Machine-assisted analysis of communication in environmental engineering

Roman Taraban, David Robledo, Francesco V. Donato, Ryan C. Campbell, Jeong Hee Kim, Danny D. Reible, Chongzheng Na

Research output: Contribution to journalConference articlepeer-review

Abstract

ABET is committed to promoting the broad development of engineering students, including knowledge of the social, cultural, environmental, and global implications of engineering practice. Coincident with the cultural shift within engineering education is a scholarly interest in the formal and informal communications of engineers and students. The present study was situated in a graduate course in environmental engineering that incorporated the arts and humanities with the goal of developing personal and professional reflection in students. The goal of this paper is to describe and analyze two methods of machine-based formative assessment of students' essays written in response to lectures and activities that related to art and narrative within the course. The two machine-based tools used here were i) naïve Bayes analysis and ii) Meaning Extraction Helper. The results showed that both tools were able to identify differences in student essays. We suggest several ways in which these machine-based methods could be extended to aid in assessing learning and reflective thinking in students.

Original languageEnglish
Article number978
JournalASEE Annual Conference and Exposition, Conference Proceedings
Volume2020-June
StatePublished - Jun 22 2020
Event2020 ASEE Virtual Annual Conference, ASEE 2020 - Virtual, Online
Duration: Jun 22 2020Jun 26 2020

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