Baltimore , Maryland
June 25, 2023
June 25, 2023
June 28, 2023
Mechanical Engineering Division (MECH) Technical Session 15: Automation and Machine Learning
Mechanical Engineering Division (MECH)
8
10.18260/1-2--44289
https://peer.asee.org/44289
196
Juliana Danesi Ruiz is currently on her fourth semester as a Ph.D. student at The University of Iowa. She graduated Fall 2020 at the University of Iowa with a BS in mechanical engineering degree, computer science, and mathematics minor. Her work has been focused on Engineering Education and Robotics, researching how to improve students experience in robotics class. She is advised by Professor Rachel Vitali and Professor Phillip Deierling.
Dr. Rachel Vitali is an Assistant Professor in the Mechanical Engineering Department at the University of Iowa. Prior to her appointment, she was a NASA-funded TRISH postdoctoral fellow in the Industrial & Operations Engineering Department at the University of Michigan, where she also received her B.S.E. in 2015, M.S.E in 2017, and Ph.D. in 2019 from the Mechanical Engineering Department. As director of the Human Instrumentation and Robotics (HIR) lab, she leads multiple lines of research in engineering dynamics with applications to wearable technology for analysis of human motion in a variety of contexts ranging from warfighters to astronauts. In addition to her engineering work, she also has an interest in engineering education research. As a doctoral student, she led a project aimed at improving the undergraduate educational experience by systematically incorporating sensor technology into the curriculum as an engaged learning activity, for which she was awarded an ASME Graduate Teacher Fellowship.
Dr. Deierling is an Associate Professor of Instruction at the University of Iowa. He holds BS, MS, and Ph.D degrees all from the University of Iowa. Prior to joining the faculty, he was a postdoctoral research associate with the Air Force Research Laboratory through the National Research Council and a design and analysis engineer in the commercial vehicle industry. His research and teaching interests are in advanced manufacturing, industry 4.0, machine learning & vision, and autonomous robotics.
This work-in-progress paper aims to document the impacts of active learning in the form of hands-on exploration on students’ conceptual understanding of fundamental robotics dynamics concepts and motivation to learn the material. Active learning is well-known to have noteworthy and largely positive impacts on cognitive gains of undergraduate students in engineering (as well as many other disciplines). Freeman et al. (2014) described the power of active learn- ing as, ”If the experiments analyzed here had been conducted as randomized controlled trials of medical interventions, they may have been stopped for ben- efit—meaning that enrolling patients in the control condition might be discon- tinued because the treatment being tested was clearly more beneficial.” Much of engineering practice relies on visual cues, meaning strong spatial perception, reasoning, and visualization skills are often key to success in many engineering careers. These skills are especially true for engineering practice related to the design and operation of robotic automated manufacturing systems. Thus, it is crucial for students to have hands-on (active learning) experiences to fully grasp and appreciate the complexities of operating and controlling a three-dimensional robotic arms that might have multiple degree of freedom.
This active learning activity will be introduce into a Mechanical Engineering technical elective course called ME:4140 - Modern Robotics and Automation, which is offered every spring semester at the University of Iowa. The class introduces students to the basics of robotics and automation principles, the most relevant of which to this work are the topics covering robotic motion and kinematics. Bi-weekly hands-on laboratories will covered spatially relevant top- ics like rotation matrices, forward/inverse kinematics, rigid body motions, and other concepts through in person laboratories and using robot simulators. The timing of these laboratories is such that the material covered in the laboratories is related to the upcoming assignment due. For assessment, surveys will be distributed at the beginning and end of each laboratory that consists of Likert- scale questions probing student motivation as well as self-reported homework assignment scores to capture their level of conceptual understanding.
This paper will first describe the hands-on laboratories that will be designed and implemented in ME:4140 during the Spring 2023 semester. Preliminary results for student motivation and student conceptual understanding will also be included. Implications for this work include insights into how student motivation and learning are positively influenced by hands-on exploration. Future work includes an investigation into the transferability of results to other robotics contexts so instructors can implement similar activities in their courses.
Danesi Ruiz, J., & Vitali, R., & Deierling, P. (2023, June), Work in Progress: Integrating Hands-on Exploration into an Undergraduate Robotics and Automation Class Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--44289
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