Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
NSF Grantees Poster Session
7
10.18260/1-2--29958
https://peer.asee.org/29958
451
Rachel Vitali is a doctoral candidate in the Mechanical Engineering department at the University of Michigan, where she also received her B.S.E. in 2015 and M.S.E. in 2017. Her research interests include computational and analytical dynamics with applications to wearable sensing technology for analysis of human motion in addition to incorporating technology into undergraduate courses for engaged learning.
Noel Perkins is the Donald T. Greenwood Collegiate Professor of Engineering and an Arthur F. Thurnau Professor in Mechanical Engineering at the University of Michigan. He earned his PhD at U. C. Berkeley in 1986 (Mechanical Engineering) prior to joining the faculty at Michigan. His research interests draw from the fields of computational and nonlinear dynamics with applications to the mechanics of single molecule DNA and DNA/protein complexes, wireless inertial sensors for analyzing human motion, and structural dynamics, topics on which he has published over 150 publications in archival journals and conference proceedings.
Dr. Cynthia Finelli is Associate Professor of Electrical Engineering and Computer Science, Associate Professor of Education, and Director and Graduate Chair for Engineering Education Research Programs at University of Michigan (U-M). Dr. Finelli is a fellow in the American Society of Engineering Education, a Deputy Editor of the Journal for Engineering Education, an Associate Editor of the IEEE Transactions on Education, and past chair of the Educational Research and Methods Division of ASEE. She founded the Center for Research on Learning and Teaching in Engineering at U-M in 2003 and served as its Director for 12 years. Prior to joining U-M, Dr. Finelli was the Richard L. Terrell Professor of Excellence in Teaching, founding director of the Center for Excellence in Teaching and Learning, and associate professor of electrical engineering at Kettering University.
Dr. Finelli's current research interests include student resistance to active learning, faculty adoption of evidence-based teaching practices, the use of technology and innovative pedagogies on student learning and success, and the impact of a flexible classroom space on faculty teaching and student learning. She also led a project to develop a taxonomy for the field of engineering education research, and she was part of a team that studied ethical decision-making in engineering students.
Newtonian mechanics, particularly dynamics, can be inherently difficult to teach in a large lecture setting. The study of motion is not always easily represented through traditional teaching methods that include lectures on theory augmented with homework problems. Motion, however, can be readily measured and analyzed using a new inexpensive technology known as inertial measurement units (IMUs), and IMU-based experiments can be incorporated into large classes to expose concepts about motion. We hypothesize using such experiments will: 1) increase student understanding of dynamics concepts, 2) build student self-efficacy, and 3) grow student intention to persist in the field.
The study, which is funded by NSF’s DUE:EHR program, is conducted in the context of an undergraduate introductory dynamics course at a large public university required by several different engineering disciplines. We are systematically incorporating the IMU-based experiments into the class at three levels: 1) instructor-created, instructor-led experiments, 2) instructor-created, student-led experiments, and 3) student-created, student-led experiments. The impact of the experiments on student conceptual understanding is measured by the Dynamics Concept Inventory, a well-researched, validated instrument that probes student understanding of engineering dynamics. Furthermore, the impact on self-efficacy and persistence is measured with a modified version of the Longitudinal Assessment of Engineering Self-Efficacy (LAESE), which is a validated survey instrument that measures four subfactors: 1) engineering self-efficacy, 2) course-specific self-efficacy, 3) intention to persist in the field, and 4) feelings of inclusion. Both instruments are administered at the beginning and end of the term.
To date, we have collected control data from 131 students across 3 sections in 1 semester with no IMU-based experiments, and we are completing the first level of our study (354 students across 7 sections in 2 semesters). This first level is comprised of instructors demonstrating two experiments relating to commonly misunderstood DCI concepts and students completing assignments exploring those concepts using data provided by IMUs. The DCI results show that two demonstrations alone have limited impact on student conceptual understanding. Furthermore, the LAESE results are inconclusive regarding how these experiments affect student self-efficacy and intention to persist. We hypothesize the effects will become apparent as students become more involved in the experiments with the aforementioned subsequent levels (i.e., as the experiments become progressively more hands-on and engaging). This update will compare the results of this first level of the study against the control data.
Vitali, R., & Perkins, N. C., & Finelli, C. J. (2018, June), Board 155: Introduction and Assessment of i-Newton for the Engaged Learning of Engineering Dynamics Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--29958
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