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ECG rhythm analysis with expert and learner-generated schemas in novice learners

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Abstract

Although instruction using expert-generated schemas is associated with higher diagnostic performance, implementation is resource intensive. Learner-generated schemas are an alternative, but may be limited by increases in cognitive load. We compared expert- and learner-generated schemas for learning ECG rhythm interpretation on diagnostic accuracy, cognitive load and knowledge acquisition. Fifty-seven medical students were randomized to two experiments. Experiment 1 (n = 29) compared use of traditional teaching frameworks to expert generated schemas. Participants randomly received either a traditional framework or an expert-generated schema to practice each of two content areas in a crossed design. Learning accuracy and cognitive load were measured during the training phase. Discriminating knowledge and diagnostic accuracy were tested immediately after the training phase and 1–2 weeks after. Using the same methodology, experiment 2 (n = 28) compared use of learner-generated versus expert-generated schemas. In experiment 1, learning from expert-generated schemas was associated with lower cognitive load (13 vs 16, p < 0.001), higher diagnostic accuracy on immediate testing (40 vs 29 %, p = 0.018), and higher discriminating knowledge (81 vs 71 %, p < 0.001). Both groups performed similarly on delayed testing (14 vs 8 %, p = 0.6). In experiment 2, use of learner-generated schemas reduced diagnostic accuracy during the training phase (55 vs 77 %, p < 0.001), with similar performance on the immediate (30 vs 33 %, p = 0.89) and delayed (7 vs 5 %, p = 0.79) testing phases.. Learner-generated schema generation was associated with increased cognitive load (17.1 vs 13.5, p < 0.001). When compared to traditional frameworks, use of an expert-generated schema improved learning of ECG rhythm interpretation. Participants generating their own schemas perform similarly to those using expert-generated schemas despite reporting higher cognitive load.

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Acknowledgments

The authors would like to acknowledge Ms. Tobi Lam for her assistance in producing the schemas, the Faculty of Medicine at the University of Toronto and the Dr. Herbert Ho Ping Kong Centre for Excellence in Education and Practice for their support of the study and Ms. Sarah Meilach for her administrative support.

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Authors and Affiliations

  1. Division of Cardiology, Western University, London, ON, Canada

    Sarah Blissett

  2. Department of Medicine, University of Toronto, Toronto, ON, Canada

    Rodrigo Cavalcanti & Matthew Sibbald

  3. Dr. Herbert Ho Ping Kong Centre for Excellence in Education and Practice, Toronto, ON, Canada

    Sarah Blissett, Rodrigo Cavalcanti & Matthew Sibbald

  4. University Hospital, 339 Windermere Road, London, ON, N6A 5A5, Canada

    Sarah Blissett

Authors
  1. Sarah Blissett
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  2. Rodrigo Cavalcanti
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  3. Matthew Sibbald
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Corresponding author

Correspondence to Sarah Blissett.

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Blissett, S., Cavalcanti, R. & Sibbald, M. ECG rhythm analysis with expert and learner-generated schemas in novice learners. Adv in Health Sci Educ 20, 915–933 (2015). https://doi.org/10.1007/s10459-014-9572-y

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  • DOI: https://doi.org/10.1007/s10459-014-9572-y

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