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Improving Conceptual Learning in Engineering Economy using Model-Eliciting Activities (MEAs)

Improving Conceptual Learning in Engineering Economy using Model-Eliciting Activities (MEAs). KAREN M. BURSIC, LARRY SHUMAN, MARY BESTERFIELD-SACRE, TUBA PINAR YILDIRIM UNIVERSITY OF PITTSBURGH DEPARTMENT OF INDUSTRIAL ENGINEERING NORA SIEWIOREK SCHOOL OF EDUCATION

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Improving Conceptual Learning in Engineering Economy using Model-Eliciting Activities (MEAs)

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  1. Improving Conceptual Learning in Engineering Economy using Model-Eliciting Activities (MEAs) KAREN M. BURSIC, LARRY SHUMAN, MARY BESTERFIELD-SACRE, TUBA PINAR YILDIRIM UNIVERSITY OF PITTSBURGH DEPARTMENT OF INDUSTRIAL ENGINEERING NORA SIEWIOREK SCHOOL OF EDUCATION INDUSTRIAL ENGINEERING RESEARCH CONFERENCE CANCUN, MEXICO JUNE 7, 2010

  2. What are MEAs? • Model-Eliciting Activities (MEAs) – method for presenting complex, realistic, open-ended client driven problems. • Originally developed by mathematics education researchers and now increasingly used in engineering. • Based on six principles: model construction, reality, self-assessment, model documentation, generalizability, and effective prototype. • E-MEAs – introduce an ethical component into the problem that students must recognize and address.

  3. Experiment in Engineering Economy • Two sections of the core engineering economy course taught at Pitt by the same instructor, Fall 2009. • In one section the instructor assigned 3 E-MEAs; the other section was taught the same material with the same assignments but no MEAs were introduced. • Primary concern: Does the use of the E-MEAs increase learning of specific concepts? (Time value of money, cost estimation, comparing alternative investments, benefit-cost ratios, consideration of all relevant criteria, economic analysis of contemporary problems, and dealing with uncertainty)

  4. ABET outcomes • Secondary goal – improving student attainment of specific ABET outcomes: • f – “an understanding of professional and ethical responsibility” • h – “the broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context” • j – “ a knowledge of contemporary issues”

  5. “Concept Inventory” • To measure conceptual knowledge, we developed an “engineering economics concept inventory”. • 9 questions, 5 points each • mix of multiple choice and short answer aimed at the specific engineering economic analysis concepts. • Given to both sections of students at the beginning (pre) and end (post) of the semester. • Graded by the same graduate student researcher using the solution key developed by the instructor.

  6. E-MEAs used in Engineering Economy

  7. Concept Inventory Scores (45 possible)

  8. Additional Results • Teaching evaluation contains questions related to ABET outcomes (standard across the school). • “…indicate how much has this course improved your knowledge or skill …” • Scale of 1 to 5 (“not at all” to “a great deal”).

  9. Analysis of student work • Performed well with respect to the direct application of engineering economic analysis tools. • General models were not always created. • Did not sufficiently determine the economic impacts of the ethical and other societal issues. • Overall, students recognized that “least-cost” is not always the best solution!

  10. Conclusions • Use of E-MEAs requires substantial effort on the part of the instructor. • To be truly effective, the instructor must provide feedback and engage the students in a useful discussion. • E-MEAs can be very effective in reinforcing and integrating course concepts. • E-MEAs are ideally suited as measures of ABET outcomes. • Our experiment demonstrated that E-MEAs are effective for increasing conceptual learning in an engineering economy course.

  11. Future Directions • Must be able to further students understanding of “generalizability” of models. • We are introducing MEAs in probability and statistics 1 and 2, and engineering ethics as well as courses in other departments in the school, including bio-engineering. • Student “reflective” data is also being collected to measure life long learning and other benefits of MEAs. • Additional research is focused on the modeling aspects of MEAs.

  12. Acknowledgement This research is supported in part by the National Science Foundation through DUE 071780: “Collaborative Research: Improving Engineering Students’ Learning Strategies through Models and Modeling.” More information at: http://modelsandmodeling.net Questions?

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