CCLI: Model Eliciting Activities

Karen M. Bursic; Natasa S. Vidic; Nora Siewiorek; Mary E. Besterfield-Sacre; Larry J. Shuman

Download Paper | Permalink

Conference: 2012 ASEE Annual Conference & Exposition
Location: San Antonio, Texas
Publication Date: June 10, 2012
Start Date: June 10, 2012
End Date: June 13, 2012
ISSN: 2153-5965
Conference Session: NSF Grantees' Poster Session
Tagged Topic: NSF Grantees Poster Session
Page Count: 20
Page Numbers: 25.290.1 - 25.290.20
DOI: 10.18260/1-2--21048
Permanent URL: https://peer.asee.org/21048
Download Count: 456

Paper Authors

biography

Larry J. Shuman University of Pittsburgh orcid.org/0000-0001-6884-7070

visit author page

Larry J. Shuman is Senior Associate Dean for Academic Affairs and professor of industrial engineering at the Swanson School of Engineering, University of Pittsburgh. His research focuses on improving the engineering education 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, Shuman is the Founding Editor of Advances in Engineering Education. He has published widely in 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 a B.S.E.E. from the University of Cincinnati. Shuman is an ASEE Fellow.

visit author page

biography

Mary E. Besterfield-Sacre University of Pittsburgh

visit author page

Mary Besterfield-Sacre is an Associate Professor and Fulton C. Noss Faculty Fellow in industrial engineering. She is the Director for the new Engineering Education Research Center (EERC) in the Swanson School of Engineering, and serves as a Center Associate for the Learning Research and Development Center at the University of Pittsburgh. Her principal research is in engineering assessment, which has been funded by the NSF, Department of Education, Sloan Foundation, Engineering Information Foundation, and NCIIA. Besterfield-Sacre’s current research focuses on three distinct but highly correlated areas: innovative design, entrepreneurship, and modeling. She also serves as an Associate Editor for the AEE Journal.

visit author page

biography

Karen M. Bursic University of Pittsburgh

visit author page

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. 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.

visit author page

author page

Natasa S. Vidic University of Pittsburgh

biography

Nora Siewiorek University of Pittsburgh

visit author page

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 M.S. in information science. Her research interests include engineering education and educational assessment and evaluation. Her K-12 outreach activities involve organizing a local science fair and a hands-on workshop in nanotechnology. Her other research interests include higher education administration and comparative and international education.

visit author page

Download Paper | Permalink

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. Here our overall goal has been touse the MEA construct as a means for enhancing engineering students’ problem solving andmodeling skills as well as their conceptual understanding of certain engineering topics. Over thefour years of the project we have pursued two main research avenues: MEAs as teaching toolsand MEA as learning assessment tools. This paper summarizes our results for these two researchthrusts as we near project completion.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 25 MEAs for upper level students that target problem solving skills and conceptual learning. In doing this we have found that MEAs can both improve conceptual learning and significantly 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, having conducted a series of experiments over the past three years. 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 have learned to triangulate measurements using three assessment instruments, two of which we developed: (1) pre- and post- concept questions 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). In addition, we have 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.

Citation
Format

Shuman, L. J., & Besterfield-Sacre, M. E., & Bursic, K. M., & Vidic, N. S., & Siewiorek, N. (2012, June), CCLI: Model Eliciting Activities Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21048

TY  - CPAPER
AB  -                   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. Here our overall goal has been touse the MEA construct as a means for enhancing engineering students’ problem solving andmodeling skills as well as their conceptual understanding of certain engineering topics. Over thefour years of the project we have pursued two main research avenues: MEAs as teaching toolsand MEA as learning assessment tools. This paper summarizes our results for these two researchthrusts as we near project completion.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 25 MEAs for upper level    students that target problem solving skills and conceptual learning. In doing this we have    found that MEAs can both improve conceptual learning and significantly 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, having conducted a series of experiments over the past three years. 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    have learned to triangulate measurements using three assessment instruments, two of which    we developed: (1) pre- and post- concept questions 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). In addition, we have 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.
AU  - Larry J. Shuman
AU  - Mary E. Besterfield-Sacre
AU  - Karen M. Bursic
AU  - Natasa S. Vidic
AU  - Nora Siewiorek
CY  - San Antonio, Texas
DA  - 2012/06/10
PB  - ASEE Conferences
TI  - CCLI: Model Eliciting Activities 
UR  - https://peer.asee.org/21048
DO  - 10.18260/1-2--21048
ER  -