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Assessing the Data Analysis Training of Engineering Undergraduates

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2019 ASEE Annual Conference & Exposition


Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

ERM Technical Session 19: Thinking about the Engineering Curriculum

Tagged Division

Educational Research and Methods

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Paper Authors


Eunhye Kim Purdue University, West Lafayette

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Eunhye Kim is a Ph.D. student and research assistant in the School of Engineering Education at Purdue University. Her research interests lie in engineering design education, especially for engineering students’ entrepreneurial mindsets and multidisciplinary teamwork skills in design and innovation projects. She earned a B.S. in Electronics Engineering and an M.B.A. in South Korea and worked as a hardware development engineer and an IT strategic planner in the industry.

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Nathan M. Hicks Purdue University, West Lafayette Orcid 16x16

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Nathan M. Hicks is a Ph.D. student in Engineering Education at Purdue University. He received his B.S. and M.S. degrees in Materials Science and Engineering at the University of Florida and previously taught high school math, science, and engineering.

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Matilde Luz Sanchez-Pena Purdue University, West Lafayette

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Matilde Sanchez-Pena is a Visiting Assistant Professor of Engineering Education at Purdue University. She completed her Ph.D. at the same institution in 2018. Her dissertation explored differences across gender of faculty retention and promotion at research-intensive institutions. Dr. Sanchez-Pena aims to promote a more equitable engineering field, in which students of all backgrounds can acquire the knowledge and skills to achieve their goals. Before engaging in Engineering Education research, she completed graduate degrees in Industrial Engineering and Statistics and contributed to a wide range of research areas including genetic disorders, manufacturing optimization, cancer biomarker detection, and the evaluation of social programs.

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The need for acquiring data analysis skills is nowadays ubiquitous to all professions. In engineering, this need has been recognized through elements such as the current ABET student outcome 3.b. which expect engineering graduates to have “an ability to design and conduct experiments, as well as to analyze and interpret data.” While this outcome is a requirement of engineering programs, the length and depth of the data analysis training of undergraduate students vary significantly across engineering majors. The evolution in the capacity to produce and storage data, requires an exploration of the status on data analysis training of engineers. We propose that such exploration can start through the research question: what data analysis training has been available and has been procured by engineering undergraduate students? In this work, we aim to answer this question through a mixed methods approach, in which the courses available to engineering students are first coded according to their data analysis content. A comprehensive database with the records of courses taken by engineer students between 1987 and 2011 at two public institutions is then used to generate profiles reflecting different levels of data analysis preparation that students have engaged with. Results from this study will provide the baseline for evaluating if the training of engineers is satisfying the demands of employers, especially as it relates to the expanding employment opportunities related to data analysis skills. Through the generation of profiles, similarities and differences in the data analysis preparation across different engineering majors will come to the fore, as a first stage for potential programmatic evaluations and changes.

Kim, E., & Hicks, N. M., & Sanchez-Pena, M. L. (2019, June), Assessing the Data Analysis Training of Engineering Undergraduates Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32119

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