In the medical sciences, educators struggle with the formidable task of teaching students to think deductively in order to diagnose disease. Our goal is to help students and professionals in all branches of medicine reason through the diagnostic process in a logical, systematic and thorough manner, based on sound foundations in mechanisms of disease. Learning strategies for mastering the art of diagnostic reasoning are especially problematic for novices in the medical sciences where prerequisite courses are largely fact-based. Survival strategies for success in these prerequisite courses typically included cramming instead of disciplined learning and rote memorization of facts rather than learning for mastery and understanding. While a background of facts is necessary to perform any skill, stopping at the rote memorization stage does not serve students well in applying their knowledge to diagnostic reasoning.
Educational research shows that the thinking process of experts and novices are quite different.
This research has identified discrete cognitive stages a novice must pass through to achieve the automated, subconscious thinking processes of an expert. In part because the expert's thinking processes are invisible to the observer, the transition in thinking from novice level to expert can be difficult and frustrating. Our experience shows that students having difficulties with the transition from rote memorization to analytical thinking often jump to diagnostic conclusions based on poorly supported assumptions rather than identifying and analyzing all available evidence to make sound, defensible conclusions. The PLG uses a case-based approach to instruction that actively engages students in the learning process and links performance with positive reward. New drag and drop Java tools facilitate this higher-order thinking as the student associates data abnormalities with their casual mechanism in a hierarchy on a computer grid.