Should we teach using schemas? Evidence from a randomised trial.
BACKGROUND
Schema-based instruction may alter knowledge organisation and diagnostic reasoning strategies through the provision of structured knowledge to novice trainees. The effects of schema-based instruction on diagnostic accuracy and knowledge organisation have not been rigorously tested.


METHODS
Year 2 medical students were randomised to learn four cardiac diagnoses using schema-based instruction (n = 26) or traditional instruction (n = 27) on a high-fidelity cardiopulmonary simulator (CPS). Students completed case-based learning in groups of two to five and underwent individual written and practical tests. The written test consisted of questions testing features that linked or distinguished diagnoses (structured knowledge) and questions testing features of individual diagnoses (factual knowledge). A practical test of diagnostic accuracy on the CPS was performed for two diagnoses present in the learning phase (taught lesions) and two untaught lesions. A majority of students (n = 37, 70%) voluntarily returned for follow-up written testing 2-4 weeks later.


RESULTS
Learning time and accuracy did not differ between students on schema-based and those on traditional instruction. Students receiving schema-based instruction performed better on structured knowledge questions (p < 0.001) and no differently on factual knowledge questions (p = 0.7). Relative differences between groups remained unchanged on follow-up testing. Diagnostic success was higher in the schema-based instruction group for taught lesions (mean difference = 38%, 95% confidence interval [CI] 20-56; p < 0.001) and untaught lesions (mean difference = 31%, 95% CI 15-48; p < 0.001).


CONCLUSIONS
Schema-based instruction was associated with improved retention of structured knowledge and diagnostic performance among novices. This study provides important proof-of-concept for a schema-based approach and suggests there is substantial benefit to using this approach with novice trainees.
Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
Sarah
S
Blissett
Rodrigo B
RB
Cavalcanti
Matthew
M
Sibbald
D000465:Algorithms; D000704:Analysis of Variance; D002170:Canada; D004501:Education, Medical / Q000379:methods* / Q000592:standards; D004521:Educational Measurement / Q000379:methods* / Q000592:standards; D006801:Humans; D013337:Students, Medical / Q000523:psychology*; D013663:Teaching / Q000379:methods* / Q000592:standards