Loose Change and Dishwasher Optimization: Creative Applications of Engineering Statistics

Matthew Cooper

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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: "Modular" Learning
Tagged Division: Chemical Engineering
Page Count: 10
Page Numbers: 23.878.1 - 23.878.10
DOI: 10.18260/1-2--22263
Permanent URL: https://strategy.asee.org/22263
Download Count: 389

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Matthew Cooper North Carolina State University - Department of Chemical and Biomolecular Engineering orcid.org/0000-0003-1060-4628

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Dr. Matthew Cooper was born in Elkins, W.Va. and received his B.S. in Chemical Engineering from West Virginia University in 2002. Following a position as an oilfield engineer with Schlumberger, Dr. Cooper attended Ohio University for graduate work. His M.S. research with Dr. Gerardine Botte focused on the electrochemical production of hydrogen from ammonia for PEM fuel cell applications. Dr. Cooper continued his Ph.D. work under the guidance of Dr. David Bayless at Ohio University, developing novel catalysts for the efficient production of electricity by solid oxide fuel cells. After receiving his Ph.D. in Chemical Engineering in 2008, Dr. Cooper moved to the Raleigh area to serve as a research chemical engineer for RTI International, focusing on energy research. In January 2011, Dr. Cooper joined the Department of Chemical and Biomolecular Engineering at North Carolina State University, where he currently teaches Transport Phenomena and the Unit Operations I and II laboratory sequence.

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Abstract

Loose Change and Dishwasher Optimization: Creative Applications of Engineering StatisticsStatistical concepts such as design of experiments (DOE) serve an important role incontemporary engineering, forming the basis of business management methods such as SixSigma. Though statistical analysis has many important applications, evaluating engineeringstudents’ understanding of statistical concepts in the classroom often relies on surface-typeapproaches. For instance, a typical DOE assignment involves providing experimental data froman already-performed study to students, who then complete data analysis by following roteproblem-solution procedures. While this approach can be used to evaluate students’ applicationof statistical procedures to engineering data, students often struggle to understand therelationship between statistical design and real-life processes since they do not actually “design”the experiment, execute the design, then draw conclusions based on statistical analysis.An alternative assignment for evaluating student understanding of statistical methods, namely theDOE concept, is presented in this study. In the proposed assignment, students are asked toevaluate any process and factors they desire via a 2-level 3-factor full factorial DOE method. Theonly stipulation on the chosen process is that it must provide an objective, quantitative response.To illustrate, students have chosen to examine the process of cooling a room-temperaturebeverage, evaluating the effect of factors such as the container material (can or bottle), coolingmethod (refrigerator or freezer) and cooling time (20 minutes or 40 minutes) on the finalbeverage temperature (an objective, quantitative response). Other examples of student-chosenprocesses include the length of time elapsed before someone picks up loose change on the floorof a busy building, or the ideal settings for an old dishwasher in their apartment. Students areencouraged to evaluate a process with which they are familiar; by allowing students to choosethe process they are evaluating, students are able to relate the subject of statistical methods totheir lives and interests, demonstrating the relevance of the concepts.Pedagogical research has found that writing assignments effectively facilitate learning by forcingstudents to explore connections and patterns in the studied material. These benefits of writingassignments are enhanced in fields such as engineering, since students are rarely assignedreflective writing tasks and thus have few opportunities to develop associated abilities. Withthese factors in mind, students are asked to summarize their DOE study and findings in a brieftwo-page report. The report presents the students’ experimental design and findings, includingdevelopment and application of a statistical model able to predict system response for untestedvariables. As part of this analysis, students prioritize which factors are most / least important tosystem response, as well as identify what they learned about their tested system that they did notknow before. Key goals of this assignment are to (1) increase subject relevance, (2) improvecritical thinking skills, and (3) develop and strengthen creativity in order to encourage higher-order thinking skills of Bloom’s Taxonomy such as analyzing, evaluating and creating.Request “Regular” ASEE Session

Citation
Format

Cooper, M. (2013, June), Loose Change and Dishwasher Optimization: Creative Applications of Engineering Statistics Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--22263

TY  - CPAPER
AB  -      Loose Change and Dishwasher Optimization: Creative Applications of                          Engineering StatisticsStatistical concepts such as design of experiments (DOE) serve an important role incontemporary engineering, forming the basis of business management methods such as SixSigma. Though statistical analysis has many important applications, evaluating engineeringstudents’ understanding of statistical concepts in the classroom often relies on surface-typeapproaches. For instance, a typical DOE assignment involves providing experimental data froman already-performed study to students, who then complete data analysis by following roteproblem-solution procedures. While this approach can be used to evaluate students’ applicationof statistical procedures to engineering data, students often struggle to understand therelationship between statistical design and real-life processes since they do not actually “design”the experiment, execute the design, then draw conclusions based on statistical analysis.An alternative assignment for evaluating student understanding of statistical methods, namely theDOE concept, is presented in this study. In the proposed assignment, students are asked toevaluate any process and factors they desire via a 2-level 3-factor full factorial DOE method. Theonly stipulation on the chosen process is that it must provide an objective, quantitative response.To illustrate, students have chosen to examine the process of cooling a room-temperaturebeverage, evaluating the effect of factors such as the container material (can or bottle), coolingmethod (refrigerator or freezer) and cooling time (20 minutes or 40 minutes) on the finalbeverage temperature (an objective, quantitative response). Other examples of student-chosenprocesses include the length of time elapsed before someone picks up loose change on the floorof a busy building, or the ideal settings for an old dishwasher in their apartment. Students areencouraged to evaluate a process with which they are familiar; by allowing students to choosethe process they are evaluating, students are able to relate the subject of statistical methods totheir lives and interests, demonstrating the relevance of the concepts.Pedagogical research has found that writing assignments effectively facilitate learning by forcingstudents to explore connections and patterns in the studied material. These benefits of writingassignments are enhanced in fields such as engineering, since students are rarely assignedreflective writing tasks and thus have few opportunities to develop associated abilities. Withthese factors in mind, students are asked to summarize their DOE study and findings in a brieftwo-page report. The report presents the students’ experimental design and findings, includingdevelopment and application of a statistical model able to predict system response for untestedvariables. As part of this analysis, students prioritize which factors are most / least important tosystem response, as well as identify what they learned about their tested system that they did notknow before. Key goals of this assignment are to (1) increase subject relevance, (2) improvecritical thinking skills, and (3) develop and strengthen creativity in order to encourage higher-order thinking skills of Bloom’s Taxonomy such as analyzing, evaluating and creating.Request “Regular” ASEE Session
AU  - Matthew Cooper
CY  - Atlanta, Georgia
DA  - 2013/06/23
PB  - ASEE Conferences
TI  - Loose Change and Dishwasher Optimization: Creative Applications of Engineering Statistics
UR  - https://strategy.asee.org/22263
DO  - 10.18260/1-2--22263
ER  -