Author
Author Technology and Engineering Teacher - Volume 78, Issue 2 - October, 2018
PublisherITEEA, Reston, VA
ReleasedSeptember 25, 2018
Copyright@2018
ISBN2158-0502
Technology and Engineering Teacher - Volume 78, Issue 2 - October 2018

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EXCELLING IN ENGINEERING: Validating the Value Proposition of Engineering Design Problems through Quantitative Analysis

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The purpose of this article is to provide educators with resources to help students establish a deeper understanding of the application and role of statistical analysis within the design and innovation process. Quantitative analyses are often taught and applied through design activities, especially during testing or experimenting phases of design. However, we posit that quantitative analytics and statistical procedures are also extremely useful during the early phases of the design process (i.e., problem validation, customer analysis, problem framing). Also, we believe that when quantitative analyses are conducted within the early stages of design, the related engineering concepts are more closely connected to the human element of design. This can potentially provide more culturally relevant and authentic contexts for students to learn essential engineering concepts and skills such as the proper application of statistics to validate a problem or the economic feasibility of potential design solutions.

As a result, the lesson resources provided here can present students the opportunity to identify, define, and validate potential design problems/opportunities before attempting to design a product, thus potentially saving time, energy, and other resources. To do so, the students can be intentionally taught concepts related to quantitative analyses while linking them to entrepreneurial thinking in terms of recognizing the value proposition of potential solutions. The Advancing Excellence in P-12 Engineering (AEEE) project identified Engineering Statistics and Probability as one of the core engineering concepts fundamental for setting the foundation for students to conduct the quantitative analyses that engineers and other related professionals perform (Strimel et al., 2018). In addition, the AEEE project highlights entrepreneurial thinking as a fundamental engineering subconcept for enabling students to better frame problems, address customer needs, and plan for the exploitation of technological innovations. The instructional materials provided in this article have been designed to address these fundamental engineering concepts while being flexible with respect to project length (e.g., one-time, semester long, etc…), project assessment method (e.g., report, PowerPoint, poster, video, prototype, etc…), and project concepts (e.g., description statistics, regression, hypothesis testing, etc.), depending on the needs of the students.

 


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Fundamental Engineering Core Concepts and Subconcepts

The AEEE project established a taxonomy of engineering knowledge that includes a variety of content areas, two of which are Quantitative Analysis and Ethics and Society. Each of these content areas is comprised of a series of core concepts and subconcepts deemed important for all students to learn in order  to become engineering literate. Within these content areas are the core concepts of Engineering Statistics and Probability and Engineering-Related Careers. The Engineering Statistics and Probability core concept is comprised of the subconcepts of (a) recognizing, selecting, and applying appropriate probability and statistics concepts and practices, (b) basic statistics (normal distributions, percentiles), (c) probability, (d) regression, and (e) inferential statistics and tests of significance (e.g., t-tests, statistical tolerance).

The Engineering-Related Careers core concept is comprised of the subconcepts of (a) career recognition, (b) trade organizations, (c) professional licensing, and (d) entrepreneurship. In addition, sample progressions of learning to integrate these concepts into future or existing engineering coursework were created in an effort to deepen students’ engineering design practices and ultimately increase their abilities to produce optimized solutions to authentic problems (Tables 1 and 2). Relatedly, the instructional resources provided in Tables 3 and 4 have been developed to address the concepts of Engineering Statistics and Probability (Basic descriptive statistics, regression, and tests for significance) and Engineering-Related Careers (Entrepreneurship).

 

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References

Bosman, L. & Fernhaber, S. (2017). Teaching entrepreneurial mindset to engineers. Berlin, Germany: Springer.

Kriewall, T. J. & Mekemson, K. (2010). Instilling the entrepreneurial mindset into engineering undergraduates. Journal of Engineering Entrepreneurship, 1(1), 5-19.

Stevens, G. & Burley, J. (1997). 3,000 raw ideas = 1 commercial success! Research-Technology Management, 40(3), 16-27.

Strimel, G. J., Grubbs, M. E., Huffman, T. J., & Bartholomew, S. R. (2018). Establishing progressions of learning in engineering for high school students. Paper presented at the 2018 Pupils Attitudes Towards Technology Conference, Atholone, Ireland.

 

Lisa B. Bosman, Ph.D., works in the Purdue Polytechnic Institute at Purdue University, located in West Lafayette, IN. She can be reached at lbosman@purdue.edu.

Steve O’Brien, Ph.D., is a professor in the Department of Integrative-STEM (i-STEM) Education and Director of the Center for Excellence in STEM Education at the College of New Jersey, located in Ewing, New Jersey. He can be reached at obriens@tcnj.edu.

Susheela Shanta, Ph.D., is a faculty member and Director for the Engineering Program at the Governor’s STEM Academy@BCAT, located in Roanoke, Virginia. She can be reached at sshanta@vt.edu.

Greg J. Strimel, Ph.D, is an assistant professor of Technology Leadership and Innovation at Purdue University and serves as a Co-Director for the Advancing Excellence in P-12 Engineering Education initiative. He can be reached at gstrimel@purdue.edu.