Poster #70 - Mindful Language and Inclusive Policy: Investigating Biased Language in Engineering Education Research

• In Event: Friday Poster Reception

Abstract

Language plays a crucial role in shaping equitable and inclusive academic environments.
National style and language guidelines, such as those from the American Psychological
Association (APA), provide instruction on language usage that offers practical suggestions and
highlights examples of biased language commonly found in academic writing. In this academic
atmosphere, the engineering education community is increasingly recognizing that language use
is one of the essential components of creating an inclusive and equitable learning
environment. Although efforts to promote inclusive communication have gained momentum in
terms of policy perspective, limited empirical evidence exists on how these practices are
reflected in published scholarship. The primary aim of this study is to investigate the impact of
bias-free language guidelines within the field of engineering education. In recent years, bias-free
language has increasingly appeared as a policy trend and normative standard in academic
discourse and terminology.

In light of this, the present study integrates two conceptual frameworks: implicit bias theory and
academic literacy theory. This approach allows for a detailed investigation into biased language
use trends within engineering education research, as well as an understanding of how these
trends diverge from the field’s goals of diversity and inclusion. Implicit bias theory examines
unconscious attitudes and stereotypes that subtly but significantly influence language use in
academic settings. Meanwhile, academic literacy theory sheds light on the conventions and
practices of communication in academic writing. To determine what constitutes biased language,
we developed a keyword-based model in accordance with the latest APA 7th language
guidelines. We identified 85 keywords from the guide and grouped them into seven categories.
The study analyzed 5,237 conference proceedings published at the American Society for
Engineering Education Annual Meetings from 2020 to 2022. By employing the keyword-based
model in R, we applied the keyword-based model to detect instances of biased language within
the proceedings. Our analysis revealed a slight decrease in biased language usage over time, with
359 unique instances detected in 2020, compared to 283 instances in 2022. The three most
persistent categories of biased language over the three years were Gender, Racial and Ethnic
Identity, and Socioeconomic Status. Specifically, the top five most frequently used biased terms
were Females/Males; Caucasian; Achievement Gap; The Poor; and The Elderly.

These findings highlight the importance of evidence-based policy interventions and inclusive
editorial practices in promoting equitable academic discourse, in particular, in disciplines where
relevant norms are still evolving. The formation and development of such guidelines are
inherently complex, which underscores the significance of these practices in fostering inclusivity
within scholarly communication. To some extent, the results suggest that within the introduction
of biased-free language guidelines and the growing emphasis on inclusive language within
educational environments, shifting are indeed occurring. Scholars are becoming more attuned to
their language choices and are increasingly adopting bias-free language. However, despite these
improvements, biased language remains relatively common within the engineering education
discipline. This indicates a continued need for stronger academic policies and awareness
initiatives to ensure that inclusive language becomes a standard across all fields.

Author

• Li Tan, Arizona State University
  Presenting Author