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Evidence for the Effectiveness of a Grand Challenge-based Framework for Contextual Learning

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Conference

2014 ASEE Annual Conference & Exposition

Location

Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014

ISSN

2153-5965

Conference Session

NSF Grantees’ Poster Session

Tagged Division

Division Experimentation & Lab-Oriented Studies

Tagged Topic

NSF Grantees Poster Session

Page Count

11

Page Numbers

24.552.1 - 24.552.11

Permanent URL

https://peer.asee.org/20443

Download Count

34

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Paper Authors

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Lisa 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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Dr. Michael R. Gustafson II is an Associate Professor of the Practice of Electrical and Computer Engineering at Duke University. He received a B.S.E. in 1993 from Duke University, majoring in Electrical Engineering and Mechanical Engineering and Materials Science. He continued on at Duke to earn his M.S. and Ph.D. in Mechanical Engineering and Materials Science. His primary focus is on undergraduate curriculum and laboratory development, and he is responsible for the first-year Computational Methods in Engineering course required for all engineering students at Duke University.

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Joseph C. Nadeau Duke University

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Dr. Joseph C. Nadeau is an associate professor of the practice in the Department of Civil and Environmental Engineering at Duke University where he also serves as Director of Undergraduate Studies and ABET Coordinator for the department. He received a B.S. in Civil Engineering from Lehigh University, a S.M. in Civil Engineering from the Massachusetts Institute of Technology, and a Ph.D. in Engineering from the University of California at Berkeley. His teaching and research interests are in the areas of mechanics, structural design, and composite materials. He is a registered Professional Engineer.

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David E. 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.

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 (http://deid.pratt.duke.edu/) and is a registered professional engineer in 21 states.

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Michael M. Barger Duke University

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Michael is a doctoral student in Psychology and Neuroscience at Duke University. His research interests include students' motivation in STEM and the development of students' beliefs about education.

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Lisa Linnenbrink-Garcia Michigan State University

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Dr. Lisa Linnenbrink-Garcia is an associate professor of Educational Psychology in the Department of Counseling, Educational Psychology, and Special Education at Michigan State University. She received her Ph.D. in Education and Psychology from the University of Michigan, Ann Arbor. Her research focuses on the development of achievement motivation in school settings and the interplay among motivation, emotions, and learning, especially in STEM fields.

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Abstract

Evidence for the Effectiveness of a Grand Challenge-based Framework for Contextual LearningStudent motivation – and associated educational outcomes – can be influenced by the degree towhich course material connects to recognizable societal problems. The National Academy forEngineering has established the “Engineering Grand Challenges”, a set of 14 fundamentalproblems whose solution will require integrated contributions from engineers, scientists, andpolicy-makers. The current work builds the Engineering Grand Challenges (EGC) into apedagogical framework integrated into courses in several engineering disciplines, assessingwhether this framework increased student motivation and, if so, what sorts of students benefitfrom this approach.The EGC framework, as implemented here, follows a series of six stages that progress fromstatement of the problem, through exercises that teach a foundational concept using an EGCexample, to reflection on the role of engineering in addressing the problem. The framework wasimplemented in three diverse courses: a computational methods course taken by all first-yearengineering students, an upper-level signal-processing elective in electrical engineering, and adesign course for upper-level students in environmental engineering. Instructors for each of thesecourses implemented the EGC framework in a manner appropriate for their course. For example,students in the signal processing course investigated the EGC of “Reverse-Engineering theBrain”, which included a lecture/discussion led by a neuroscientist who uses signal processing,followed by a project assignment that applied spectral analysis and filter design to publiclyavailable data from a brain-computer interface contest. For all courses, baseline data werecollected from the same classes taught by the same instructors in the previous year.Results from the first year of implementation indicated significant benefits for the EGCframework, as well as differences in effectiveness across settings. Each student provided datathat included self-reported ratings of ABET criteria and standard psychological measures ofmotivation, and those measures were included in structural equation models that predicted inter-student differences in grades. The EGC framework was associated with significantly higher self-reported class effectiveness, as indexed by ABET criteria. Furthermore, in advanced classes theEGC framework enhanced a key measure of student motivation (i.e., situational interest), whichin turn was a positive predictor of student grades. This effect was not present in the introductoryclass examined. No differences between EGC and baseline groups were found in other measuresof self-reported motivation (e.g., perceived competence). Collectively, these results providestrong initial evidence that framing course activities around large-scale, societally relevantchallenges can have salutary effects upon students’ motivation and course performance. Ongoingwork examines these effects across multiple semesters of the same courses as well as acrossadditional courses from throughout engineering curricula.

Huettel, L., & Gustafson, M. R., & Nadeau, J. C., & Schaad, D. E., & Barger, M. M., & Linnenbrink-Garcia, L. (2014, June), Evidence for the Effectiveness of a Grand Challenge-based Framework for Contextual Learning Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. https://peer.asee.org/20443

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