The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Developing conceptual understanding and procedural skill in mathematics: An iterative process.
Abstract
The authors propose that conceptual and procedural knowledge develop in an iterative fashion and that improved problem representation is 1 mechanism underlying the relations between them. Two experiments were conducted with 5th- and 6th-grade students learning about decimal fractions. In Experiment 1, children's initial conceptual knowledge predicted gains in procedural knowledge, and gains in procedural knowledge predicted improvements in conceptual knowledge. Correct problem representations mediated the relation between initial conceptual knowledge and improved procedural knowledge. In Experiment 2, amount of support for correct problem representation was experimentally manipulated, and the manipulations led to gains in procedural knowledge. Thus, conceptual and procedural knowledge develop iteratively, and improved problem representation is 1 mechanism in this process
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Citations
Cites
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“First, amount of conceptual knowledge in a domain is positively correlated with the accuracy and elaborateness of problem representations (Chase & Simon, 1973; Chi, Feltovich, & Glaser, 1981).”
Self‐Explanations: How Students Study and Use Examples in Learning to Solve Problems
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Koedinger, K. R., Corbett, A. T., & Perfetti, C. (2012) · Cognitive Science · doi:10.1111/j.1551-6709.2012.01245.x · Cited by 772
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Jordan, N. C., Glutting, J., & Ramineni, C. (2010) · Learning and Individual Differences · doi:10.1016/j.lindif.2009.07.004 · Cited by 471
The shuffling of mathematics problems improves learning
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Principles of evolutionary educational psychology
Geary, D. C. (2002) · Learning and Individual Differences · doi:10.1016/s1041-6080(02)00046-8 · Cited by 163
Eysink, T. H. S., de Jong, T., Berthold, K., Kolloffel, B., Opfermann, M., & Wouters, P. (2009) · American Educational Research Journal · doi:10.3102/0002831209340235 · Cited by 158
Moreno, R., Ozogul, G., & Reisslein, M. (2011) · Journal of Educational Psychology · doi:10.1037/a0021995 · Cited by 128
The Effects of Dynamic Strategic Math on English Language Learners’ Word Problem Solving
Orosco, M. J., Swanson, H. L., O’Connor, R., & Lussier, C. (2013) · The Journal of Special Education · doi:10.1177/0022466911416248 · Cited by 65
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Chinnappan, M., & Forrester, T. (2014) · Mathematics Education Research Journal · doi:10.1007/s13394-014-0131-x · Cited by 58
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Doerr, H. M., Ärlebäck, J. B., & Costello Staniec, A. (2014) · Journal of Engineering Education · doi:10.1002/jee.20037 · Cited by 37
Comparing and contrasting different algorithms leads to increased student learning
Patitsas, E., Craig, M., & Easterbrook, S. (2013) · Proceedings of the ninth annual international ACM conference on International computing education research · doi:10.1145/2493394.2493409 · Cited by 26
Testolin, A. (2024) · Applied Sciences · doi:10.3390/app14020744 · Cited by 24
Achievement motivation and knowledge development during exploratory learning
DeCaro, D. A., DeCaro, M. S., & Rittle-Johnson, B. (2015) · Learning and Individual Differences · doi:10.1016/j.lindif.2014.10.015 · Cited by 20
Self-citationMathematics education: What was it, what is it, and what will it be?
Cotič, M., Doz, D., Jenko, M., & Žakelj, A. (2024) · International Electronic Journal of Mathematics Education · doi:10.29333/iejme/14663 · Cited by 19
Ngu, B. H., & Phan, H. P. (2024) · Asian Journal for Mathematics Education · doi:10.1177/27527263241266765 · Cited by 10
Metadata
- Authors
- Rittle-Johnson, B., Siegler, R. S., & Alibali, M. W.
- Venue
- Journal of Educational Psychology
- Published
- 2001-06
- Volume
- 93
- Issue
- 2
- Pages
- 346-362
- ISSN
- 0022-0663 (ISSN-L)
- 1939-2176
- OpenAlex
- W2147763969
- Cited by
- 1,075
- References
- 5
- FWCI
- 7.37
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
