Preparing Biomedical Engineers For Real World Problem Solving

NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2209 Preparing Biomedical Engineers for Real-World Problem-Solving Kristina M. Ropella, Ph.D.1 , David M. Kelso Ph.D.2, John D. Enderle, Ph.D.3, 1 Dept. Biomedical Engineering, Marquette University / 2 Dept. Biomedical Engineering, Northwestern University/ 3 Biomedical Engineering Program, Dept. of Electrical & Computer Engineering, University of Connecticut I. Introduction Over two-thirds of graduating engineers pursue industrial positions immediately following completion of their bachelor’s degree. Upon entering the workforce, the rookie engineer is immediately confronted with challenges like circuit board fabrication, software validation, design reviews, functional requirements, specifications, project scheduling, project management, FDA compliance, 510K’s, clinical trials, ethical debate, patient risk, intellectual property, documentation, and a variety of other responsibilities. Having spent four or more years studying the theory of p-n doping, free-body diagrams, Laplace transforms, Fourier transforms, Kreb’s cycle and Poiseuille’s law, it is no wonder that the recent graduate is frustrated by the seemingly disconnect between higher education and the “real-world”. Academicians struggle to establish that balance between theory and practice. Many fear that too much “real-world” is simply job training. Yet, too little practical experience leaves the graduate with naive problem solving skills and no appreciation for approximation, optimization and error. Even everyday tasks calibrating a transducer, selecting the appropriate sampling frequency for collecting data from an instrument or writing an effective memo may be beyond the experience of the biomedical engineer trained with classic science and math courses and theory-laden textbooks written for disciplines outside biomedical engineering. Given the wide spectrum of courses addressing these real-world needs, one might consider where courses fall on a "reality" scale. At the lowest level of the reality scale are courses using analytical tools like MATLAB, SolidWorks, Mathematica or SIMULINK. Level two requires students working in teams to solve problems with a "correct answer" (like a physics or chemistry lab). Level three courses might require problems which are structured and researched by faculty, but that could have multiple solutions. As one ascends the reality scale, one finds industrial clients with fuzzy problem descriptions that require initial research to “Proceedings of the 2001 American Society for Engineering Education Annual Conference & 1 Exposition Copyright Ó 2001, American Society for Engineering Education”