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Problem-Based Learning and Industrial Engineering

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Conference

2013 ASEE Annual Conference & Exposition

Location

Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013

ISSN

2153-5965

Conference Session

Improving course effectiveness

Tagged Divisions

Engineering Management, Engineering Economy, and Industrial Engineering

Page Count

10

Page Numbers

23.985.1 - 23.985.10

Permanent URL

https://peer.asee.org/22370

Download Count

60

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Paper Authors

biography

Abhijit Gosavi Missouri University of Science & Technology

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Abhijit Gosavi obtained a Ph.D. in Industrial Engineering from the University of South Florida in 1999. He also has a B.S. and a master's degree in Mechanical Engineering (both degrees were from India). His research interests include simulation-based optimization, engineering education, Markov decision processes, revenue management, and productive maintenance. He has published in numerous journals in areas related to his research. He is currently an Assistant Professor in the Department of Engineering Management and Systems Engineering in Missouri University of Science and Technology, located in Rolla, MO.

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biography

Jane M. Fraser Colorado State University, Pueblo

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Jane M. Fraser is Chair of the Department of Engineering at Colorado State University, Pueblo. She was formerly on the faculty at the Ohio State University and Purdue University. She has a B.A. in mathematics from Swarthmore College and a M.S. and a Ph.D. in industrial engineering and operations research from the University of California, Berkeley.

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Abstract

Problem-Based Learning and Industrial EngineeringProblem-based learning (PBL), also called inductive learning, is a well-known approach forteaching engineering courses. We undertake a study of concepts that can be taught via PBL,along with an analysis of courses in the industrial engineering (IE) curriculum suitable for PBL.While in the traditional deductive style of teaching, one usually starts with explanation ofprinciples followed by examples, PBL is primarily characterized by providing examples first andthen generalizing to the underlying principles later. There is a great deal of literature that citesevidence of PBL being more effective than deductive learning. However, PBL also providesnumerous challenges to the instructor – especially to those used to teaching deductively. Someof these challenges include taking a fresh look at how the same topics that were taught viadeductive methods can potentially be taught using PBL, the tradeoff between the increasedamount of time it consumes with the amount of material to be covered in the class, and ensuringthat the student is not confused and does not walk away with an incorrect understanding of thetopic. It is our intent to enumerate these challenges in terms of specific courses. We haveselected two different courses, taught at two separate universities (at which the authors areemployed), in which a subset of topics will be taught via PBL. In particular, we have selected anundergraduate course on Discrete-event Simulation and an undergraduate course on EngineeringProbability and Statistics as our pilot courses. Within the area of PBL, there are two extreme viewpoints, and perhaps both are of onlytheoretical interest; an instructor is likely to choose a middle path. Nonetheless, it is useful toanalyze these two extremes, as it can provide perspective on what can and cannot be achieved byPBL and what challenges PB can pose. One extreme is to present a topic and ask the students toresearch the topic entirely on their own before discussing it in the classroom. The other extremeis for the instructor to provide an example and engage the class in a discussion on how togeneralize from the example. Both methods have their merits and demerits that we seek toanalyze. We will also develop metrics to evaluate the effectiveness of PBL that can beimplemented within our IE courses. Finally, we will present an analysis of topics in the IEcurriculum that in our opinion are best taught via deductive learning.

Gosavi, A., & Fraser, J. M. (2013, June), Problem-Based Learning and Industrial Engineering Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. https://peer.asee.org/22370

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