Engineering Vocabulary Development Using an Automated Software Tool

Chirag Variawa; Susan McCahan

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Conference: 2014 ASEE Annual Conference & Exposition
Location: Indianapolis, Indiana
Publication Date: June 15, 2014
Start Date: June 15, 2014
End Date: June 18, 2014
ISSN: 2153-5965
Conference Session: Innovative Use of Technology and the Internet in Engineering Education
Tagged Division: Educational Research and Methods
Page Count: 13
Page Numbers: 24.513.1 - 24.513.13
DOI: 10.18260/1-2--20404
Permanent URL: https://peer.asee.org/20404
Download Count: 349

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Paper Authors

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Chirag Variawa University of Toronto

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Chirag Variawa is an accelerated-stream Ph.D. Candidate in the Department of Mechanical and Industrial Engineering at the University of Toronto. He earned his B.A.Sc. in Materials Science Engineering in 2009 from the same institution. He is the first Graduate Student member of the University of Toronto Governing Council elected from Engineering. His multi-disciplinary research uses principles from artificial intelligence, computational linguistics, higher education and aspects of neuroscience to investigate the design of engineering learning environments.

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Susan McCahan University of Toronto

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Susan McCahan is a Professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. She currently holds the position of Vice Dean, Undergraduate in the Faculty of Applied Science and Engineering. She received her B.S. (Mechanical Engineering) from Cornell University, and M.S. and Ph.D. (Mechanical Engineering) from Rensselaer Polytechnic Institute. She is a Fellow of the American Association for the Advancement of Science in recognition of contributions to engineering education has been the recipient of several major teaching and teaching leadership awards including the 3M National Teaching Fellowship and the Medal of Distinction in Engineering Education from Engineers Canada.

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Abstract

Engineering Vocabulary Development using an Automated Software ToolUnderstanding domain-specific vocabulary is often a learning objective in higher education, anda significant part of professional communication in the engineering profession. Language used inengineering education plays a key role in creating an accessible and inclusive learningenvironment. The corpus of language common to both the instructor and student ought toconverge as the student masters the course content. Instructors may currently use techniques tohelp identify this vocabulary, including referring to glossaries and increasing the frequency oftheir use in the classroom. The challenge here is to increase transparency and accessibility tosuch vocabulary by developing an automated software-based tool that can be used by allinstructors to create customized course-specific wordlists for their courses. Using text extractedfrom instructional material in a course, the program is able to hierarchically identify and displaycourse-specific terminology using principles from artificial intelligence, linguistics, highereducation, and industrial engineering. Grounded in the theory of Universal Instructional Design,these wordlists can be integrated into a syllabus and then be used as a teaching aid to promote anaccessible engineering education. The goal is to reduce barriers to learning by developing anexplicitly-identified and robust list of vocabulary for all students; creating an automated programthat improves vocabulary information over time keeps it relevant and usable by instructors aswell as students.Presently, there is no automated method to develop course-specific vocabulary lists. As a result,the authors have created a computer program, using a repository of over 2800 engineering examssince the year 2000 from a large North American university, which automatically identifiesdomain-specific terms on any engineering exam. Specifically, each word from each exam isdigitized and computed against others using a modified form of the Term-Frequency InverseDocument-Frequency (TF-IDF) algorithm to generate lists of context-specific characteristicterms. This well-known algorithm is used in the field of computational linguistics as a methodof identifying words characteristic to a document, given a comparator set of documents. In thisstudy, the modified approach uses several comparator sets to develop a sense of context, which isused to produce a more accurate list of engineering vocabulary. Then, the efficacy of theprogram in developing these lists is evaluated by subject-matter experts using a quantitativestudy method. This paper will use the data gathered to discuss the efficacy of this automatedprogram in the context of engineering research methods, and will identify ways in which to makethis program accessible to, and usable by, more educators in the field of engineering education.

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Variawa, C., & McCahan, S. (2014, June), Engineering Vocabulary Development Using an Automated Software Tool Paper presented at 2014 ASEE Annual Conference & Exposition, Indianapolis, Indiana. 10.18260/1-2--20404

TY  - CPAPER
AB  -             Engineering Vocabulary Development using an Automated                                Software ToolUnderstanding domain-specific vocabulary is often a learning objective in higher education, anda significant part of professional communication in the engineering profession. Language used inengineering education plays a key role in creating an accessible and inclusive learningenvironment. The corpus of language common to both the instructor and student ought toconverge as the student masters the course content. Instructors may currently use techniques tohelp identify this vocabulary, including referring to glossaries and increasing the frequency oftheir use in the classroom. The challenge here is to increase transparency and accessibility tosuch vocabulary by developing an automated software-based tool that can be used by allinstructors to create customized course-specific wordlists for their courses. Using text extractedfrom instructional material in a course, the program is able to hierarchically identify and displaycourse-specific terminology using principles from artificial intelligence, linguistics, highereducation, and industrial engineering. Grounded in the theory of Universal Instructional Design,these wordlists can be integrated into a syllabus and then be used as a teaching aid to promote anaccessible engineering education. The goal is to reduce barriers to learning by developing anexplicitly-identified and robust list of vocabulary for all students; creating an automated programthat improves vocabulary information over time keeps it relevant and usable by instructors aswell as students.Presently, there is no automated method to develop course-specific vocabulary lists. As a result,the authors have created a computer program, using a repository of over 2800 engineering examssince the year 2000 from a large North American university, which automatically identifiesdomain-specific terms on any engineering exam. Specifically, each word from each exam isdigitized and computed against others using a modified form of the Term-Frequency InverseDocument-Frequency (TF-IDF) algorithm to generate lists of context-specific characteristicterms. This well-known algorithm is used in the field of computational linguistics as a methodof identifying words characteristic to a document, given a comparator set of documents. In thisstudy, the modified approach uses several comparator sets to develop a sense of context, which isused to produce a more accurate list of engineering vocabulary. Then, the efficacy of theprogram in developing these lists is evaluated by subject-matter experts using a quantitativestudy method. This paper will use the data gathered to discuss the efficacy of this automatedprogram in the context of engineering research methods, and will identify ways in which to makethis program accessible to, and usable by, more educators in the field of engineering education.
AU  - Chirag Variawa
AU  - Susan McCahan
CY  - Indianapolis, Indiana
DA  - 2014/06/15
PB  - ASEE Conferences
TI  - Engineering Vocabulary Development Using an Automated Software Tool
UR  - https://peer.asee.org/20404
DO  - 10.18260/1-2--20404
ER  -