Sketching Instruction in Engineering Design with an Intelligent Tutoring Software

Hillary E. Merzdorf; Donna Jaison; Tracy Anne Hammond; Vimal Kumar Viswanathan

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Conference: 2024 ASEE Annual Conference & Exposition
Location: Portland, Oregon
Publication Date: June 23, 2024
Start Date: June 23, 2024
End Date: June 26, 2024
Conference Session: Engineering Design Graphics Division (EDGD) Technical Session 2
Tagged Division: Engineering Design Graphics Division (EDGD)
Page Count: 14
DOI: 10.18260/1-2--47971
Permanent URL: https://peer.asee.org/47971
Download Count: 119

Paper Authors

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Hillary E. Merzdorf Texas A&M University

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Hillary E. Merzdorf is a postdoctoral researcher with the Institute for Engineering Education and Innovation at Texas A&M University. Her research interests are in educational technology, spatial reasoning in engineering, and educational assessment.

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Donna Jaison Texas A&M University

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Donna Jaison is a PhD student under Dr. Karan Watson in the Multidisciplinary Engineering Department at Texas A&M College Station. She is a Graduate research assistant at the Institute of Engineering Education and Innovation(IEEI) at Texas A&M University

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Tracy Anne Hammond Texas A&M University 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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Vimal Kumar Viswanathan San Jose State University 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, additive manufacturing, and engineering education. He joined San Jose State University in 2016.

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Abstract

Engineers who learn to sketch develop many essential skills, such as spatial visualization, design idea representation and fluency, and communication. However, most engineering programs focus on digital design tools and no longer teach freehand sketching. In addition, large engineering classrooms make it challenging for instructors to provide personalized teaching and immediate feedback on sketching assignments. We developed an intelligent tutoring system for teaching 2D and 3D sketching fundamentals in perspective. The system has been deployed at three universities for 4 years in undergraduate and graduate mechanical engineering and design graphics courses. It has also been used by undergraduate instructors outside of engineering. While our research has demonstrated the impact of classroom instruction with the software on student learning, self-efficacy, sketching skills, and design ideation, there has been little research into instructors’ experiences teaching freehand sketching with the system.

This study evaluates how instructors implemented an intelligent tutoring system for sketching in their classrooms. We hypothesize that course level, subject area, and class size can be sources of variety in instructors’ teaching approaches. Our research is guided by the following question: In what ways do engineering and design instructors teach freehand perspective sketching with an intelligent tutoring software? This study follows a qualitative research methodology that included interviews to capture details on instructors’ use of the intelligent sketching tutoring software. Three instructors who implemented the system in their mechanical engineering and design visualization courses at both undergraduate and graduate levels, one instructor of first-year engineering, and one industrial design instructor were purposefully recruited. Three instructors taught with the software for multiple semesters. We follow a semi-structured interview protocol asking how instructors introduce the software into their course, how student work with the software is incorporated into the course learning objectives, what benefits instructors saw with using the software to their own instruction and to students’ learning, and what difficulties and areas for improvement they identified at the end of each semester. Thematic analysis was performed on the interview responses by two researchers using a qualitative data analysis tool namely MAXQDA.

Our results will examine each instructor’s practices in detail. We will report the degree to which the intelligent tutoring software was integrated with lessons and assignments and the ways that instructors scaffold student use of the software. We will also identify key design strengths and weaknesses of the system which helped or hindered its use. We will discuss instructors’ opinions on the software’s support in reaching each course’s learning objectives and compare experiences by level (undergraduate or graduate), subject area (engineering, design, or integrated), and class size. These findings will pinpoint future design features and functions for the software, and generate wider recommended best practices for sketching instruction in engineering and design courses when using intelligent systems.

Citation
Format

Merzdorf, H. E., & Jaison, D., & Hammond, T. A., & Viswanathan, V. K. (2024, June), Sketching Instruction in Engineering Design with an Intelligent Tutoring Software Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. 10.18260/1-2--47971

TY - CPAPER
AB - Engineers who learn to sketch develop many essential skills, such as spatial visualization, design idea representation and fluency, and communication. However, most engineering programs focus on digital design tools and no longer teach freehand sketching. In addition, large engineering classrooms make it challenging for instructors to provide personalized teaching and immediate feedback on sketching assignments. We developed an intelligent tutoring system for teaching 2D and 3D sketching fundamentals in perspective. The system has been deployed at three universities for 4 years in undergraduate and graduate mechanical engineering and design graphics courses. It has also been used by undergraduate instructors outside of engineering. While our research has demonstrated the impact of classroom instruction with the software on student learning, self-efficacy, sketching skills, and design ideation, there has been little research into instructors’ experiences teaching freehand sketching with the system.

This study evaluates how instructors implemented an intelligent tutoring system for sketching in their classrooms. We hypothesize that course level, subject area, and class size can be sources of variety in instructors’ teaching approaches. Our research is guided by the following question: In what ways do engineering and design instructors teach freehand perspective sketching with an intelligent tutoring software?
This study follows a qualitative research methodology that included interviews to capture details on instructors’ use of the intelligent sketching tutoring software. Three instructors who implemented the system in their mechanical engineering and design visualization courses at both undergraduate and graduate levels, one instructor of first-year engineering, and one industrial design instructor were purposefully recruited. Three instructors taught with the software for multiple semesters. We follow a semi-structured interview protocol asking how instructors introduce the software into their course, how student work with the software is incorporated into the course learning objectives, what benefits instructors saw with using the software to their own instruction and to students’ learning, and what difficulties and areas for improvement they identified at the end of each semester. Thematic analysis was performed on the interview responses by two researchers using a qualitative data analysis tool namely MAXQDA.

Our results will examine each instructor’s practices in detail. We will report the degree to which the intelligent tutoring software was integrated with lessons and assignments and the ways that instructors scaffold student use of the software. We will also identify key design strengths and weaknesses of the system which helped or hindered its use. We will discuss instructors’ opinions on the software’s support in reaching each course’s learning objectives and compare experiences by level (undergraduate or graduate), subject area (engineering, design, or integrated), and class size. These findings will pinpoint future design features and functions for the software, and generate wider recommended best practices for sketching instruction in engineering and design courses when using intelligent systems.
AU - Hillary E. Merzdorf
AU - Donna Jaison
AU - Tracy Anne Hammond
AU - Vimal Kumar Viswanathan
CY - Portland, Oregon
DA - 2024/06/23
PB - ASEE Conferences
TI - Sketching Instruction in Engineering Design with an Intelligent Tutoring Software
UR - https://peer.asee.org/47971
DO - 10.18260/1-2--47971
ER -