21 <sup>st</sup> Century Learning in Medicine: Traditional Teaching versus Team-based Learning

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2012-06-01

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Abstract

The learning strategy developed by Duke-NUS educators, called TeamLEAD, incorporates Team-Based Learning principles. Lectures, readings and e-learning on a given topic are completed before class; in-class activity focuses on assuring understanding, applying principles, and solving problems within student teams facilitated by faculty. The study compared Duke-NUS students’ results on the National Board of Medical Examiners Comprehensive Basic Science Examination (CBSE) and United States Medical Licensing Examination (USMLE) Step 1 with those of US medical students. The Duke and Duke-NUS curriculum is unique in that the basic science foundation is taught in one year, typically half the time devoted at other US medical schools. At the end of their basic science instruction, the first three student cohorts from Duke-NUS performed comparably to US students on the CBSE At the end of their second year (devoted to clinical work), the Duke-NUS students scored significantly higher than the US students (66.5±7.8 vs. 61.0±11.0) (p<.0.05; 95% CI [65.1 to 67.9]). The first two years of Duke-NUS student also scored significantly higher than US students on the USMLE Step 1 (228.4±20.7 vs. 222±24) (p<.028; 95% CI [223.5 to 233.3]). In less curricular time, Duke-NUS students achieved the standards of basic science knowledge achieved by US medical students. Duke-NUS students at the end of their second (clinical) year, performed significantly higher than the US students.

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10.1007/BF03341758

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Kamei, RK, S Cook, J Puthucheary and CF Starmer (2012). 21 st Century Learning in Medicine: Traditional Teaching versus Team-based Learning. Medical Science Educator, 22(2). pp. 57–64. 10.1007/BF03341758 Retrieved from https://hdl.handle.net/10161/31151.

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Scholars@Duke

Robert Ken Kamei

Professor of Pediatrics
Starmer

Charles Franklin Starmer

Professor Emeritus of Computer Science

Though trained formally as an electrical engineer, I found myself starting my career in the Cardiology Division of the Department of Medicine. My first research project was to explore the nature of electically induced ventricular fibrillation - an introduction to reentrant cardiac arrhythmias. Though VF is complex, this work ignited my curioisity about the underlying nature of cellular communication - whether electrical via gap junctions or chemical via ligand-receptor interactions. Along the way, I found myself developing the computational infrastructure for the Cardiology Databank, starting the Computer Science Department and eventually finding my way to the laboratory. With my long term collaborator, Gus Grant, we explored single cardiac ion channels, drug interactions with cardiac ion channels and found ourselves facing my initial interests in reentrant cardiac arrhythmias, in this case, induced by drug-channel interactions. The modeling of cardiac and neuronal action potentials resulted in identifying the determinants of the cardiac vulnerable period and today is referred to as computational biology. Computational biology has the unique feature of providing a direct look at underlying modeled processes in contrast to laboratory investigations where much is hidden. For me, identifying the physical basis of generic biological processes (e.g. excitation, propagation, ligand-receptor binding and transporters) via computational models is simply great fun.


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