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A Grand Challenge-based Framework for Contextual Learning in Engineering

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2013 ASEE Annual Conference & Exposition


Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013



Conference Session

NSF Grantees' Poster Session

Tagged Topic

NSF Grantees Poster Session

Page Count


Page Numbers

23.51.1 - 23.51.14



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Paper Authors


Lisa G. Huettel Duke University

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Dr. Lisa G. Huettel is an associate professor of the practice in the Department of Electrical and Computer Engineering at Duke University where she also serves as associate chair and director of Undergraduate Studies for the department. She received a B.S. in Engineering Science from Harvard University and earned her M.S. and Ph.D. in Electrical Engineering from Duke University. Her research interests are focused on engineering education, curriculum and laboratory development, and applications of statistical signal processing.

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Michael R. Gustafson II Duke University

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Joseph C. Nadeau Duke University


David Schaad Duke University

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Dr. David Schaad has over seventeen years of design and engineering experience as a consulting engineer working for various firms including: Parsons Engineering Science, Appian Consulting Engineers and Marshall Miller and Associates.
As part of his experience, Dr. Schaad has: designed waste water treatment systems to address industrial and domestic waste streams; developed designs of storm water control structures and strategies to address water quality and quantity; designed fluid transport systems to replace water supplies impacted by anthropogenic sources; designed fuel transport and delivery systems; developed designs for commercial and residential development; prepared land use plans; developed designs to protect against potential flood hazards; designed and developed plans and specifications for fluid handling systems, waste mitigation alternatives and remedial actions for RCRA and CERCLA sites including active industrial facilities and inactive disposal sites (including NPL sites); conducted feasibility studies by evaluating and analyzing the economic and engineering considerations of multiple design alternatives; obtained extensive experience with innovative remedial techniques (including groundwater extraction and treatment, air sparging, soil vapor extraction, and bioventing).
Current research focuses on sustainable engineering, community development, water and wastewater treatment design, stormwater retention/detention and treatment design, urban hydrology, constructed wetland and stream restoration design, ecological stabilization, sustainable engineering in land development, water resources, water and wastewater treatment.
He is also the faculty advisor for Duke Engineers for International Development and the Duke Chapter of Engineers Without Borders and has led DukeEngage experiences every year since the inception of the program. He has facilitated and/or led trips to Indonesia, Uganda, Kenya, Honduras, El Salvador, Bolivia, and Peru. Representative projects he has worked on include: building a 4800sf Infant and Maternal Health Clinic, constructing a 100ft long vehicular bridge over a seasonally flooded river, and installing a 3km long waterline. He was an inaugural member of the Faculty Leadership Council (FLC) of EWB and is a registered professional engineer in 21 states.

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Michael M Barger


Lisa Linnenbrink-Garcia Duke University

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Dr. Lisa Linnenbrink-Garcia is an associate professor of Developmental Psychology and Education at Duke University. She received her Ph.D. in Education and Psychology from the University of Michigan-Ann Arbor. Her research (1) identifies educational contexts that enhance motivation and subsequent engagement and learning and (2) examines the mechanisms through which motivation influences academic engagement and achievement. Dr. Linnenbrink-Garcia conducts this research in school-age samples (upper elementary through college), with a specific focus on students' learning and motivation in STEM fields.

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A Grand Challenge-based Framework for Contextual Learning in EngineeringA key finding within the current engineering education literature is that exposure to real-worldapplications – especially when presented in an active, experiential learning environment –increases both student interest and pedagogical effectiveness. This idea of contextual learning,learning designed so that students can carry out activities and solve problems in ways that reflectthe real-world nature of such tasks, is based on cognitive learning theory and current research incognitive psychology and neuroscience. Such research suggests that initial learning, transfer ofthat learning to other contexts, and retention of learned material is facilitated when concepts arepresented in a familiar or relatable context. That is, students are motivated when learning hasmeaning.The National Academy of Engineering recently outlined twelve Grand Challenges forEngineering (e.g., “Reverse Engineering the Brain”) that collectively constitute some of thelargest and most pressing real-world issues facing engineering research and practice. We havedevleoped an instructional framework for engineering education that utilizes these GrandChallenges as the context for teaching a wide range of concepts. The framework comprises sixstages, each building upon its predecessor. Students first progress from thinking about the grand,overarching theme from a multitude of perspectives to focusing on a discipline-specific view ofthe challenge (Stages 1-3). Then, they concentrate on learning the specific course content andapplying what they have learned to a specific, real-world problem inspired by the overarchingGrand Challenge (Stages 4-5). Finally, they analyze what they have learned from the hands-onactivities and reflect back on how this can inform their understanding of, and solutions to, theGrand Challenge (Stage 6).This paper begins with a description of the framework including its foundation in contextuallearning theory and the motivation for using the Grand Challenges. Subsequently, theimplementation of the framework in two engineering courses is described: ComputationalMethods in Engineering, which is required of all freshman engineers, and Fundamentals ofDigital Signal Processing, which is an elective course taken by junior and senior students inElectrical and Computer Engineering and Biomedical Engineering. Details of the learningmodules and activities corresponding to the six stages of the framework are presented for eachcourse. Similarities and differences in implementation are highlighted, illustrating how acommon framework can be applied to seemingly very different courses. Finally, the use of theframework is evaluated in terms of its impact on student motivation and learning usingpsychologically rigorous measures..

Huettel, L. G., & Gustafson, M. R., & Nadeau, J. C., & Schaad, D., & Barger, M. M., & Linnenbrink-Garcia, L. (2013, June), A Grand Challenge-based Framework for Contextual Learning in Engineering Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--19065

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