The DOI matches its Crossref record.
Models for Understanding
Abstract
What can be done to empower students to be creative when they are faced with problems? One promising instructional technique for improving students’ understanding of scientific explanations is the use of conceptual models. This review examines three predictions concerning the effects of conceptual models on students’ understanding of scientific prose: that models improve recall of conceptual information, decrease verbatim retention, and increase creative solutions on transfer problems. In a review of 20 studies involving 31 separate tests, results consistently indicated that models can help lower aptitude learners to think systematically about the scientific material they study.
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Citations
Cited by
Instructional design models for well-structured and III-structured problem-solving learning outcomes
Jonassen, D. H. (1997) · Educational Technology Research and Development · doi:10.1007/bf02299613 · Cited by 1,608
Multimedia learning: Are we asking the right questions?
Mayer, R. E. (1997) · Educational Psychologist · doi:10.1207/s15326985ep3201_1 · Cited by 1,533
Self-citationChinn, C. A., & Brewer, W. F. (1993) · Review of Educational Research · doi:10.3102/00346543063001001 · Cited by 1,393
Mayer, R. E., & Sims, V. K. (1994) · Journal of Educational Psychology · doi:10.1037/0022-0663.86.3.389 · Cited by 1,191
Self-citationWhen is an illustration worth ten thousand words?
Mayer, R. E., & Gallini, J. K. (1990) · Journal of Educational Psychology · doi:10.1037/0022-0663.82.4.715 · Cited by 1,051
Self-citation
Metadata
- Authors
- Mayer, R. E.
- Venue
- Review of Educational Research
- Publisher
- SAGE Publications
- Published
- 1989
- Volume
- 59
- Issue
- 1
- Pages
- 43
- ISSN
- 0034-6543 (ISSN-L)
- OpenAlex
- W2124818066
- Cited by
- 671
- References
- 0
- FWCI
- 23.84
- Type
- journal article
- Language
- en
- Open access
- closed
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- Mayer, R. E. (1989). Models for Understanding. Review of Educational Research. https://doi.org/10.2307/1170446
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.
