Developing Machine-Assisted Analysis of Engineering Students' Ethics Course Assignments
- Conference
- Location
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Columbus, Ohio
- Publication Date
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June 24, 2017
- Start Date
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June 24, 2017
- End Date
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June 28, 2017
- Conference Session
- Tagged Division
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Engineering Ethics
- Page Count
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10
- DOI
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10.18260/1-2--28146
- Permanent URL
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https://strategy.asee.org/28146
- Download Count
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501
Paper Authors
Roman Taraban Texas Tech University
Roman Taraban is Professor in the Department of Psychological Sciences at Texas Tech University. He received his Ph.D. in cognitive psychology from Carnegie Mellon University. His interests are in how undergraduate students learn, and especially, in critical thinking and how students draw meaningful connections in traditional college content materials.
Mark Stephen LaCour Jr. Texas Tech University
Mark is a doctoral student in the Department of Psychological Sciences at Texas Tech University. He completed his thesis on the role of calculator usage on mathematical cognition at the University of Louisiana at Lafayette.
William M. Marcy P.E. Texas Tech University
Professor and Director of the Murdough Center for Engineering Professionalism and The National Institute for Engineering Ethics
Texas Tech University
Lubbock, Texas
Richard A. Burgess II Texas Tech University
Richard Burgess currently works as an Instructor in the Murdough Center for Engineering Professionalism (MCEP) and National Institute for Engineering Ethics (NIEE) in the Whitacre College of Engineering at Texas Tech University. He oversees the day to day operations of the Center’s distance learning courses for both engineering students and practicing engineers. Additionally, he teaches an on-campus ethics course for undergraduate students. Burgess provides guest lectures on ethics throughout the Whitacre College of Engineering.
Burgess has also worked to incorporate ethics into K-12 STEM education. The push to increase the number of students pursuing STEM careers needs to be accompanied by a sophisticated understanding of the complexity of technology. Ethics is a key part of this complexity and the next generation of STEM professionals will need the skills to effectively engage the ethical challenges they will face. Burgess is a regular presenter on incorporating ethics in a K-12 setting.
A theme throughout these roles is the importance of teaching ethics and promoting ethical reflection in a way that is both accessible and substantive. This is a challenge that Burgess is keenly interested in. He holds bachelor’s and master’s degrees in Philosophy and is currently a PhD student in Systems and Engineering Management program in the Texas Tech Industrial Engineering Department.
Abstract
Our research concerns engineering ethics education. We were drawn to this topic by a recent paper titled “Do Ethics Classes Teach Ethics?”, but more so by ABET criteria 3f and 3h regarding the development of ethical responsibility in engineering students. The purpose of the present project is to use the learning and analytical capabilities of IBM Watson Natural Language Classifier to analyze capstone papers submitted by undergraduates in a course on engineering ethics. The capstone papers that we analyzed required students to identify and discuss a contemporary engineering technology (e.g., autonomous tractor trailers) and to explicitly discuss the ethical issues involved. In the two tests described here we assessed whether Watson-NLC could classify sentences from students’ papers as either related to ethics or not related to ethics. Additionally, we consider the utility of these simple machine-based classifications. Our longer-term goals are to use Watson-NLC to identify the ethical theory or theories from the course that students adopt to frame their ethical positions, to assess the effectiveness of students’ ethical arguments, and to assess changes in ethical thinking across the semester.
Taraban, R., & LaCour, M. S., & Marcy, W. M., & Burgess, R. A. (2017, June), Developing Machine-Assisted Analysis of Engineering Students' Ethics Course Assignments Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28146
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