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
In the citing paper
“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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Cites
Axten, N., Newell, A., & Simon, H. A. (1973) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2063712 · Cited by 6,886
Categorization and Representation of Physics Problems by Experts and Novices*
Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981) · Cognitive Science · doi:10.1207/s15516709cog0502_2 · Cited by 5,248
Why a Diagram is (Sometimes) Worth Ten Thousand Words
Larkin, J. H., & Simon, H. A. (1987) · Cognitive Science · doi:10.1111/j.1551-6708.1987.tb00863.x · Cited by 3,855
The role of theories in conceptual coherence.
Murphy, G. L., & Medin, D. L. (1985) · Psychological Review · doi:10.1037/0033-295x.92.3.289 · Cited by 2,805
Advances in Instructional Psychology
(2019) · Routledge eBooks · doi:10.4324/9781315864341 · Cited by 2,538
(2014) · doi:10.4324/9781315799681 · Cited by 2,174
Advances in the Psychology of Human Intelligence
Sternberg, R. J. (2014) · Psychology Press eBooks · doi:10.4324/9781315808246 · Cited by 1,771
Eye fixations and cognitive processes
Just, M. A., & Carpenter, P. A. (1976) · Cognitive Psychology · doi:10.1016/0010-0285(76)90015-3 · Cited by 1,730
Advances in the psychology of human intelligence
Eysenck, H. J. (1983) · Behaviour Research and Therapy · doi:10.1016/0005-7967(83)90111-0 · Cited by 1,590
Michelene, T. H., Glaser, R. A., & Rees, E. T. (1981) · Defense Technical Information Center (DTIC) · Cited by 1,529
Flagged first, then by citations
Research works printed but not linked: 4
- Brown A.L., 1983, Cognitive development (Vol. III of P.H. Mussen, Ed., Handbook of child psychology, 77
- Halliday D., 1981, Fundamentals of physics.
- Lewis C.H., 1982, Proceedings of the conference of human factors in computer systems
- 10.1007/BF00116250 · DOI · Paper page
Cited by
Sorted by citations
Items 801–900 of 1,022
Les mots du vin : experts et novices diffèrent-ils quand ils décrivent des vins ?
Valentin, D., Chollet, S., & Abdi, H. (2003) · Corpus · doi:10.4000/corpus.36 · Cited by 37
Instructional explanations in history
Leinhardt, G. (1997) · International Journal of Educational Research · doi:10.1016/s0883-0355(97)89730-3 · Cited by 37
Self-explanation and digital games: Adaptively increasing abstraction
Clark, D. B., Virk, S., Barnes, J., & Adams, D. M. (2016) · Computers & Education · doi:10.1016/j.compedu.2016.09.010 · Cited by 37
Gabriele, A. J. (2006) · British Journal of Educational Psychology · doi:10.1348/000709905x89490 · Cited by 35
O'Grady, G., Yew, E. H. J., Goh, K., & Schmidt, H. (2012) · Springer eBooks · doi:10.1007/978-981-4021-75-3 · Cited by 31
Influence of abilities on performance during practice: Effects of massed and distributed practice.
Mumford, M. D., Costanza, D. P., Baughman, W. A., Threlfall, K. V., & Fleishman, E. A. (1994) · Journal of Educational Psychology · doi:10.1037/0022-0663.86.1.134 · Cited by 37
The Role of Why Questions in Effective Human Tutoring
Rosé, C. P. (2003) · Cited by 37
Individual selection of examples in an intelligent learning environment
Weber, G. (1996) · Cited by 37
Extensions of intelligent tutoring paradigms to support collaborative learning
Lesgold, A. M., Katz, S., Greenberg, L., Hughes, E., & Eggan, G. (1992) · NATO ASI series. Series F : Computer and system sciences · doi:10.1007/978-3-662-02840-7_18 · Cited by 37
Wouters, P., Paas, F., & van Merriënboer, J. J. G. (2010) · Instructional Science · doi:10.1007/s11251-008-9079-0 · Cited by 37
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Yang, S. C., Vong, W. K., Sojitra, R. B., Folke, T., & Shafto, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-89267-4 · Cited by 37
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Vollmeyer, R., & Burns, B. D. (2002) · Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) · doi:10.1027//1618-3169.49.2.98 · Cited by 36
Ayres, P. (2012) · Learning and Instruction · doi:10.1016/j.learninstruc.2012.08.002 · Cited by 40
Misconceptions and Learning Algebra
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Ngu, B. H., & Yeung, A. S. (2011) · Contemporary Educational Psychology · doi:10.1016/j.cedpsych.2011.09.001 · Cited by 36
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van den Hurk, M. M., Dolmans, D. H. J. M., Wolfhagen, I. H. A. P., vMuijtjens, A. M. M., & van der Vleuten, C. P. M. (1999) · Teaching and Learning in Medicine · doi:10.1207/s15328015tlm110403 · Cited by 36
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Renkl, A., & Atkinson, R. K. (2007) · doi:10.1093/acprof:oso/9780195178845.003.0007 · Cited by 36
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Gee, J. P. (2008) · Springer eBooks · doi:10.1007/978-0-387-09775-6_3 · Cited by 39
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Petre, M., & Blackwell, A. F. (1997) · doi:10.1145/266399.266409 · Cited by 36
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Wilkerson, L. (1996) · New Directions for Teaching and Learning · doi:10.1002/tl.37219966806 · Cited by 36
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Budé, L. M. (2007) · Research Publications (Maastricht University) · doi:10.26481/dis.20071005lb · Cited by 36
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Dyer, J.-O., Hudon, A., Montpetit-Tourangeau, K., Charlin, B., Mamede, S., & van Gog, T. (2015) · BMC Medical Education · doi:10.1186/s12909-015-0308-3 · Cited by 43
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LEUNG, M., Low, R., & Sweller, J. (1997) · Instructional Science · doi:10.1023/a:1002969618881 · Cited by 38
Facilitating deep learning through self-explanation in an open-ended domain
Weerasinghe, A., & Mitrović, A. (2006) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.3233/kes-2006-10101 · Cited by 35
Schempp, P. G., Webster, C. A., McCullick, B. A., Busch, C. A., & Mason, I. S. (2007) · Sport Education and Society · doi:10.1080/13573320701287502 · Cited by 35
Manlove, S., Lazonder, A. W., & de Jong, T. (2008) · Interactive Learning Environments · doi:10.1080/10494820701706437 · Cited by 35
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Tajika, H., Nakatsu, N., Nozaki, H., Neumann, E., & Maruno, S. (2007) · Japanese Psychological Research · doi:10.1111/j.1468-5884.2007.00349.x · Cited by 35
Creating and nurturing distributed asynchronous learning environments
Kochtanek, T. R., & Hein, K. K. (2000) · Online Information Review · doi:10.1108/14684520010350632 · Cited by 35
An analysis of learner arguments in a collective learning environment
Jermann, P., & Dillenbourg, P. (1999) · Computer-supported collaborative learning/The Computer-Supported Collaborative Learning Conference · doi:10.3115/1150240.1150273 · Cited by 35
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Supnithi, T. (1999) · Cited by 35
von der Mühlen, S., Richter, T., Schmid, S., & Berthold, K. (2019) · Instructional Science · doi:10.1007/s11251-018-9471-3 · Cited by 34
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Lee, Y., Kim, T. S., Kim, S., Yun, Y., & Kim, J. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581369 · Cited by 35
Example-based learning: The benefits of prompting organization before providing examples
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Begolli, K. N., Dai, T., McGinn, K. M., & Booth, J. L. (2021) · Instructional Science · doi:10.1007/s11251-021-09550-9 · Cited by 51
Exploring the Assistance Dilemma: Comparing Instructional Support in Examples and Problems
McLaren, B. M., van Gog, T., Ganoe, C., Yaron, D., & Karabinos, M. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-07221-0_44 · Cited by 34
Scogin, S. C., & Stuessy, C. L. (2015) · Science Education · doi:10.1002/sce.21145 · Cited by 40
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Mitrović, A. (2005) · Cited by 34
Transfer of Declarative Knowledge in Complex Information-Processing Domains
Harvey, L., & Anderson, J. R. (1996) · Human-Computer Interaction · doi:10.1207/s15327051hci1101_3 · Cited by 34
Generating explanations via analogical comparison
Hoyos, C., & Dedre Gentner, C. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1289-5 · Cited by 37
Scaffolding Self-Explanation to Improve Learning in Exploratory Learning Environments.
Bunt, A., Conati, C., & Müldner, K. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30139-4_62 · Cited by 33
Gilliam, S., Magliano, J. P., Millis, K., Levinstein, I. B., & Boonthum, C. (2007) · Behavior Research Methods · doi:10.3758/bf03193148 · Cited by 34
State of the art of interaction analysis for Metacognitive Support & DiagnosisPreprint
Dimitrakopoulou, A., Petrou, A., Martínez‐Monés, A., Marcos, J. A., Kollias, V., Jermann, P., Harrer, A., Dimitriadis, Y. A., & Bollen, L. (2006) · HAL · Cited by 34
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Tops, W., Callens, M., Desoete, A., Stevens, M., & Brysbaert, M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0106550 · Cited by 37
Najar, A. S., Mitrović, A., & McLaren, B. M. (2016) · User Modeling and User-Adapted Interaction · doi:10.1007/s11257-016-9181-y · Cited by 37
How Do Creative Experts Practice New Skills? Exploratory Practice in Breakdancers
Shimizu, D., & Okada, T. (2018) · Cognitive Science · doi:10.1111/cogs.12668 · Cited by 41
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The Tutoring Research Group, Person, N. K., & Graesser, A. C. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47987-2_82 · Cited by 33
Hershkovitz, S., Peled, I., & Littler, G. (2009) · BRILL eBooks · doi:10.1163/9789087909352_017 · Cited by 33
The role of self‐explanation in learning to use a spreadsheet through examples
Reimann, P., & Neubert, C. (2000) · Journal of Computer Assisted Learning · doi:10.1046/j.1365-2729.2000.00144.x · Cited by 33
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Hoppe, H. U. (2001) · Cited by 33
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Freedman, R., Rosé, C. P., Ringenberg, M. A., & VanLehn, K. A. (2000) · Lecture notes in computer science · doi:10.1007/3-540-45108-0_47 · Cited by 33
Student strategies for learning programming from a computational environment
Recker, M. M., & Pirolli, P. L. T. (1992) · Lecture notes in computer science · doi:10.1007/3-540-55606-0_46 · Cited by 33
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Kershaw, T. C., Flynn, C. K., & Gordon, L. T. (2012) · Thinking & Reasoning · doi:10.1080/13546783.2012.742852 · Cited by 32
Cheshire, A., Ball, L. J., & Lewis, C. N. (2005) · eScholarship (California Digital Library) · Cited by 32
Aleven, V. A. W. M. M. (2013) · Springer international handbooks of education · doi:10.1007/978-1-4419-5546-3_21 · Cited by 31
Zhao, K., & Zheng, Y. (2013) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-013-0103-z · Cited by 32
Payne, V. L., Medvedeva, O., Legowski, E. A., Castine, M., Tseytlin, E., Jukic, D. M., & Crowley, R. S. (2009) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2009.07.002 · Cited by 32
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Taasoobshirazi, G., & Carr, M. (2009) · Journal of Educational Psychology · doi:10.1037/a0014557 · Cited by 32
Hsu, C.-Y., & Tsai, C. (2012) · Interactive Learning Environments · doi:10.1080/10494820.2012.705850 · Cited by 33
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van Velzen, J. H. (2011) · Teachers and Teaching · doi:10.1080/13540602.2012.629843 · Cited by 33
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Griffin, J. M. (2016) · Conference on Information Technology Education · doi:10.1145/2978192.2978231 · Cited by 32
Arguments in the practical reasoning underlying constructed probability responses
Curley, S. P., Browne, G. J., Smith, G. F., & Benson, P. G. (1995) · Journal of Behavioral Decision Making · doi:10.1002/bdm.3960080102 · Cited by 30
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Babin, L.-M., Tricot, A., & Mariné, C. (2009) · Computers & Education · doi:10.1016/j.compedu.2009.01.015 · Cited by 31
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Sarwar, G. S., & Trumpower, D. L. (2015) · Educational Technology Research and Development · doi:10.1007/s11423-015-9368-7 · Cited by 32
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Self-Explanation Prompts on Problem-Solving Performance in an Interactive Learning Environment.
Kwon, K., Kumalasari, C. D., & Howland, J. L. (2011) · Journal of interactive online learning · Cited by 30
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Çimer, S. O. (2011) · European Journal of Teacher Education · doi:10.1080/02619768.2011.552183 · Cited by 30
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Rau, M. A. (2016) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2623303 · Cited by 30
Rong, H., & Choi, I. (2018) · Educational Technology Research and Development · doi:10.1007/s11423-018-9629-3 · Cited by 31
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Chapter 5 Analyzing Diagnostic Expertise of Competitive Swimming Coaches
Leas, R. R., & Michelene, T. H. (1993) · Advances in psychology · doi:10.1016/s0166-4115(08)61466-1 · Cited by 30
Better Learners Use Analogical Problem Solving Sparingly
VanLehn, K. A., & Jones, R. M. (1993) · Elsevier eBooks · doi:10.1016/b978-1-55860-307-3.50050-2 · Cited by 30
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
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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.
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