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Using cognitive theory to facilitate medical education
Open accessAbstract
BACKGROUND: Educators continue to search for better strategies for medical education. Although the unifying theme of reforms was "increasing interest in, attention to, and understanding of the knowledge base structures", it is difficult to achieve all these aspects via a single type of instruction. METHODS: We used related key words to search in Google Scholar and Pubmed. Related search results on this topic were selected for discussion. RESULTS: Despite the range of different methods used in medical education, students are still required to memorize much of what they are taught, especially for the basic sciences. Subjects like anatomy and pathology carry a high intrinsic cognitive load mainly because of the large volume of information that must be retained. For these subjects, decreasing cognitive load is not feasible and memorizing appears to be the only strategy, yet the cognitive load makes learning a challenge for many students. Cognitive load is further increased when inappropriate use of educational methods occurs, e.g., in problem based learning which demands clinical reasoning, a high level and complex cognitive skill. It is widely known that experts are more skilled at clinical reasoning than novices because of their accumulated experiences. These experiences are based on the formation of cognitive schemata. In this paper we describe the use of cognitive schemata, developed by experts as worked examples to facilitate medical students' learning and to promote their clinical reasoning. CONCLUSION: We suggest that cognitive load theory can provide a useful framework for understanding the challenges and successes associated with education of medical professionals.
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“The effects of different teaching methods on students’ cognitive load have been described in other settings [5, 20–24].”
From Teaching a single manual therapy technique at a time reduces cognitive load in physiotherapy students: a randomized controlled educational study (2025) · cites this paper as [23]
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Metadata
- Authors
- Qiao, Y. Q., Shen, J., Liang, X., Ding, S., Chen, F. Y., Shao, L., Zheng, Q., & Ran, Z. H.
- Affiliations
- Shanghai Jiao Tong University (CN)
- Corresponding author
- Ran, Zhi Hua
- Venue
- BMC Medical Education
- Published
- 2014-04-14
- Volume
- 14
- Issue
- 1
- Pages
- 79-79
- ISSN
- 1472-6920 (ISSN-L)
- PMC
- PMC3989791
- OpenAlex
- W2136692330
- Cited by
- 111
- References
- 59
- Citations by year
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- FWCI
- 7.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Medical Education (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Shanghai Jiao Tong University
- Chinese Medical Association
- Topics
- Social Sciences › Education › Innovations in Medical Education
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Visual and Cognitive Learning Processes
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- cognitive load theory, cognitive schemas, worked examples, clinical reasoning, medical education, problem-based learning, memorization, expert-novice differences, anatomy education, pathology education
- MeSH terms
- Cognition, Education, Medical, Humans, Learning, Psychological Theory, Teaching, Problem-Based Learning
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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