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Designing Industrial Engineering Course Content and Delivery with an Understanding of the Learning Preferences and Factors Driving Satisfaction of Undergraduate Industrial Engineering Students

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2014 ASEE Annual Conference & Exposition


Indianapolis, Indiana

Publication Date

June 15, 2014

Start Date

June 15, 2014

End Date

June 18, 2014



Conference Session

Industrial Engineering Division Technical Session 1

Tagged Division

Industrial Engineering

Page Count


Page Numbers

24.379.1 - 24.379.17



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Paper Authors


Paul C. Lynch Pennsylvania State University, University Park, PA

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Paul C. Lynch received his Ph.D., M.S., and B.S. degrees in Industrial Engineering from the Pennsylvania State University. Dr. Lynch is a member of AFS, SME, IIE, and ASEE. Dr. Lynch’s primary research interests are in metal casting, manufacturing, and engineering education. Dr. Lynch has been recognized by Alpha Pi Mu, IIE, and the Pennsylvania State University for his scholarship, teaching, and advising. He received the Outstanding Industrial Engineering Faculty Award in 2011 and 2013 for his work in undergraduate education at Penn State. Dr. Lynch worked as a regional production engineer for Universal Forest Products prior to pursuing his graduate degrees. He is currently a Lecturer and Academic Adviser in the Harold and Inge Marcus Department of Industrial & Manufacturing Engineering at the Pennsylvania State University.

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Cynthia Bober Penn State University

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Cynthia Bober is a senior at Penn State University pursuing an Integrated M.S./B.S. Degree in Industrial Engineering with a minor in Six Sigma Methodology. As a Schreyer Honors Collegr scholar, she is writing her thesis in Engineering Education, specifically from a Learning Styles perspective. In the summer of 2013, Cyndy interned with the Walt Disney Company in the Workforce Management Department. As an intern, she was able to create a Variance Analysis Tool to monitor workload forecasting for the Walt Disney World resort.

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Jennifer Louise Mines The Pennsylvania State University

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Jennifer is a 2013 graduate of The Pennsylvania State University with a Master's degree in Industrial Engineering. Her Master's thesis examined undergraduate student satisfaction regarding Industrial Engineering education. Jennifer received her Bachelor's degree in Mathematics and certification in Secondary Education at Misericordia University. She is a certified Mathematics teacher for grades 7-12 in the state of Pennsylvania. She currently works in consulting as an analyst at Accenture.

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Designing Industrial Engineering Course Content and Delivery with an Understanding of the Learning Preferences and Factors Driving Satisfaction of Undergraduate Industrial Engineering Students This paper discusses the results of a study carried out to understand the learningpreferences, motivation, and satisfaction of junior and senior level industrial engineeringstudents. While researching the literature on engineering education it became clear that littlework exists that purely examines industrial engineering students, and virtually no work examinesindustrial engineering students’ learning styles, motivation, and satisfaction collectively. Thispaper will carefully examine the learning styles of industrial engineering undergraduate studentsand will study the course and instructional practices that motivate them, and in turn, bring themsatisfaction with their undergraduate education. The study has shown that the majority of industrial engineering students are active,sensing, visual, sequential learners according to the Felder Learning Styles assessment. Theresults of this study break down the characteristics of a satisfying and unsatisfying industrialengineering class. In a satisfying class, there is an increased emphasis on problem solvingsessions, group activities, hands-on activities, and demonstrations when compared to the modesof instruction that are commonly provided in an unsatisfying class. Class attendance wasincluded in grading much more frequently in a satisfying class, and attendance was not includedin grading as frequently in an unsatisfying class. More than half of students in an unsatisfyingclass specified that attending class did not help his or her understanding of class material.Students in an unsatisfying class often experienced modes of instruction that were not favorablewith their learning styles. It was found that the performance and preparation of the instructoroverwhelmingly drives student satisfaction. In a satisfying class students rated his or herinstructor as being excellent more than ninety-percent of the time. Student satisfaction among industrial engineering students is driven by several differentvariables. Student motivation is critical to student satisfaction, and it can be reinforced bytargeting student interest in class topics, showing passion for the course material, offering to helpstudents, and by creating a supportive classroom environment. Taking student learning stylesinto consideration by using a variety of instruction modes is also vital component of studentsatisfaction. Classroom design is also an important factor in student satisfaction. It is crucial thatinstructors make grading procedures clear, make assessments straight-forward, returnassignments to students in a timely manner with helpful feedback, and use “real-world”examples in class to help students clearly understand class material. These practices will helpinstructors give students the most satisfying experience with his or her education. Industrialengineering online education is a topic that has also received increased attention. The results ofthis study showed that more than ninety-percent of students chose the traditional in-class formatas his or her preferred class format versus online class format. Suggestions for designing industrial engineering course content and delivery both in classand on-line are made in an attempt to improve the industrial engineering education experienceand help keep students in industrial engineering through improved instructional methods andcourse delivery structure. Active 90.00% 80.00% Global 70.00% Refelective 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% Male Sequential 0.00% Sensing Female Verbal Intuitive VisualFigure 1: Spider graph displaying overall percentages of students in each learning style aftertaking the Felder Learning Styles Assessment. Red displays females only. Blue displays malesonly.

Lynch, P. C., & Bober, C., & Mines, J. L. (2014, June), Designing Industrial Engineering Course Content and Delivery with an Understanding of the Learning Preferences and Factors Driving Satisfaction of Undergraduate Industrial Engineering Students Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20270

ASEE holds the copyright on this document. It may be read by the public free of charge. Authors may archive their work on personal websites or in institutional repositories with the following citation: © 2014 American Society for Engineering Education. Other scholars may excerpt or quote from these materials with the same citation. When excerpting or quoting from Conference Proceedings, authors should, in addition to noting the ASEE copyright, list all the original authors and their institutions and name the host city of the conference. - Last updated April 1, 2015