Quantitative Assessment of Medical Student Learning through Effective Cognitive Bayesian Representation

  •  Zhidong Zhang    
  •  Jingyan Lu    


The changes of learning environments and the advancement of learning theories have increasingly demanded for feedback that can describe learning progress trajectories. Effective assessment should be able to evaluate how learners acquire knowledge and develop problem solving skills. Additionally, it should identify what issues these learners have during the learning processes and why they have these issues. This study depicts visual representations of cognitive tasks as crucial points to connect learning and assessment. This study is an exploration of these cognitive tasks in complex learning environments and a quantitative representation of these measureable objects in cognitive structures.

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