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Ccli: Model Eliciting Activities: Experiments And Mixed Methods To Assess Student Learning

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2010 Annual Conference & Exposition


Louisville, Kentucky

Publication Date

June 20, 2010

Start Date

June 20, 2010

End Date

June 23, 2010



Conference Session

NSF Grantees Poster Session

Page Count


Page Numbers

15.266.1 - 15.266.18

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Paper Authors


Larry Shuman University of Pittsburgh

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Larry J. Shuman is Senior Associate Dean for Academics and Professor of Industrial Engineering at the University of Pittsburgh. His research focuses on improving the engineering educational experience with an emphasis on assessment of design and problem solving, and the study of the ethical behavior of engineers and engineering managers. A former senior editor of the Journal of Engineering Education, Dr. Shuman is the founding editor of Advances in Engineering Education. He has published widely in the engineering education literature, and is co-author of Engineering Ethics: Balancing Cost, Schedule and Risk - Lessons Learned from the Space Shuttle (Cambridge University Press). He received his Ph.D. from The Johns Hopkins University in Operations Research and the BSEE from the University of Cincinnati. He is an ASEE Fellow.

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Mary Besterfield-Sacre University of Pittsburgh

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Mary Besterfield-Sacre, Associate Professor and Fulton C. Noss Faculty Fellow in the Department of Industrial Engineering and Center Associate for the Learning Research and Development Center at the University of Pittsburgh. Dr. Sacre’s principal research interests are in engineering education assessment and evaluation methods. She has served as an associate editor for the Journal of Engineering Education and is currently associate editor for the Applications in Engineering Education Journal. She received her B.S. in Engineering Management from the University of Missouri - Rolla, her M.S. in Industrial Engineering from Purdue University, and a Ph.D. in Industrial Engineering at the University of Pittsburgh.

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Tuba Pinar Yildirim University of Pittsburgh

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Tuba Pinar Yildirim is a dual doctoral candidate of Industrial Engineering and Marketing at University of Pittsburgh. She received her MS degree in Industrial Engineering at the University of Pittsburgh, and BS degrees in Industrial and Mechanical Engineering fields from Middle East Technical University in Turkey. Her primary research interests are modeling and cognitive and affective processes that motivate or hinder modeling skills. Her publications appeared in Journal of Engineering Education, International Journal of Eng.
Education and Journal of Marketing. Her other research is under review at American Economic Review, Journal of Marketing and Marketing Science. She was also the recipient of the IERC Best Paper Award in Engineering Education in 2007.

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Nora Sieworiek University of Pittsburgh

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Nora Siewiorek is a graduate student in the Administrative and Policy Studies department in the School of Education at the University of Pittsburgh where she also received her MS in Information Science. Her research interests include: engineering education and educational assessment and evaluation. Her K-12 outreach activities are organizing a local science fair and a hands on workshop in nanotechnology. Her other research interests are: higher education administration, comparative and international education.

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

CCLI: Model Eliciting Activities: Experiments and Mixed Methods to Assess Student Learning Abstract As part of a seven university CCLI Type 3 collaborative effort addressing models and modeling as a foundation for undergraduate curriculum enhancement and assessment, we are building upon and extending the model eliciting activity (MEA) construct, originally developed and validated by mathematics education researchers. Our overall goal is to enhance problem solving and modeling skills and conceptual learning of engineering students through the use this construct. At the University of Pittsburgh, we have pursued two main research avenues: MEAs as teaching tools and MEA as learning assessment tools. This paper summarizes our results to date. Under the first – using MEAs as a teaching tool – we have focused on three main activities: development of effective MEAs, implementation of (new or adapted) MEAs, and enhancing the learning benefits of MEAs:

Under the second stream - using MEAs as a learning tool - we have focused on two additional activities: assessing the effectiveness of MEAs in various dimensions including improving conceptual learning and problem solving, and assessing the MEA motivated problem solving process.

We summarize our achievements in these five activities over the first two and half years of our four year project. We provide an overview of the 18 MEAs we have developed or modified. Particular emphasis is placed on our mixed measurements of student learning and achievement, including the use of pre and post concept inventories, deconstruction of MEA solution paths and conceptual understanding, rubric scoring of completed MEAs and student reflections of the just completed problem solving process.

Introduction “Collaborative Research: Improving Engineering Students' Learning Strategies Through Models and Modeling” is a CCLI Type 3 project involving seven university partners: California Polytechnic State University, Colorado School of Mines, Purdue University, United States Air Force Academy, University of Pittsburgh, University of Minnesota-Twin Cities and Pepperdine University. We are building upon and extending the model-eliciting activities (MEA) constructoriginally developed by mathematics educators, that has recently been introduced into engineering education. These posed scenarios simulate authentic, real-world problems that teams of students then address. MEAs were first developed as a mechanism for observing the development of student problem-solving competencies and the growth of mathematical cognition. However, it has been increasingly documented that MEAs provide a learning methodology that helps students become better problem solvers.

We are taking the theoretical framework from mathematics education and research results from a series of NSF funded studies in order to create a strategic, scalable approach for addressing crucial goals in engineering education. These include: ≠ Developing effective, transferable competencies in problem-solving and creativity; ≠ More effectively learning and retaining important concepts; and ≠ More effectively identifying misconceptions and nurturing positive ethical frameworks.

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2010 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015