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Improved Learning Through Collaborative, Scenario-based Quizzes in an Undergraduate Control Theory Course

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Conference

2017 ASEE Annual Conference & Exposition

Location

Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Electrical and Computer Division Technical Session 6

Tagged Division

Electrical and Computer

Tagged Topic

Diversity

Page Count

26

Permanent URL

https://peer.asee.org/28485

Download Count

205

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Paper Authors

biography

Meeko Oishi University of New Mexico

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Meeko Oishi received the Ph.D. (2004) and M.S. (2000) in Mechanical Engineering from Stanford University (Ph.D. minor, Electrical Engineering), and a B.S.E. in Mechanical Engineering from Princeton University (1998). She is an Associate Professor of Electrical and Computer Engineering at the University of New Mexico. Her research interests include nonlinear dynamical systems, hybrid control theory, control of human-in-the-loop systems, reachability analysis, and modeling of motor performance and control in Parkinson's disease. She previously held a faculty position at the University of British Columbia at Vancouver, and postdoctoral positions at Sandia National Laboratories and at the National Ecological Observatory Network. She is the recipient of the UNM Regents’ Lectureship, the NSF CAREER Award, the UNM Teaching Fellowship, the Peter Wall Institute Early Career Scholar Award, the Truman Postdoctoral Fellowship in National Security Science and Engineering, and the George Bienkowski Memorial Prize, Princeton University. She was a Summer Faculty Fellow at AFRL Space Vehicles Directorate, and a Science and Technology Policy Fellow at The National Academies.

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Vanessa Svihla University of New Mexico Orcid 16x16 orcid.org/0000-0003-4342-6178

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Dr. Vanessa Svihla is a learning scientist and assistant professor at the University of New Mexico in the Organization, Information & Learning Sciences program, and in the Chemical & Biological Engineering Department. She served as Co-PI on an NSF RET Grant and a USDA NIFA grant, and is currently co-PI on three NSF-funded projects in engineering and computer science education, including a Revolutionizing Engineering Departments project. She was selected as a National Academy of Education / Spencer Postdoctoral Fellow. Dr. Svihla studies learning in authentic, real world conditions; this includes a two-strand research program focused on (1) authentic assessment, often aided by interactive technology, and (2) design learning, in which she studies engineers designing devices, scientists designing investigations, teachers designing learning experiences and students designing to learn.

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Victor Law University of New Mexico

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Dr. Victor Law is an Assistant Professor at the University of New Mexico in the Program of Organization, Information, and Leaning Sciences. He received his PhD in Educational Psychology from the University of Oklahoma. His research explores the social aspects of self-regulation in collaborative learning environments. In addition, he has been conducting studies examining the effects of different scaffolding approaches, including massively multiplayer online games, computer-based simulation, and dynamic modeling, on students’ complex problem-solving learning outcomes. Dr. Law has published empirical studies in national and international refereed journals such as Computers in Human Behaviors, Journal of Educational Computing Research, Journal of Educational Technology & Society, Technology, Instruction, Cognition, and Learning, and International Journal of Knowledge Management and E-Learning.

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Abstract

A significant challenge for many students in introductory control theory courses is the abstract mathematical concepts, as well as application of those concepts to engineering problems. When students are overwhelmed with the material, they often aim for rote application of mathematical formulas, without attempting higher-level critical thinking (e.g., evaluation, comparison, design) (\cite{RN10304} Mason, Shuman, and Cook, IEEE Trans. Education, (56)4:430-435, 2013). We constructed a series of five in-class scenario-based exercises, implemented in lieu of standard lectures, to facilitate higher levels of understanding. Students worked in teams of three to solve multiple-choice and short-answer problems designed around a specific scenario. For example, one scenario involved analysis of transient properties (e.g., settling time, percent overshoot) of a teleoperation system for robotic surgery, and prompted students to weigh trade-offs between responsiveness and excessive motion in the end effector. Other scenarios related to problems about sprung floors for gymnasts and active suspension control for automobiles. In order to assess the value of collaboration, we contrast student gains on one scenario-based quiz completed individually to those completed collaboratively. We deliberately selected this particular quiz because it allowed for comparison of growth trajectories; specifically, we evaluated pre-test performance using a validated concept inventory (\cite{RN10197} Bristow et al., IEEE Trans. Education, (55)2:203-212, 2012). We compared growth on items on which students scored very low and low. We evaluated the effect of the collaborative exercise through a pre- and post- test, based on the same control systems concept inventory, and evaluated the change in performance for concepts covered through the collaborative exercises as opposed to the non-collaborative exercise. We believe that the collaborative exercises have potential to improve understanding of math-intensive engineering concepts, as well as further develop students’ ability to apply these concepts to actual engineering analysis and design problems.

Oishi, M., & Svihla, V., & Law, V. (2017, June), Improved Learning Through Collaborative, Scenario-based Quizzes in an Undergraduate Control Theory Course Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. https://peer.asee.org/28485

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