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Application of Brain-based Learning Principles to Engineering Mechanics Education: Implementation and Preliminary Analysis of Connections Between Employed Strategies and Improved Student Engagement

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Cognitive Engagement

Tagged Division

Educational Research and Methods

Page Count

11

Permanent URL

https://peer.asee.org/29806

Download Count

16

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Paper Authors

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Firas Akasheh Tuskegee University

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Dr. Akasheh has been with the Mechanical Engineering Department at Tuskegee University since 2008. His primary interest is in the area of solid mechanics and manufacturing as well as the integration of best practices in engineering education.

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John T. Solomon Tuskegee University

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John T Solomon is Associate Professor at Tuskegee University, Alabama. He received PhD in Mechanical Engineering from Florida State University, USA in 2010. Prior joining Tuskegee University he was a research associate in Florida Center for Advanced Aero- Propulsion. Dr. Solomon's research interests include high speed flow control, actuator development, experimental fluid mechanics and engineering education.

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Eric Hamilton Pepperdine University

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Eric Hamilton is Professor of Education at Pepperdine University in Los Angeles. He holds a courtesy appointment in mathematics. Dr. Hamilton recently completed a three year Fulbright effort in the Republic of Namibia studying the potential for digital makerspaces in strengthening science and mathematics education there, and he currently leads an NSF-funded informal science education project exploring digital makerspaces and participatory teaching in international collaborations. Dr. Hamilton is co-PI for an NSF-funded IUSE project based at Tuskegee University, blending digital tools with advances in the learning sciences to improve undergraduate engineering education. He has also led the NSF-funded Distributed Learning and Collaboration symposium series in Shanghai, Singapore and Germany. Dr. Hamilton came to Pepperdine from the US Air Force Academy, where he was a research professor and director of the Center for Research on Teaching and Learning. Prior to that, he held was a member of the US government’s senior executive service corps as the director for the education and learning technology research division at NSF. Originally tenured in computer science, he came to NSF from Loyola University Chicago, where he organized and led a large consortium on STEM learning, invented and secured patents on pen-based computing collaboration, and directed the Chicago Systemic Initiative in mathematics and science education. Hamilton earned undergraduate and graduate degrees from the University of Chicago and a PhD from Northwestern University.

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Chitra R. Nayak Tuskegee University

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Dr. Nayak joined Tuskegee University as an assistant professor in Physics in 2014. After completing her Ph.D (2009) in the area of nonlinear dynamics from Cochin University, India, she worked as a postdoctoral fellow in the interdisciplinary field of bacterial biophysics and immunology at Dalhousie University and University of Toronto, Canada. Her current area of research work includes nonlinear analysis of bio-signals and fluid dynamics. Dr. Nayak is also involved in education research at Tuskegee University.

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

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Dr. Vimal Viswanathan is an assistant professor in the Mechanical Engineering Department at San Jose State University. He earned his Ph.D. from Texas A&M University. His research interests include design innovation, creativity, design theory and engineering education.

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Abstract

In a pilot study supported by NSF, an instructional model that uses brain based learning principles as instructional protocols has been developed and successfully implemented in the course Introduction to Fluid Mechanics at a HBCU. Motivated by that success, we extended a similar intervention to another course, Dynamics, in the same school. In this paper, we report preliminary data from this intervention. The main strategies implemented in this intervention include: organization of the course into specific concepts and sub-concepts, which are concisely presented by short (limited to 2-6 minutes) content-rich lectures (diagrams, animations, narrations), active learning through in-class worksheets, and prompt (next class) feedback. The lectures, by design, included brain-based protocols like connection to relevant old/prior knowledge, creating of neural networks, and repeated use of neurons. Results from this implementation showed that students’ engagement and efficacy were significantly enhanced by this approach. That confirms the findings of our previous study and shows that the model effectiveness is independent of the instructor who develops and implements the model, as long as the brain-based learning principles are followed. One third of the participating students strongly agreed that the approach is more engaging both inside and outside the classroom and that the overall learning of the presented concepts was improved. Additionally this study investigated the contribution of the various components of the brain-based approach, when compared to the traditional delivery of the same course, to the improved learning and engagement. Teaching using concept-oriented short premade lectures rich with illustrations and animations was an essential contributor to the observed improvements in the opinion of about 40% of the participating students. Similarly, the active work on in-class worksheets based on the presented concept(s) during class time and availability of the lectures to the students anytime/anywhere beyond the class time were essential elements over traditional teaching in the opinion of 33% and 77% of the students, respectively. Further results and analysis will be presented based on the data currently being collected. Nevertheless, there is sufficient evidence that substantiates the benefits of brain-based learning principles in improving basic engineering mechanics education.

Akasheh, F., & Solomon, J. T., & Hamilton, E., & Nayak, C. R., & Viswanathan, V. K. (2018, June), Application of Brain-based Learning Principles to Engineering Mechanics Education: Implementation and Preliminary Analysis of Connections Between Employed Strategies and Improved Student Engagement Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/29806

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