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Board # 38 : Work in Progress: Quantification of Learning through Learning Statements and Text Mining

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Design in Engineering Education Division Poster Session

Tagged Division

Design in Engineering Education

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Paper Authors


Jackson Lyall Autrey University of Oklahoma Orcid 16x16

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Jackson L. Autrey is a Master of Science student in Mechanical Engineering at the University of Oklahoma from Tulsa, Oklahoma. He holds a Bachelor of Science in Mechanical Engineering from the University of Oklahoma and currently is involved with research into design-based engineering education. After completion of his Master’s degree, Jackson plans to pursue a Ph.D. in Mechanical Engineering.

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Jennifer M. Sieber University of Oklahoma

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Jennifer M. Sieber recently graduated with a Master of Science degree in Data Science and Analytics from the University of Oklahoma. She is currently employed full time as a Data Scientist. Her research interests include text mining, machine learning, and data analytics.

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Zahed Siddique University of Oklahoma

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Zahed Siddique is a Professor of Mechanical Engineering at the School of Aerospace and Mechanical Engineering of University of Oklahoma. His research interest include product family design, advanced material and engineering education. He is interested in motivation of engineering students, peer-to-peer learning, flat learning environments, technology assisted engineering education and experiential learning. He is the coordinator of the industry sponsored capstone from at his school and is the advisor of OU's FSAE team.

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Farrokh Mistree University of Oklahoma

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Farrokh’s passion is to have fun in providing an opportunity for highly motivated and talented people to learn how to define and achieve their dreams.

Farrokh Mistree holds the L. A. Comp Chair in the School of Aerospace and Mechanical Engineering at the University of Oklahoma in Norman, Oklahoma. Prior to this position, he was the Associate Chair of the Woodruff School of Mechanical Engineering at Georgia Tech – Savannah. He was also the Founding Director of the Systems Realization Laboratory at Georgia Tech.

Farrokh’s current research focus is model-based realization of complex systems by managing uncertainty and complexity. The key question he is investigating is what are the principles underlying rapid and robust concept exploration when the analysis models are incomplete and possibly inaccurate? His quest for answers to the key question are anchored in three projects, namely,

Integrated Realization of Robust, Resilient and Flexible Networks

Integrated Realization of Engineered Materials and Products

Managing Organized and Disorganized Complexity: Exploration of the Solution Space

His current education focus is on creating and implementing, in partnership with industry, a curriculum for educating strategic engineers—those who have developed the competencies to create value through the realization of complex engineered systems.


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How can instructors leverage assessment instruments in design, build, and test courses to simultaneously improve student outcomes and assess student learning well enough to improve courses for future students?

We recognize the need for a framework for shaping and assessing student learning. In that spirit, we contend that in design, build, and test courses students learn when they are required to reflect on their experiences and identify their learning explicitly. Further, we posit that utilization of an assessment instrument, the learning statement (LS), can be used to both enable and assess student learning.

In our course, AME4163: Principles of Engineering Design, a senior-level, pre-capstone, engineering design course, students learn by reflecting on doing by writing statements anchored in Kolb’s experiential learning cycle. In Fall 2016 we collected over 11,000 learning statements from over 150 students. To address the challenge of analyzing and gleaning knowledge from the large number of learning statements we resorted to text mining to analyze student-submitted learning statements.

We assert that text mining empowers instructors to better assess student learning in design, build, and test courses. Utilizing current algorithms for identifying writing patterns, we form a picture of learning over the course of the semester. Employing this tool, we can quantify student learning through reflection on doing and improve the course for future offerings. We find that student insight is characterized by focusing on the future utility of learning, particularly in areas such as planning a design process and evaluating design concepts. In this paper, we cover the salient features of the course, the learning statements, text mining and initial findings.

Autrey, J. L., & Sieber, J. M., & Siddique, Z., & Mistree, F. (2017, June), Board # 38 : Work in Progress: Quantification of Learning through Learning Statements and Text Mining Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--27843

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