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.