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What Is Case-Based Reasoning?
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Citations
Cited by
HMELO-SILVER, C. E., DUNCAN, R. G., & CHINN, C. A. (2007) · Educational Psychologist · doi:10.1080/00461520701263368 · Cited by 2,641
Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., Puntambekar, S., & Ryan, M. (2003) · Journal of the Learning Sciences · doi:10.1207/s15327809jls1204_2 · Cited by 1,061
Self-citationToward an Instructionally Oriented Theory of Example‐Based Learning
Renkl, A. (2014) · Cognitive Science · doi:10.1111/cogs.12086 · Cited by 509
Commonsense reasoning and commonsense knowledge in artificial intelligence
Davis, E., & Marcus, G. (2015) · Communications of the ACM · doi:10.1145/2701413 · Cited by 434
Analogy and Relational Reasoning
Holyoak, K. J. (2012) · The Oxford Handbook of Thinking and Reasoning · doi:10.1093/oxfordhb/9780199734689.013.0013 · Cited by 394
Parallel prototyping leads to better design results, more divergence, and increased self-efficacy
Dow, S. P., Glassco, A., Kass, J., Schwarz, M., Schwartz, D. L., & Klemmer, S. R. (2010) · ACM Transactions on Computer-Human Interaction · doi:10.1145/1879831.1879836 · Cited by 367
A Framework for Designing Scaffolds That Improve Motivation and Cognition
Belland, B. R., Kim, C., & Hannafin, M. J. (2013) · Educational Psychologist · doi:10.1080/00461520.2013.838920 · Cited by 306
Explanation in Case-Based Reasoning—Perspectives and Goals
Sørmo, F., Cassens, J., & Aamodt, A. (2005) · Artificial Intelligence Review · doi:10.1007/s10462-005-4607-7 · Cited by 239
Design fixation: Classifications and modern methods of prevention
Youmans, R. J., & Arciszewski, T. (2014) · Artificial Intelligence for Engineering Design, Analysis and Manufacturing · doi:10.1017/s0890060414000043 · Cited by 121
Avoiding Design Fixation: Transformation and Abstraction in Mapping from Source to Target
GOLDSCHMIDT, G. (2011) · The Journal of Creative Behavior · doi:10.1002/j.2162-6057.2011.tb01088.x · Cited by 64
Belland, B. R. (2011) · Educational Psychology Review · doi:10.1007/s10648-011-9176-5 · Cited by 64
Instructional Scaffolding: Foundations and Evolving Definition
Belland, B. R. (2017) · Instructional Scaffolding in STEM Education · doi:10.1007/978-3-319-02565-0_2 · Cited by 40
Schultheis, A., Bertrand, Y., Grüger, J., Malburg, L., Bergmann, R., & Serral Asensio, E. (2025) · IoT · doi:10.3390/iot6040063 · Cited by 2
From Obstacle to Catalyst: A Narrative Review of Learning from Errors in Science Education
Fabre-Mitjans, N., Jiménez-Valverde, G., Viciana, S., & Crespo i Torres, F. (2026) · Encyclopedia · doi:10.3390/encyclopedia6090192 · Cited by 2
Metadata
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.
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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.