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In-code Comments as a Self-explanation Strategy for Computational Science Education

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Conference

2016 ASEE Annual Conference & Exposition

Location

New Orleans, Louisiana

Publication Date

June 26, 2016

Start Date

June 26, 2016

End Date

August 28, 2016

ISBN

978-0-692-68565-5

ISSN

2153-5965

Conference Session

Classroom Practice II: Technology - and Game-Based Learning

Tagged Division

Educational Research and Methods

Page Count

15

DOI

10.18260/p.25642

Permanent URL

https://strategy.asee.org/25642

Download Count

184

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Paper Authors

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Camilo Vieira Purdue University Orcid 16x16 orcid.org/0000-0001-8720-0002

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PhD Candidate at Purdue University
Master of Engineering in Educational Technologies - Eafit University
Systems Engineer - Eafit University

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Anindya Roy Johns Hopkins University

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Alejandra J. Magana Purdue University, West Lafayette Orcid 16x16 orcid.org/0000-0001-6117-7502

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Alejandra Magana is an Associate Professor in the Department of Computer and Information Technology and an affiliated faculty at the School of Engineering Education at Purdue University. She holds a B.E. in Information Systems, a M.S. in Technology, both from Tec de Monterrey; and a M.S. in Educational Technology and a Ph.D. in Engineering Education from Purdue University. Her research is focused on identifying how model-based cognition in STEM can be better supported by means of expert technological and computing tools such as cyber-physical systems,visualizations and modeling and simulation tools.

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Michael L. Falk Johns Hopkins University Orcid 16x16 orcid.org/0000-0002-8383-4259

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Michael Falk is a Professor in the Department of Materials Science and Engineering at Johns Hopkins University's Whiting School of Engineering where he has served on the faculty since 2008 with secondary appointments in Mechanical Engineering and in Physics and Astronomy. He holds a B.A. in Physics (1990) and a M.S.E. in Computer Science (1991) from Johns Hopkins University and a Ph.D. in Physics (1998) from the University of California, Santa Barbara. He has been twice selected as a visiting Chaire Joliot at the École Supérieure de Physique et de Chimie Industrielles at Paris Tech and has organized extended workshops on the physics of glasses and on friction, fracture and earthquakes at the Kavli Institute for Theoretical Physics. He has received several awards for his educational accomplishments, and in 2011 he received an award from the university's Diversity Leadership Council for his work on LGBT inclusion. His education research focuses on integrating computation into the undergraduate core curriculum. Falk also serves as the lead investigator for STEM Achievement in Baltimore Elementary Schools (SABES) an NSF funded Community Enterprise for STEM Learning partnership between JHU and Baltimore City Schools.

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Michael J. Reese Jr. Johns Hopkins University

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Michael Reese is the Associate Director at the Johns Hopkins Center for Educational Resources. Reese previously worked as an Educational Technologist at Caliber Learning and Booz-Allen and Hamilton. He also consulted with the University of Maryland School of Nursing on the launch of their distance education program. He earned a Ph.D. in sociology from the Johns Hopkins University and an M.Ed. in educational technology from the University of Virginia. He graduated with a B.S. in electrical engineering at Virginia Tech, where he was named the Paul E. Torgersen Leadership Scholar.

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Abstract

Computational science and engineering is an important field that integrates computational tools and methods, and disciplinary sciences and engineering to solve complex problems. However, several research studies and national agencies report that engineering students are not well prepared to use or create these tools in the context of their discipline. Furthermore, some of the skills within computational science and engineering (e.g., programming) can be difficult to learn. This study explores potential pedagogical strategies for the implementation of worked-examples in the context of computational science and engineering. Students’ self-explanations of a worked-example are collected as in-code comments, and analyzed to identify effective self-explanation strategies. The results from this study suggest that students’ in-code comments: (1) can be used to elicit self-explanations and engage students in exploring the worked-example; and (2) show differences that can be used to identify the self-explanation effect.

Vieira, C., & Roy, A., & Magana, A. J., & Falk, M. L., & Reese, M. J. (2016, June), In-code Comments as a Self-explanation Strategy for Computational Science Education Paper presented at 2016 ASEE Annual Conference & Exposition, New Orleans, Louisiana. 10.18260/p.25642

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