Integration of a Computational Lab Sequence Into a Junior-level Quantitative Physiology Course

Kurt A. Thoroughman; Ranjan Patrick Khan; Haoxin Sun; Patricia L. Widder

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Conference: 2012 ASEE Annual Conference & Exposition
Location: San Antonio, Texas
Publication Date: June 10, 2012
Start Date: June 10, 2012
End Date: June 13, 2012
ISSN: 2153-5965
Conference Session: BME Course and Curriculum Development
Tagged Division: Biomedical
Page Count: 10
Page Numbers: 25.816.1 - 25.816.10
DOI: 10.18260/1-2--21573
Permanent URL: https://peer.asee.org/21573
Download Count: 348

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Paper Authors

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Kurt A. Thoroughman Ph.D. Washington University, St. Louis

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Kurt A. Thoroughman, Ph.D., is the Associate Chair for Undergraduate Studies and an Associate Professor in the Department of Biomedical Engineering at Washington University in St. Louis. Thoroughman has joint appointments in the departments of Anatomy & Neurophysiology and Physical Therapy.

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Ranjan Patrick Khan Washington University, St. Louis

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Department of BME

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Haoxin Sun Washington University, St. Louis

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Patricia L. Widder Washington University, St. Louis

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Patricia Widder serves as Teaching Lab Coordinator in the Biomedical Engineering Department at Washington University in St. Louis. She received her B.S. degree in electrical engineering from the University of Illinois, Urbana-Champaign, and her M.S. degree in biomedical engineering from Washington University in St. Louis. Prior to her current position, she worked as an instrumentation and controls engineer for Monsanto, Co.

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Abstract

Integration of a Computational Lab Sequence Into a Junior-Level Quantitative Physiology CourseQuantitative Physiology I is a junior-level course that requires students to integrate overfoundational coursework in physics, biology, electrical and mechanical engineering, computerscience, and technical writing. Students explore current and classic models of instrumentation,nerve, muscle, and biomechanics. Preceding 2004 the course was three credits consisted of alecture- or lab-format; each week featured either traditional lectures or hands-on dry or wetlaboratories. A consequence of the either-or structure was gap generation in lecture, leading tolack of substance and theme continuity, and a lack of thought or energy preceding or followinglabs. A consequence of that structure was a lack of continuity in substance and theme of thecourse.In 2005 we expanded the course to four units and added a computational lab sequence. Theselabs were designed with several goals: - theoretical and numerical exploration of core concepts introduced in lecture - substantive preparation for ideas underlying physical labs - overall investigation and appreciation of the dynamic nature of models, above and beyond what is possible with pencil and paper - integration across the course to discover mathematical and quantitative physiological concepts that spanned individual topics and modulesStudents completed computational labs in three to four hour sessions, using Simulink as adynamic programming platform and MATLAB to drive simulations and analyze outputs.Individual labs considered elementary physical systems; simple filters and feedback systems;retinal processing; resistor-circuit models of excitable tissue; Hodgkin-Huxley formalism ofaction potential generation; and isometric and isotonic force generation of a Hill-type musclemodel. Students submitted informal lab reports that summarize model output, subsequentanalyses, and text demonstrating their understanding of the simulation and its relevance to corecourse concepts.The final computational lab session was a practicum examination. Students were given a newphysiological problem and required to build a model, analyze the results, and report insightgained from modeling. An example practicum problem was the simulation of a stretch reflex,which required explicit integration over prior sessions.This paper demonstrates two outcomes achieved by our seven years of teaching thecomputational lab sequence: the ability of students to integrate concepts from across the course,as evidenced by their computational lab reports, and the ability to generalize beyond theircomputational training, as evidenced by computational lab practicum performance.

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Thoroughman, K. A., & Khan, R. P., & Sun, H., & Widder, P. L. (2012, June), Integration of a Computational Lab Sequence Into a Junior-level Quantitative Physiology Course Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21573

TY  - CPAPER
AB  -        Integration of a Computational Lab Sequence Into a Junior-Level                        Quantitative Physiology CourseQuantitative Physiology I is a junior-level course that requires students to integrate overfoundational coursework in physics, biology, electrical and mechanical engineering, computerscience, and technical writing. Students explore current and classic models of instrumentation,nerve, muscle, and biomechanics. Preceding 2004 the course was three credits consisted of alecture- or lab-format; each week featured either traditional lectures or hands-on dry or wetlaboratories. A consequence of the either-or structure was gap generation in lecture, leading tolack of substance and theme continuity, and a lack of thought or energy preceding or followinglabs. A consequence of that structure was a lack of continuity in substance and theme of thecourse.In 2005 we expanded the course to four units and added a computational lab sequence. Theselabs were designed with several goals:    - theoretical and numerical exploration of core concepts introduced in lecture    - substantive preparation for ideas underlying physical labs    - overall investigation and appreciation of the dynamic nature of models, above and        beyond what is possible with pencil and paper    - integration across the course to discover mathematical and quantitative physiological        concepts that spanned individual topics and modulesStudents completed computational labs in three to four hour sessions, using Simulink as adynamic programming platform and MATLAB to drive simulations and analyze outputs.Individual labs considered elementary physical systems; simple filters and feedback systems;retinal processing; resistor-circuit models of excitable tissue; Hodgkin-Huxley formalism ofaction potential generation; and isometric and isotonic force generation of a Hill-type musclemodel. Students submitted informal lab reports that summarize model output, subsequentanalyses, and text demonstrating their understanding of the simulation and its relevance to corecourse concepts.The final computational lab session was a practicum examination. Students were given a newphysiological problem and required to build a model, analyze the results, and report insightgained from modeling. An example practicum problem was the simulation of a stretch reflex,which required explicit integration over prior sessions.This paper demonstrates two outcomes achieved by our seven years of teaching thecomputational lab sequence: the ability of students to integrate concepts from across the course,as evidenced by their computational lab reports, and the ability to generalize beyond theircomputational training, as evidenced by computational lab practicum performance.
AU  - Kurt A. Thoroughman Ph.D.
AU  - Ranjan Patrick Khan
AU  - Haoxin Sun
AU  - Patricia L. Widder
CY  - San Antonio, Texas
DA  - 2012/06/10
PB  - ASEE Conferences
TI  - Integration of a Computational Lab Sequence Into a Junior-level Quantitative Physiology Course
UR  - https://peer.asee.org/21573
DO  - 10.18260/1-2--21573
ER  -