by a mental model analysis of the self‐explanation protocols. High explainers all achieved the correct mental model of the circulatory system, whereas many of the unprompted students as well as the low explainers did not. Three processing characteristics of self‐explaining are considered as reasons for the gains in deeper understanding.
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“Much research on “self-explanation” indicates that prompting students to provide verbal explanations of actions (whether their own or ones given in a worked example) can often aid robust learning (Aleven & Koedinger, 2002;Chi et al., 1994;Graesser, McNamara, & VanLehn, 2005;Hausmann & VanLehn, 2007).”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Chi et al., 1994
In the citing paper
“Encouraging students to explain to themselves parts of instruction (steps in worked example or sentences in a text) yields more robust learning than not prompting or providing such explanations to students.”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Chi et al. (1994)
In the citing paper
“When students are given a worked example or text to study, prompting them to self-explain each step of the worked example or each line of the text usually causes higher learning gains than having them study the material without such prompting (e.g., Aleven & Koedinger, 2002;Chi, de Leeuw, Chiu, & LaVancher, 1994;Renkl, Stark, Gruber, & Mandl, 1998), but exceptions are discussed below.”
From The Knowledge‐Learning‐Instruction Framework: Bridging the Science‐Practice Chasm to Enhance Robust Student Learning (2012) · cites this paper as Chi, de Leeuw, Chiu, & LaVancher, 1994
In the citing paper
“Ignoring or copying teachers' support, as opposed to applying support, results in an illusion of understanding (Chi, 2000;Chi, de Leeuw, Chiu, & LaVancher, 1994).”
From Scaffolding Student Understanding in Small-Group Work: Students’ Uptake of Teacher Support in Subsequent Small-Group Interaction (2019) · cites this paper as Chi, de Leeuw, Chiu, & LaVancher, 1994
In the citing paper
“When ignoring teachers' support, the same happens, in that students do not integrate teachers' explanations into their existing knowledge schemes.”
From Scaffolding Student Understanding in Small-Group Work: Students’ Uptake of Teacher Support in Subsequent Small-Group Interaction (2019) · cites this paper as Chi, de Leeuw, Chiu, & LaVancher, 1994
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Khemlani, S. S., & Johnson-Laird, P. N. (2012) · Acta Psychologica · doi:10.1016/j.actpsy.2012.01.010 · Cited by 36
Explanation recruits comparison in a category-learning task
Edwards, B. J., Williams, J. J., Gentner, D., & Lombrozo, T. (2019) · Cognition · doi:10.1016/j.cognition.2018.12.011 · Cited by 34
Metadata
- Authors
- Chi, M. T. H., De Leeuw, N., Chiu, M., & Lavancher, C.
- Affiliations
- National Taiwan Normal University (TW)
- University of Pittsburgh (US)
- ORCID
- Chiu, Mei‐Hung
- Venue
- Cognitive Science
- Publisher
- Wiley
- Published
- 1994-07
- Volume
- 18
- Issue
- 3
- Pages
- 439-477
- ISSN
- 0364-0213 (ISSN-L)
- 1551-6709
- OpenAlex
- W4243420394
- Cited by
- 757
- References
- 28
- Citations by year
- 2026: 51 · 2025: 26 · 2024: 35 · 2023: 29 · 2022: 36 · 2021: 48 · 2020: 53 · 2019: 35 · 2018: 34 · 2017: 27 · 2016: 42 · 2015: 28 · 2014: 30 · 2013: 34 · 2012: 35
- FWCI
- 5.72
- Type
- journal article
- Language
- en
- Open access
- closed
- 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, self-explanation prompts, declarative knowledge, expository text reading, cardiovascular system, mental models, knowledge integration, eighth grade students, reading comprehension
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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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.
