Engineering Vocabulary Development Using an Automated Software Tool

Understanding technical vocabulary is often a desired learning outcome in engineering education, and a significant part of professional communication in the engineering profession. Language used in engineering education plays a key role in creating an accessible and inclusive learning environment. The corpus of language common to both the instructor and student ought to converge as the student masters the course content. Instructors may currently use techniques to help identify this vocabulary, including referring to glossaries and increasing the frequency of their use in the classroom. There is an opportunity to increase transparency and accessibility to such vocabulary by developing an automated software-based tool that can be used by instructors to create customized course-specific wordlists for their courses. Using text extracted from instructional material in a course, the algorithm developed for this study is able to hierarchically identify and display course-specific terminology using principles from artificial intelligence, linguistics, higher education, 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 an accessible engineering education. The goal is to reduce barriers to learning by developing an explicitly-identified and robust list of vocabulary for all students in a given course. Creating an automated program that improves vocabulary information over time keeps it relevant and usable by instructors as well as students. Presently, there is no automated method to develop course-specific vocabulary lists. To fill this gap, the authors have created a computer program, using a repository of over 2200 engineering exams since the year 2000 from the University of Toronto, which automatically identifies domain-specific terms on any given engineering exam. Specifically, each word from each exam is digitized and computed against others using a modified form of the Term-Frequency Inverse Document-Frequency (TF-IDF) algorithm to generate lists of context-specific characteristic terms. This well-known algorithm is used in the field of computational linguistics as a method of identifying words characteristic to a document, given a comparator set of documents. In this work, a modified approach has been developed that uses several comparator sets to produce a list of engineering vocabulary for a course. The effectiveness of this approach is evaluated by comparing the results to the judgment of subject-matter experts. This paper will use the data gathered to discuss the efficacy of this automated program in the context of engineering research methods, and will identify ways in which to make this program accessible to, and usable by, more educators in the field of engineering education.