Abstract

Computer simulations play an important role in a range of biomedical engineering applications. Thus, it is important that biomedical engineering students engage with modeling in their undergraduate education and establish an understanding of its practice. In addition, computational tools enhance active learning and complement standard pedagogical approaches to promote student understanding of course content. Herein, we describe the development and implementation of learning modules for computational modeling and simulation (CM&S) within an undergraduate biomechanics course. We developed four CM&S learning modules that targeted predefined course goals and learning outcomes within the febio studio software. For each module, students were guided through CM&S tutorials and tasked to construct and analyze more advanced models to assess learning and competency and evaluate module effectiveness. Results showed that students demonstrated an increased interest in CM&S through module progression and that modules promoted the understanding of course content. In addition, students exhibited increased understanding and competency in finite element model development and simulation software use. Lastly, it was evident that students recognized the importance of coupling theory, experiments, and modeling and understood the importance of CM&S in biomedical engineering and its broad application. Our findings suggest that integrating well-designed CM&S modules into undergraduate biomedical engineering education holds much promise in supporting student learning experiences and introducing students to modern engineering tools relevant to professional development.

References

1.
Taylor
,
C. A.
, and
Figueroa
,
C. A.
,
2009
, “
Patient-Specific Modeling of Cardiovascular Mechanics
,”
Annu. Rev. Biomed. Eng.
,
11
(
1
), pp.
109
134
.10.1146/annurev.bioeng.10.061807.160521
2.
Blemker
,
S. S.
,
Asakawa
,
D. S.
,
Gold
,
G. E.
, and
Delp
,
S. L.
,
2007
, “
Image-Based Musculoskeletal Modeling: Applications, Advances, and Future Opportunities
,”
J. Magn. Reson. Imaging
,
25
(
2
), pp.
441
451
.10.1002/jmri.20805
3.
Sigal
,
I. A.
,
Flanagan
,
J. G.
, and
Ethier
,
C. R.
,
2005
, “
Factors Influencing Optic Nerve Head Biomechanics
,”
Invest. Ophthalmol. Visual Sci.
,
46
(
11
), pp.
4189
4199
.10.1167/iovs.05-0541
4.
Wenk
,
J. F.
,
Zhang
,
Z.
,
Cheng
,
G.
,
Malhotra
,
D.
,
Acevedo-Bolton
,
G.
,
Burger
,
M.
,
Suzuki
,
T.
, et al.,
2010
, “
First Finite Element Model of the Left Ventricle With Mitral Valve: Insights Into Ischemic Mitral Regurgitation
,”
Ann. Thorac. Surg.
,
89
(
5
), pp.
1546
1553
.10.1016/j.athoracsur.2010.02.036
5.
Sander
,
E. A.
,
Stylianopoulos
,
T.
,
Tranquillo
,
R. T.
, and
Barocas
,
V. H.
,
2009
, “
Image-Based Multiscale Modeling Predicts Tissue-Level and Network-Level Fiber Reorganization in Stretched Cell-Compacted Collagen Gels
,”
Proc. Natl. Acad. Sci. USA
,
106
(
42
), pp.
17675
17680
.10.1073/pnas.0903716106
6.
Shirazi
,
R.
, and
Shirazi-Adl
,
A.
,
2009
, “
Computational Biomechanics of Articular Cartilage of Human Knee Joint: Effect of Osteochondral Defects
,”
J. Biomech.
,
42
(
15
), pp.
2458
2465
.10.1016/j.jbiomech.2009.07.022
7.
Giudice
,
J. S.
,
Zeng
,
W.
,
Wu
,
T.
,
Alshareef
,
A.
,
Shedd
,
D. F.
, and
Panzer
,
M. B.
,
2019
, “
An Analytical Review of the Numerical Methods Used for Finite Element Modeling of Traumatic Brain Injury
,”
Ann. Biomed. Eng.
,
47
(
9
), pp.
1855
1872
.10.1007/s10439-018-02161-5
8.
Douglas
,
P. S.
,
Pontone
,
G.
,
Hlatky
,
M. A.
,
Patel
,
M. R.
,
Norgaard
,
B. L.
,
Byrne
,
R. A.
,
Curzen
,
N.
, et al.,
2015
, “
Clinical Outcomes of Fractional Flow Reserve by Computed Tomographic Angiography-Guided Diagnostic Strategies vs. usual Care in Patients With Suspected Coronary Artery Disease: The Prospective Longitudinal Trial of FFR(CT): Outcome and Resource Impacts Study
,”
Eur. Heart J.
,
36
(
47
), pp.
3359
3367
.10.1093/eurheartj/ehv444
9.
Trusty
,
P. M.
,
Wei
,
Z. A.
,
Slesnick
,
T. C.
,
Kanter
,
K. R.
,
Spray
,
T. L.
,
Fogel
,
M. A.
, and
Yoganathan
,
A. P.
,
2019
, “
The First Cohort of Prospective Fontan Surgical Planning Patients With Follow-Up Data: How Accurate is Surgical Planning?
,”
J. Thorac. Cardiovasc. Surg.
,
157
(
3
), pp.
1146
1155
.10.1016/j.jtcvs.2018.11.102
10.
20123
, " “
Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submissions
,”
Guidance for Industry and Food and Drug Administration Staff, U.S. Food and Drug Administration
,
Spring, MD
.
11.
Magana
,
A. J.
, and
de Jong
,
T.
,
2018
, “
Modeling and Simulation Practices in Engineering Education
,”
Comput. Appl. Eng. Educ.
,
26
(
4
), pp.
731
738
.10.1002/cae.21980
12.
Penner
,
D. E.
,
2002
, “
Chapter 1: Cognition, Computers, and Synthetic Science: Building Knowledge and Meaning Through Modeling
,”
Rev. Res. Educ.
,
25
(
1
), pp.
1
35
.10.3102/0091732X025001001
13.
Magana
,
A. J.
, and
Silva Coutinho
,
G.
,
2017
, “
Modeling and Simulation Practices for a Computational Thinking-Enabled Engineering Workforce
,”
Comput. Appl. Eng. Educ.
,
25
(
1
), pp.
62
78
.10.1002/cae.21779
14.
Harris
,
T. R.
,
Bransford
,
J. D.
, and
Brophy
,
S. P.
,
2002
, “
Roles for Learning Sciences and Learning Technologies in Biomedical Engineering Education: A Review of Recent Advances
,”
Annu. Rev. Biomed. Eng.
,
4
(
1
), pp.
29
48
.10.1146/annurev.bioeng.4.091701.125502
15.
Magana
,
A. J.
,
Falk
,
M. L.
, and
Reese
,
M. J.
,
2013
, “
Introducing Discipline-Based Computing in Undergraduate Engineering Education
,”
ACM Trans. Comput. Educ.
,
13
(
4
), pp.
1
22
.10.1145/2534971
16.
Chinn
,
C. A.
, and
Samarapungavan
,
A.
,
2008
, “
Learning to Use Scientific Models: Multiple Dimensions of Conceptual Change
,”
Teaching Scientific Inquiry
,
Brill
,
Leiden, The Netherlands
.
17.
Schwarz
,
C. V.
,
Reiser
,
B. J.
,
Davis
,
E. A.
,
Kenyon
,
L.
,
Achér
,
A.
,
Fortus
,
D.
,
Shwartz
,
Y.
,
Hug
,
B.
, and
Krajcik
,
J.
,
2009
, “
Developing a Learning Progression for Scientific Modeling: Making Scientific Modeling Accessible and Meaningful for Learners
,”
J. Res. Sci. Teaching
,
46
(
6
), pp.
632
654
.10.1002/tea.20311
18.
Yaşar
,
O.
,
Little
,
L.
,
Tuzun
,
R.
,
Rajasethupathy
,
K.
,
Maliekal
,
J.
, and
Tahar
,
M.
,
2006
, “
Computational Math, Science, and Technology (CMST): a Strategy to Improve STEM Workforce and Pedagogy to Improve Math and Science Education
,”
Proceedings of the 6th International Conference on Computational Science - Volume Part II
, Reading, UK, May 28–31, pp.
169
176
.10.1007/11758525_23
19.
Goergen
,
C. J.
,
Shadden
,
S. C.
, and
Marsden
,
A. L.
,
2017
, “
SimVascular as an Instructional Tool in the Classroom
,”
2017 IEEE Frontiers in Education Conference
, Indianapolis, IN, Oct. 18–21, pp.
1
4
.10.1109/FIE.2017.8190438
20.
Boster
,
K. A. S.
,
Dong
,
M.
,
Oakes
,
J. M.
,
Bellini
,
C.
,
Rayz
,
V. L.
,
LaDisa
,
J. F.
,
Parker
,
D.
, et al.,
2020
, “
Integrated Image-Based Computational Fluid Dynamics Modeling Software as an Instructional Tool
,”
ASME J. Biomech. Eng.
,
142
(
11
), p.
111008
.10.1115/1.4047479
21.
Clyne
,
A. M.
, and
Billiar
,
K. L.
,
2016
, “
Problem-Based Learning in Biomechanics: Advantages, Challenges, and Implementation Strategies
,”
ASME J. Biomech. Eng.
,
138
(
7
), p.
070804
.10.1115/1.4033671
22.
Jia
,
M. S.
,
Rao
,
R. R.
, and
Elsaadany
,
M.
,
2023
, “
Early Introduction of 3D Modeling Modules Promotes the Development of Simulation Skills in Downstream Biomedical Engineering Curricula
,”
J. Biol. Eng.
,
17
(
1
), p.
26
.10.1186/s13036-023-00339-7
23.
Linsenmeier
,
R. A.
, and
Saterbak
,
A.
,
2020
, “
Fifty Years of Biomedical Engineering Undergraduate Education
,”
Ann. Biomed. Eng.
,
48
(
6
), pp.
1590
1615
.10.1007/s10439-020-02494-0
24.
Ochia
,
R.
,
2021
, “
A Hybrid Teaching Method for Undergraduate Biomechanics Lab
,”
Biomed. Eng. Education
,
1
(
1
), pp.
187
193
.10.1007/s43683-020-00033-w
25.
Flynn, D., Palmer, M., Schiestl, R., Levine, S., and Meader, T., 2023, “
Landscape Report & Industry Survey on the Use of Computational Modeling & Simulation in Medical Device Development
,” Medical Device Innovation Consortium,
Arlington
,
VA
, accessed Aug. 29, 2023, https://mdic.org/cmslandscapereportrelease/
26.
Maas
,
S. A.
,
Ellis
,
B. J.
,
Ateshian
,
G. A.
, and
Weiss
,
J. A.
,
2012
, “
FEBio: Finite Elements for Biomechanics
,”
ASME J. Biomech. Eng.
,
134
(
1
), p.
011005
.10.1115/1.4005694
27.
Maas
,
S. A.
,
Ateshian
,
G. A.
, and
Weiss
,
J. A.
,
2017
, “
FEBio: History and Advances
,”
Annu. Rev. Biomed. Eng.
,
19
(
1
), pp.
279
299
.10.1146/annurev-bioeng-071516-044738
28.
Spencer
,
A. J.
,
2004
,
Continuum Mechanics (Dover Books on Physics)
,
Dover Publications
,
Mineola, NY
.
29.
Humphrey
,
J. D.
, and
O'Rourke
,
S. L.
,
2015
, “
An Introduction to Biomechanics Solids and Fluids, Analysis and Design
,”
Springer, Berlin, Germany
.
30.
Wiggins
,
G.
, and
McTighe
,
J.
,
2005
, “
Understanding by Design 2nd Expanded Edition
,”
Association for Supervision and Curriculum Development
,
Alexandria, VA
.
31.
Krathwowl
,
D. R.
,
2010
, “
A Revision of Bloom's Taxonomy: An Overview
,”
Theory Pract.
,
41
(
4
), pp.
212
218
.10.1207/s15430421tip4104_2
32.
Magana
,
A. J.
,
Brophy
,
S. P.
, and
Bodner
,
G. M.
,
2012
, “
Instructors' Intended Learning Outcomes for Using Computational Simulations as Learning Tools
,”
J. Eng. Educ.
,
101
(
2
), pp.
220
243
.10.1002/j.2168-9830.2012.tb00049.x
33.
K N
,
C.
,
Zuber
,
M.
,
Bhat N
,
S.
,
Shenoy B
,
S.
, and
R Kini
,
C.
,
2019
, “
Static Structural Analysis of Different Stem Designs Used in Total Hip Arthroplasty Using Finite Element Method
,”
Heliyon
,
5
(
6
), p.
e01767
.10.1016/j.heliyon.2019.e01767
34.
Higbee
,
S.
, and
Miller
,
S.
,
2021
, “
Finite Element Analysis as an Iterative Design Tool for Students in an Introductory Biomechanics Course
,”
ASME J. Biomech. Eng.
,
143
(
12
), p.
121005
.10.1115/1.4051659
35.
ABET
, 2021, “
Criteria for Accrediting Engineering Programs, 2022 – 2023
,” ABET, Baltimore, MD, accessed Sept. 16, 2023, https://www.abet.org/accreditation/accreditation-criteria/criteria-for-accrediting-engineering-programs-2022-2023/
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