2023 ASEE Annual Conference & Exposition

Student exposure to computational modeling and simulation varies with biomedical engineering (BME) departments and institutions. Though computational science can be an entire thrust within BME research, it is also an effective educational tool for students to explore principles of core BME courses. Other engineering disciplines such as electrical or mechanical engineering regularly use computation and programming in the classroom. BME departments use these tools in varied capacities, yet most BME subdisciplines require either an understanding of quantitative relationships derived from theory or an ability to interpret and analyze quantitative data. The key biological mechanisms in cardiovascular physiology (e.g., the Frank-Starling mechanisms or Poiseuille flow) are not intuitive but can be better understood in the classroom using computational modeling and simulation. Hence, we developed an elective BME course on cardiovascular physiology that combines standard lecture-style theory presentations with computational, in-silico experiments.

The course has been offered to undergraduate and graduate students in the spring and fall 2022 quarters and is team-taught between a tenured professor and postdoctoral researcher. The class concepts follow a textbook designed for graduate and medical students that briefly summarizes key principles of cardiovascular physiology, including sarcomere function, pressure-volume loop analysis, and pulse-wave propagation. Classes were held twice a week and were split into theory lectures and in-person coding sessions. Student progress and understanding was assessed by weekly online reading quizzes and computational homework problems. Reading content was complemented by lecture style classes. In-class coding sessions provided an active learning framework for exploring topics using mathematical models developed in MATLAB. A final assessment at the end of the quarter included a review of a journal article related to cardiovascular physiology and either (a) replication of article results using computational modeling or (b) innovation on article results using the models covered in class.

The use of in-class coding and active learning style sessions not only exposed students to in-silico modeling, but also provided an “input-output” approach to understanding how physiological changes can affect measured outputs or cardiovascular indices. Our study will report anonymous pre- and post-reflections from students on the class content, their assessment on the use of in-class coding, and overall opinions of class structure. We will report which concepts were most difficult to understand from either theoretical or computational perspectives as expressed by student surveys. In summary, this work-in-progress study will provide insight into how in-silico, mechanistic computer models can promote student understanding of complex cardiovascular principles.