Research Area: Machine Learning
Transfer of learning from curricular experiences to non-academic settings is a primary goal of any academic institution. In cases where skills, knowledge, and attitudes learned in curricular experiences are used to solve complex problems, transfer is especially difficult to define and measure. This study attempts to better define transfer in medical education by comparing competency-based evaluations of two required components of a large MD program: problem-based learning completed by MD students in their second year, and the family medicine clerkship completed by the same MD students in a following year. Using factor and correlational analysis, the study corroborated earlier studies that show the importance of knowledge for expertise. The study found evidence to support the existence of adaptive transfer as a phenomenon that includes selectivity and problem-finding. Areas for further research include validation of competencies that emphasize flexible use of knowledge, and description of the problem-finding process in interdisciplinary teams.
Name of the Researcher: Baer, Tom Earl
Name of the Supervisor(s): Stephen Kerr
Year of Completion: 2014
University: University of Washington
Thesis Link: Home Page Url
