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Mechanix: An Intelligent Web Interface for Automatic Grading of Sketched Free-Body Diagrams

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

The ABCs of FBDs

Tagged Division

Mechanics

Page Count

19

Permanent URL

https://peer.asee.org/37497

Download Count

49

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Paper Authors

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Matthew Runyon Texas A&M University

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Matthew Runyon is a PhD student in the Department of Computer Science and Engineering at Texas A&M University. He received his bachelor's degree in Mechanical Engineering at Texas A&M University. He has been working with Dr. Hammond in the Sketch Recognition Lab with research focuses in artificial intelligence, human-computer interaction, and education.

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Vimal Viswanathan San Jose State University Orcid 16x16 orcid.org/0000-0002-2984-0025

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Dr. Vimal Viswanathan is an associate professor in the Mechanical Engineering Department at San Jose State University. His research interests include design innovation, creativity, design theory, and engineering education.

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Kimberly Grau Talley P.E. Texas State University Orcid 16x16 orcid.org/0000-0002-6235-0706

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Dr. Kimberly G. Talley is an associate professor in the Department of Engineering Technology, Bobcat Made Makerspace Director at Texas State University, and a licensed Professional Engineer. She received her Ph.D. and M.S.E. from the University of Texas at Austin in Structural Engineering. Her undergraduate degrees in History and in Construction Engineering and Management are from North Carolina State University. Dr. Talley teaches courses in the Construction Science and Management and Civil Engineering Technology Programs, and her research focus is in student engagement and retention in engineering and engineering technology education. Contact: talley@txstate.edu

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Tracy Anne Hammond Texas A&M University Orcid 16x16 orcid.org/0000-0001-7272-0507

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Dr. Hammond is Director of the Texas A&M University Institute for Engineering Education & Innovation and also the chair of the Engineering Education Faculty. She is also Director of the Sketch Recognition Lab and Professor in the Department of Computer Science & Engineering. She is a member of the Center for Population and Aging, the Center for Remote Health Technologies & Systems as well as the Institute for Data Science. Hammond is a PI for over 13 million in funded research, from NSF, DARPA, Google, Microsoft, and others. Hammond holds a Ph.D. in Computer Science and FTO (Finance Technology Option) from the Massachusetts Institute of Technology, and four degrees from Columbia University: an M.S in Anthropology, an M.S. in Computer Science, a B.A. in Mathematics, and a B.S. in Applied Mathematics and Physics. Hammond advised 17 UG theses, 29 MS theses, and 10 Ph.D. dissertations. Hammond is the 2020 recipient of the TEES Faculty Fellows Award and the 2011 recipient of the Charles H. Barclay, Jr. '45 Faculty Fellow Award. Hammond has been featured on the Discovery Channel and other news sources. Hammond is dedicated to diversity and equity, which is reflected in her publications, research, teaching, service, and mentoring. More at http://srl.tamu.edu and http://ieei.tamu.edu.

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Julie S. Linsey Georgia Institute of Technology

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Dr. Julie S. Linsey is an Associate Professor in the George W. Woodruff School of Mechanical Engineering at the Georgia Institute of Technological. Dr. Linsey received her Ph.D. in Mechanical Engineering at The University of Texas. Her research area is design cognition including systematic methods and tools for innovative design with a particular focus on concept generation and design-by-analogy. Her research seeks to understand designers’ cognitive processes with the goal of creating better tools and approaches to enhance engineering design. She has authored over 150 technical publications including over forty journal papers, and ten book chapters.

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Abstract

Early engineering courses at large universities enroll hundreds of students, and as of now are taught in an in person, hybrid, or online setting. These large classroom size, as well as impersonal nature of the current safe teaching methods, makes it difficult for instructors to provide meaningful feedback on assignments that students complete. This lack of feedback has an effect on the development of early engineering skills that students develop, including the free-body diagrams (FBD). Even before the shift in current instruction methods, there was a growing concern amongst engineering educators that student’s ability to idealize real world situations into these FBDs has been under-developed. Most existing homework methods do not provide students with impactful feedback on FBDs that may be draw during the solution of a problem, if any feedback is provided at all. Because of this concern, a Sketch-recognition based tutoring system called Mechanix has been developed by the Sketch Recognition Lab at Texas A&M specifically to provide real time tutoring in drawing Truss Systems. The application provides a drawing surface for students to hand-draw solutions as part of the submission process, as if they were submitting a problem through a pen-and-paper submission method. AI algorithms working in the background of the application identify the shape of the drawn FBD, the perceived internal definition of the shape, and any additional features added to the sketch. These algorithms then determine if the user inputs for the assignment are correct and will provide real time iterative feedback as to why a user’s input may be incorrect. Results of past usage of Mechanix has shown positive results in increasing engagement in struggling students, while also be as effective as other traditional homework methods for teaching statics and dynamics concepts. This paper focuses on the current effect of Mechanix on instruction across the 5 universities involved in the study in pre and post Covid-19 instruction methods. The study uses a range of instruments to gauge student understanding through concept inventories, select homework assignments, exam grades, and specialized problem sets given to students to determine the impact of the application when compared to traditional homework methods already used by the participating schools.

Runyon, M., & Viswanathan, V., & Talley, K. G., & Hammond, T. A., & Linsey, J. S. (2021, July), Mechanix: An Intelligent Web Interface for Automatic Grading of Sketched Free-Body Diagrams Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37497

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