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CCLI: Model Eliciting Activities: Experiments and Mixed Methods to Assess Student Learning

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Conference

2011 ASEE Annual Conference & Exposition

Location

Vancouver, BC

Publication Date

June 26, 2011

Start Date

June 26, 2011

End Date

June 29, 2011

ISSN

2153-5965

Conference Session

NSF Grantees Poster Session

Tagged Topic

NSF Grantees

Page Count

17

Page Numbers

22.314.1 - 22.314.17

Permanent URL

https://peer.asee.org/17595

Download Count

19

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Paper Authors

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Larry J. Shuman University of Pittsburgh Orcid 16x16 orcid.org/https://0000-0001-6884-7070

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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 E. Besterfield-Sacre University of Pittsburgh

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Mary Besterfield-Sacre is an Associate Professor and Fulton C. Noss Faculty Fellow in Department of Industrial Engineering, a Center Associate for the Learning Research and Development Center, and the Director for the Engineering Education Research Center at the University of Pittsburgh. Her principal research is in engineering education assessment, which has been funded by the NSF, Department of Education, Sloan Foundation, Engineering Information Foundation, and the NCIIA. Mary’s current research focuses on three distinct but highly correlated areas – innovative product design, entrepreneurship, and modeling. She has served as an associate editor for the JEE and is currently associate editor for the AEE Journal.

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Tuba Pinar Yildirim University of Pittsburgh

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Tuba Pinar Yildirim holds a Ph.D. in Industrial Engineering and is a doctoral candidate in Marketing at the Katz Graduate School of Business. She received an M.S. degree in Industrial Engineering at the University of Pittsburgh, and B.S. degrees in Industrial and Mechanical Engineering fields from Middle East Technical University in Turkey. Her interests are modeling, and cognitive and affective processes that motivate or hinder modeling skills, implementation of game theoretic and stochastic models.Her publications appeared in Journal of Marketing, Journal of Engineering Education, and International Journal of Eng. Education, IEEE Education. She was the recipient of the Industrial Engineering Best Paper Award in 2007, and was selected as a ‘University of Pittsburgh Honoree’ in 2008.

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Karen M. Bursic University of Pittsburgh

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Karen M. Bursic is an Assistant Professor and the Undergraduate Program Director for Industrial Engineering at the University of Pittsburgh. She received her B.S., M.S., and Ph.D. degrees in Industrial Engineering from the University of Pittsburgh. Prior to joining the department she worked as a Senior Consultant for Ernst and Young and as an Industrial Engineer for General Motors Corporation. She teaches undergraduate courses in engineering economics, engineering management, and probability and statistics in Industrial Engineering as well as engineering computing in the freshman engineering program. Dr. Bursic has done research and published work in the areas of Engineering and Project Management and Engineering Education. She is a member of IIE and ASEE and is a registered Professional Engineer in the state of Pennsylvania.

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Natasa Vidic University of Pittsburgh

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Assistant Professor
Industrial Engineering Department
Swanson School of Engineering
University of Pittsburgh

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Abstract

CCLI: MODEL ELICITING ACTIVITIES: EXPERIMENTS AND MIXED METHODS TO ASSESS STUDENT LEARNINGAs part of a seven university CCLI Type 3 collaborative effort focused on models and modeling,we have extended the model eliciting activity (MEA) construct to upper division engineeringprograms. Originally developed and validated by mathematics education researchers, MEAswere found to have significant value as an educational tool. Hence, our overall goal has been touse this construct as a means for enhancing engineering students’ problem solving and modelingskills as well as their conceptual understanding of certain engineering topics. Specifically, wehave pursued two main research avenues: MEAs as teaching tools and MEA as learningassessment tools. This paper summarizes our results for these two research thrusts as we enterour fourth project year.In using MEAs as a teaching tool – we have focused on three main activities: Development of effective MEAs: We have created a series of over 20 MEAs for upper level students that target students’ problem solving skills and conceptual learning. In doing this we have found that MEAs also enhance such important professional skills as communication, teamwork, and ethical understanding. Implementation of MEAs: We have introduced and rigorously assessed our MEAs in classroom settings as a means to further understand students’ problem solving, modeling and teamwork processes. Enhancing the learning benefits of MEAs: Our consortium has added new conceptual dimensions to MEAs to further enhance student learning. In particular, we have introduced an ethical dimension as a means for improving students’ ability to recognize and resolve those types of ethical dilemmas that arise in the engineering workplace.In 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: We have developed a series of assessment instruments to better understand and measure the educational benefits of using MEAs. Specifically, we are triangulating across three assessment instruments, two of which we developed: (1) pre- and post- concept inventories to assess gain, (2) an online reflection tool to assess process, and (3) a grading rubric to assess the resultant artifact (general model and specific solution). We have also developed an instrument to measure students’ self-efficacy scale related to their modeling skills. Assessing the MEA motivated problem solving process: Through the use of various data collection tools, including PDAs and wikis, in combination with the mentioned assessment instruments, we are identifying the various problem solving processes used by the student teams, as well as the range of problems that can be addressed, to determine how effective the various processes are relative to improved conceptual understanding.This paper summarizes our achievements in each of these five activities. Particular emphasis isplaced on our mixed measurements for student learning and achievement, and a discussion of therelative conceptual gain for a series of MEA experiments, including those where a comparisongroup was available.

Shuman, L. J., & Besterfield-Sacre, M. E., & Yildirim, T. P., & Bursic, K. M., & Vidic, N. (2011, June), CCLI: Model Eliciting Activities: Experiments and Mixed Methods to Assess Student Learning Paper presented at 2011 ASEE Annual Conference & Exposition, Vancouver, BC. https://peer.asee.org/17595

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