Developing machine-assisted analysis of engineering students' ethics course assignments

Roman Taraban, Mark Stephen LaCour, William M. Marcy, Richard A. Burgess

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

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.

Original languageEnglish
JournalASEE Annual Conference and Exposition, Conference Proceedings
Volume2017-June
StatePublished - Jun 24 2017
Event124th ASEE Annual Conference and Exposition - Columbus, United States
Duration: Jun 25 2017Jun 28 2017

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