Mathematically Quantifying Learning Experience: Correlating Magnetic Resonance Imaging (MRI) and Plastinated Brain Sections Using Utility Analysis

1 Department of Industrial and Systems Engineering Oakland University Rochester, MI 48309 USA 2,3 Department of Neurosciences College of Medicine, University of Toledo, Ohio, USA ABSTRACT: Objectives: Many researchers have shown that when used in conjunction, multiple pedagogic approaches increase student learning. Diagnostic imaging is used extensively to complement cadaveric dissection in courses such as neuroanatomy. This article provides a general framework to analyze and quantify the learning utility from combining multiple teaching methods for a richer learning experience. We present an example from neuroanatomy that combines the use of Magnetic Resonance Imaging and plastinated specimens. Materials and Methods: Two brains, from female cadavers aged between 70-90 years of age, were removed from the body, fixed in 10% formalin (mixture of 10 pbv of 37% formalin with 90 pbv water) and stored for at least 6 months before use. After six months, each brain was washed in tap-water overnight and sectioned coronally using a deli slicer. Slices measuring 10 mm in thickness were produced which were then plastinated using the standard S10/S3 silicone method. The plastinated brain slices were then used in conjunction with MRI images to analyze students’ preferences in neuroanatomy teaching. Results: Our method first aims to understand the tradeoff preferences of the educators and the students between multiple teaching methods. These preferences and tradeoff information can be incorporated into a learning utility function that brings a wealth of tools from decision analysis to analyze the proper allocation of teaching time between different methods. The synergistic effect of using multiple teaching tools in anatomy classes is, therefore, formally quantified. Conclusions: Using the example of MRI and plastinated specimens in neuroanatomy, we showed how one can analyze tradeoff between two modalities. In other words, one can determine how many hours of one modality can be traded off for another to have the same learning utility. One can also deduce the best allocation of a fixed total number of hours to maximize learning utility.