A Path to Computational Thinking and Computer Programming through Physics Problems
- Conference
- Location
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Minneapolis, MN
- Publication Date
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August 23, 2022
- Start Date
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June 26, 2022
- End Date
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June 29, 2022
- Conference Session
- Page Count
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10
- DOI
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10.18260/1-2--41304
- Permanent URL
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https://peer.asee.org/41304
- Download Count
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311
Paper Authors
Robert Mason Southern Illinois University Carbondale
I graduated with a B.S. in physics (1995) and an M.S. in physics (1997) from Southern Illinois University, Edwardsville. I taught as an adjunct instructor at various schools around Edwardsville for the 1997-1998 academic year before accepting a full-time position at Moberly Area Community College in Moberly, MO. After a year in Missouri I took my current position at Olney Central College in Olney, IL (1999). Over the last 23 years I have taught a wide range of courses; mathematics, physics, physical science, and pre-engineering. I found the pre-engineering courses to be especially rewarding due to the diversity and the rigor of the material. In 2016 I completed an M.A. in math education at Eastern Illinois University because of a keen interest in math as well as the need for a program that was flexible enough to accommodate my teaching schedule. This was a good decision as the focus on pedagogy was invaluable in the classroom, even with my experience. around that same time I became aware of a group called Partnership for Integration of Computation into Undergraduate Physics (PICUP) and started attending workshops. My experiences motivated me to pursue my doctorate in applied physics at Southern Illinois University, Carbondale, beginning in 2020. It is my work at SIUC that has introduced me to the ASEE.
Hansika Sirikumara Marian University
Hansika Sirikumara, Ph.D., is an Assistant professor of Physics and Engineering at E. S. Witchger School of Engineering, Marian University Indianapolis. She completed her MS and PhD degrees from Southern Illinois University Carbondale. Her research expertise/interests are in engineering material properties for semiconductor device applications using computational methods.
Abstract
We propose a novel approach to improve Computational Thinking (CT) and Computer Programming (CP) for upper-level undergraduate or entry graduate students majoring in Science, Technology, Engineering, and Mathematics (STEM)-related fields other than Computer Science. The proliferation of computer science into these fields is bringing radical changes in the skill requirement of the future work force. The emergence of such computation heavy interdisciplinary areas in STEM fields is increasingly becoming a driving force for the demand of STEM graduates with some knowledge in CT and CP. It is critical that students majoring in STEM subjects other than Computer Science (CS) should have easily accessible resources to develop CT and CP.
Students majoring in non-CS fields are more familiar with problem-based learning, which for them may be a more comfortable and enjoyable approach for learning CT and CP, whereas traditional computer programming lessons are mostly syntax-based learning. Therefore, integrating computing into the major classes is considered highly beneficial. However, there are not enough teaching materials readily available to do this task.
We present a set of model exercises that can be used for a problem-solving based approach for teaching CT and CP for non-CS STEM students. The lessons are aimed at upper-level (junior /senior) undergraduate or entry-level graduate students majoring in STEM fields.
Two primary goals of this lesson are to introduce numerical techniques for solving ordinary differential equations (ODEs) and to teach how to implement a numerical algorithm in a computer code.
The lesson is centered around numerically solving nonlinear simple pendulum which is typically solved in introductory level physical classes using small angle approximation. We will start our lesson with numerical solutions to first order ODEs, coupled first order ODEs and then we move to second order ODEs. We discuss three numerical techniques for solving ODEs and choose to implement these algorithms in Python. Every step of this lesson is taught using a problem-solving approach.
The knowledge is built upon in three steps with three exercises. Support programs are provided to learn all coding elements required for completing these exercises. This lesson can be used for self-learning or as instructional materials.
Mason, R., & Sirikumara, H. (2022, August), A Path to Computational Thinking and Computer Programming through Physics Problems Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41304
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