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Model Eliciting Activities Motivated Problem Solving Process: Solution Path Analysis

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2014 ASEE Annual Conference & Exposition


Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014



Conference Session

Student Learning, Problem Solving, and Critical Thinking 1

Tagged Division

Educational Research and Methods

Page Count


Page Numbers

24.911.1 - 24.911.19



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Paper Authors


Natasa S. Vidic University of Pittsburgh

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Natasa Vidic is an assistant professor in the department of industrial engineering at the University of Pittsburgh, where she received a Ph.D. in industrial engineering in 2008. She has an M.S. in operations research from the University of Delaware (1992) and a B.S. in civil/transportation engineering from the University of Belgrade in Serbia (1987).
Before joining the faculty in 2010, Dr. Vidic was a visiting assistant professor and a graduate research assistant. She also was the associate director of operations for the Engineering Education Research Center from January 2011 to September 2013. Her work experience includes two years as a project manager in the planning department of the Port Authority of Allegheny County in Pittsburgh, and a research associate at the University of Novi Sad's Institute for Traffic and Transportation Engineering.
Dr. Vidic has published in peer-reviewed journals and conference proceedings, including those of ASEE and INFORMS.
She currently is participating in collaborative research on improving engineering students’ learning strategies through models and modeling and is interested in the assessment and effectiveness of model-eliciting activities when implemented in the classroom. Her University of Pittsburgh research team is focusing on assessment and improvement in conceptual learning as well as problem solving, using a series of assessment instruments to better understand and measure the educational benefits.

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Nur Ozge Ozaltin

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Nur Özge Özaltin received her master's degree and Ph.D. in industrial engineering from the University of Pittsburgh. She received her B.S. in industrial engineering at Bogazici University in Turkey. She studies improving innovation through modeling the design process. Her methods include Bayesian network modeling, and statistical and qualitative data analysis.

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Mary E. Besterfield-Sacre University of Pittsburgh

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Dr. Mary Besterfield-Sacre is an associate professor and Fulton C. Noss Faculty Fellow in Industrial Engineering at the University of Pittsburgh. She is the director for the Engineering Education Research Center (EERC) in the Swanson School of Engineering and serves as a center associate for the Learning Research and Development Center. Her principal research is in engineering education assessment, which has been funded by the NSF, Department of Education, Sloan Foundation, Engineering Information Foundation, and NCIIA. Dr. Sacre’s current research focuses on three distinct but highly correlated areas – innovative design and entrepreneurship, engineering modeling, and global competency in engineering. She has served as an associate editor for the JEE and is currently associate editor for the AEE Journal as well as on the advisory board for the NAE Frontiers of Engineering Education.

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Larry J. Shuman University of Pittsburgh Orcid 16x16

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Larry Shuman is senior associate dean for academic affairs and distinguished service professor of industrial engineering, Swanson School of Engineering, University of Pittsburgh. His research focuses on improving the engineering educational experience, emphasizing assessment of learning and problem solving abilities, and studying the ethical behavior of engineers and engineering managers. He was the principal investigator for a seven university NSF-sponsored study on Models and Modeling that focused on using MEAs in engineering classrooms. Dr. Shuman is the founding editor of Advances in Engineering Education, and an ASEE Fellow. He holds a B.S.E.E. from the University of Cincinnati and a Ph.D. in operations research from Johns Hopkins University.

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Model Eliciting Activities motivated problem solving process: solution path analysisAs part of a larger NSF funded project focused on developing, incorporating and assessingModel Eliciting Activities (MEAs), we conducted a study to investigate the solution pathsteams used in their problem solving processes. In short, MEAs require the team to develop ageneralizable, mathematical model to solve the problem and to present both the solutionmethod and the results to the “client” in a form of a written report. Our previous researchshows improved student conceptual understanding of topics, improved student problemsolving and improved professional skills using MEAs; however, it was found that a portionof student teams still performed poorly on the activity. This particular experiment is directedat obtaining additional insight into the underlying reasons for student teams’ incorrectsolution paths when solving MEAs. In doing so, we focused on determining where and whyin the problem solving process the teams start to go awry with their solution process. Acontent analysis of the process was performed to identify underlying themes of how groupssolved the problem; a detailed path analysis of the solution process similar to the previousanalysis performed with the written reports was also performed. Data were recorded frommultiple teams solving the problem ‘out-loud,’ transcribed, and the transcripts wereanalyzed using both qualitative and quantitative methods. The qualitative analysis allowedthe research team to have an in-depth understanding of why students selected certainsolution paths. In doing the analysis we focused on six key questions: Where do students gowrong and why? Do they iterate? Do they iterate but still hold on to poor assumptions? Howmany times and do they correct their mistakes? Do they have new ideas? How often do theyhave new ideas and are these ideas right or wrong?

Vidic, N. S., & Ozaltin, N. O., & Besterfield-Sacre, M. E., & Shuman, L. J. (2014, June), Model Eliciting Activities Motivated Problem Solving Process: Solution Path Analysis Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--22844

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