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Research Papers

Using Hands-On Physical Computing Projects to Teach Computer Programming to Biomedical Engineering Students

[+] Author and Article Information
Trevor Ham

Department of Biomedical Engineering,
The University of Akron,
Akron, OH 44325
e-mail: trh56@zips.uakron.edu

S. Cyrus Rezvanifar

Department of Biomedical Engineering,
The University of Akron,
Akron, OH 44325
e-mail: sr106@zips.uakron.edu

Vineet S. Thomas

Department of Biomedical Engineering,
The University of Akron,
Akron, OH 44325
e-mail: vst2@zips.uakron.edu

Rouzbeh Amini

Mem. ASME Department of Biomedical
Engineering, The University of Akron,
Akron, OH 44325
e-mail: ramini@uakron.edu

1Corresponding author.

Manuscript received January 6, 2018; final manuscript received May 4, 2018; published online June 1, 2018. Assoc. Editor: Kristen Billiar.

J Biomech Eng 140(8), 081007 (Jun 01, 2018) (5 pages) Paper No: BIO-18-1008; doi: 10.1115/1.4040226 History: Received January 06, 2018; Revised May 04, 2018

Rapid advancements in the multidisciplinary field of biomedical engineering (BME) require competitive engineers with skill sets in a broad range of subjects including biology, physiology, mechanics, circuits, and programming. Accordingly, such a need should be reflected in the training of BME students. Among those skills, computer programming is an essential tool that is used in a wide variety of applications. In this paper, we have provided our experience in incorporating project-based learning, a promising approach in active learning, for teaching computer programming to BME students. We describe a low-cost method for using physical, hands-on computing that directly relates to BME. Additionally, we detail our efforts to teach multiple programming languages in one semester and provide a detailed analysis of the outcomes. We also provide basic materials for other instructors to adapt to fit their own needs.

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Figures

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Fig. 2

Requiring students to complete the project in a second programming language had no statistical effect as determined by Student's t-test. Data reported as mean ± standard deviation, n = 14 for each group.

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Fig. 3

Average project grades for both groups. The overall averages are similar, while the standard deviation of the AIDE group is higher (10.1 versus 7.4). Data reported as mean ± standard deviation, n = 5 for each category.

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Fig. 1

The HRM consisting of an Arduino UNO with a PPG sensor and an light-emitting diode

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