San Antonio, Texas
June 10, 2012
June 10, 2012
June 13, 2012
2153-5965
First-Year Programs
10
25.337.1 - 25.337.10
10.18260/1-2--21095
https://peer.asee.org/21095
444
Chirag Variawa is a 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. His multi-disciplinary research uses principles from artificial intelligence, computational linguistics, higher-education, and aspects of neuroscience to investigate inclusive design of engineering learning environments.
Susan McCahan is a professor in the Department of Mechanical and Industrial Engineering at the University of Toronto. In addition, she is currently the Vice Dean, Undergraduate for the Faculty of Applied Science and Engineering. She received her B.Sc. from Cornell University (1985), and M.S. (1989) and Ph.D. (1992) degrees from Rensselaer Polytechnic Institute in mechanical engineering.
Computational Method for Identifying Inaccessible Vocabulary in Engineering Educational MaterialsInstructors often face the challenge of making students feel more included in the classroom,especially in freshmen classes of engineering. In the freshman classroom, instructors are moreoften finding that their students are departing from the “traditional” homogenous demographicsof engineering in the past. More recently, engineering classrooms have better representationfrom all genders, cultural and socio-economic backgrounds and even greater variance inapproaches to learning leading to greater diversity. In order to make freshman engineeringclasses more inclusive, the authors investigate the language used in engineering teachingmaterials. The goal is the development of an automated technique to make to make educationalmaterials more accessible for all students.Final examinations are a standardized artefact of the engineering classroom whose purpose is toassess the student’s understanding of course material. However, the vocabulary used in creatingauthentic engineering problems on such assessments may cause an inaccessible and exclusiveenvironment for some students. Specifically, the vocabulary used in such assessments might beunclear or foreign to the learner. In addition if understanding this specific vocabulary is not alearning objective for the course, then the performance assessment isn’t measuring mastery ofcourse material but rather understanding of this specific vocabulary. In this study, the authorsinvestigate a very large dataset of electronically available engineering exams at a large NorthAmerican university. In particular, methods from fields such as computer science and linguisticsare used to determine an approach to electronically identify potentially inaccessible non-course-specific vocabulary and flag them for the instructor a priori.In this paper, the authors discuss how keyword generating tools might be used to identifydiscipline-specific vocabulary and retain them during the search for inaccessible words. Then,the authors identify trends in wordlists generated from millions of words mined from anextensive dataset of engineering exams over the course of 5+ years. This dataset is thenanalyzed to see how the language of some engineering disciplines compares to others; howfreshmen classes differ from senior years; and how engineering language has evolved over time.This information can then be further investigated to inform techniques that lead to more inclusivelearning environments and valid assessments as a result of using more accessible language, whilemaintaining the integrity of the course content.
Variawa, C., & McCahan, S. (2012, June), Computational Method for Identifying Inaccessible Vocabulary in Engineering Educational Materials Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. 10.18260/1-2--21095
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