The Most Misunderstood ABET Criterion - Criterion 3b

K.S. Krishnamoorthi

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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: Multidisciplinary Learning, Evaluation, and Assessment
Tagged Division: Multidisciplinary Engineering
Page Count: 11
Page Numbers: 23.1223.1 - 23.1223.11
DOI: 10.18260/1-2--22608
Permanent URL: https://peer.asee.org/22608
Download Count: 426

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K.S. Krishnamoorthi Bradley University

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Dr. Krishnamoorthi is a professor of industrial engineering in the IMET Department at Bradley University and teaches mostly engineering statistics, quality engineering and engineering economics. He has a BS in ME from University of Madras, India, MA in statistics and PhD in industrial engineering from University of Buffalo. He has published in the areas of quality costs, process control and statistical thinking for engineers. He has been a consultant to industry in process improvement using statistical methods. His book, A First Course in Quality Engineering, 2/e, was published by the CRC Press in 2011.

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Abstract

The Most Misunderstood ABET Criterion – Criterion 3b"The average Japanese worker has a more in-depth knowledge of statistical methods than anaverage American engineer," stated a U.S. businessman returning from a visit to Japan, as areason why the Japanese were able to produce better quality products than U.S.manufacturers. That statement, made almost 30 years ago, is probably true even today as morethan 75% of engineers graduating from a typical college of engineering in the U.S., with a fewexceptions, have no in-depth knowledge of statistics when they graduate and, according to ourestimate, more than 75% of the engineers currently working in American industry don’t have anin-depth understanding of statistics, either.The Criterion 3b, a student outcome in the EAC’s accreditation criteria, calls for: "an ability todesign and conduct experiments, as well as to analyze data and interpret results." This criterionwas possibly a response from EAC to demands from industry that all engineers be trained instatistics to the level of being able to design and perform experiments and analyze data therefrom in order to draw good conclusions. Designing efficient experiments is an advanced topicin statistics and requires a good preparation in the mathematics of probability and statistics.This preparation in statistics also gives an engineer tools to model process behavior so that theycan see patterns and order where an ordinary eye sees only chaos and confusion.Yet, as an ABET program evaluator for the last 15 years, this author has seen many instanceswhere engineering programs, with the exception of IE programs, try to show that they meet theCriterion 3b without incorporating any formal education in statistics in their curriculums.This paper is intended to bring home the full intent of Criterion 3b and increase the awarenessamong engineering educators the need for satisfying this requirement in letter and spirit.Several case studies are included to show how knowledge of statistics and statistical methodsenables an engineer to see relationships among process variables that are themselves cloudedwith variability, discover root causes of problems, implement good solutions in order toimprove product quality, reduce waste and increase throughput.The multi-disciplinary programs in engineering have the most opportunity to incorporatestatistics as part of their curriculum and offer classes in statistics for all engineering majors so asto spread “stat-ability” among all engineers.

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Krishnamoorthi, K. (2013, June), The Most Misunderstood ABET Criterion - Criterion 3b Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--22608

TY  - CPAPER
AB  -                         The Most Misunderstood ABET Criterion – Criterion 3b&quot;The average Japanese worker has a more in-depth knowledge of statistical methods than anaverage American engineer,&quot; stated a U.S. businessman returning from a visit to Japan, as areason why the Japanese were able to produce better quality products than U.S.manufacturers. That statement, made almost 30 years ago, is probably true even today as morethan 75% of engineers graduating from a typical college of engineering in the U.S., with a fewexceptions, have no in-depth knowledge of statistics when they graduate and, according to ourestimate, more than 75% of the engineers currently working in American industry don’t have anin-depth understanding of statistics, either.The Criterion 3b, a student outcome in the EAC’s accreditation criteria, calls for: &quot;an ability todesign and conduct experiments, as well as to analyze data and interpret results.&quot; This criterionwas possibly a response from EAC to demands from industry that all engineers be trained instatistics to the level of being able to design and perform experiments and analyze data therefrom in order to draw good conclusions. Designing efficient experiments is an advanced topicin statistics and requires a good preparation in the mathematics of probability and statistics.This preparation in statistics also gives an engineer tools to model process behavior so that theycan see patterns and order where an ordinary eye sees only chaos and confusion.Yet, as an ABET program evaluator for the last 15 years, this author has seen many instanceswhere engineering programs, with the exception of IE programs, try to show that they meet theCriterion 3b without incorporating any formal education in statistics in their curriculums.This paper is intended to bring home the full intent of Criterion 3b and increase the awarenessamong engineering educators the need for satisfying this requirement in letter and spirit.Several case studies are included to show how knowledge of statistics and statistical methodsenables an engineer to see relationships among process variables that are themselves cloudedwith variability, discover root causes of problems, implement good solutions in order toimprove product quality, reduce waste and increase throughput.The multi-disciplinary programs in engineering have the most opportunity to incorporatestatistics as part of their curriculum and offer classes in statistics for all engineering majors so asto spread “stat-ability” among all engineers.
AU  - K.S. Krishnamoorthi
CY  - Atlanta, Georgia
DA  - 2013/06/23
PB  - ASEE Conferences
TI  - The Most Misunderstood ABET Criterion - Criterion 3b
UR  - https://peer.asee.org/22608
DO  - 10.18260/1-2--22608
ER  -