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Record W2575265871 · doi:10.1111/medu.13145

Contexts, concepts and cognition: principles for the transfer of basic science knowledge

2017· article· en· W2575265871 on OpenAlexaff
Kulamakan Kulasegaram, Zarah Chaudhary, Nicole N. Woods, Kelly Dore, Alan J. Neville, Geoffrey R. Norman

Bibliographic record

VenueMedical Education · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicScience Education and Pedagogy
Canadian institutionsHamilton Health SciencesThe Wilson CentreMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsContext (archaeology)CognitionTransfer of trainingSimilarity (geometry)AnalogyConcept learningConceptual changeVariation (astronomy)Transfer of learningKnowledge transferPsychologyComputer scienceCognitive psychologyConceptual frameworkCognitive scienceMathematics educationArtificial intelligenceEpistemologyKnowledge managementNeuroscience

Abstract

fetched live from OpenAlex

CONTEXT: Transfer of basic science aids novices in the development of clinical reasoning. The literature suggests that although transfer is often difficult for novices, it can be optimised by two complementary strategies: (i) focusing learners on conceptual knowledge of basic science or (ii) exposing learners to multiple contexts in which the basic science concepts may apply. The relative efficacy of each strategy as well as the mechanisms that facilitate transfer are unknown. In two sequential experiments, we compared both strategies and explored mechanistic changes in how learners address new transfer problems. METHODS: Experiment 1 was a 2 × 3 design in which participants were randomised to learn three physiology concepts with or without emphasis on the conceptual structure of basic science via illustrative analogies and by means of one, two or three contexts during practice (operationalised as organ systems). Transfer of these concepts to explain pathologies in familiar organ systems (near transfer) and unfamiliar organ systems (far transfer) was evaluated during immediate and delayed testing. Experiment 2 examined whether exposure to conceptual analogies and multiple contexts changed how learners classified new problems. RESULTS: Experiment 1 showed that increasing context variation significantly improved far transfer performance but there was no difference between two and three contexts during practice. Similarly, the increased conceptual analogies led to higher performance for far transfer. Both interventions had independent but additive effects on overall performance. Experiment 2 showed that such analogies and context variation caused learners to shift to using structural characteristics to classify new problems even when there was superficial similarity to previous examples. CONCLUSIONS: Understanding problems based on conceptual structural characteristics is necessary for successful transfer. Transfer of basic science can be optimised by using multiple strategies that collectively emphasise conceptual structure. This means teaching must focus on conserved basic science knowledge and de-emphasise superficial features.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.001
Science and technology studies0.0020.024
Scholarly communication0.0070.013
Open science0.0030.007
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0060.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.107
GPT teacher head0.490
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations52
Published2017
Admission routes1
Has abstractyes

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