Crystal City, Virginia
April 14, 2019
April 14, 2019
April 22, 2019
Diversity and Special Topic: Computing & Technology
Keywords: Engineering, undergraduate, graduate, faculty There is resurging interest in the presence and impact of implicit bias in both formal and informal engineering environments. Implicit bias refers to the unconscious associations and stereotypes an individual ascribes based on affiliation with a particular identity that impacts attitudes, actions, and behaviors. Though individuals may hold egalitarian views, they can still act in ways that reflect an implicit bias that is incongruent with their greater beliefs and/or intentions. While literature and tests on implicit bias exists, to our knowledge, a method to specifically gauge biases that exist in the perceptions and dynamics affecting engineering environments does not.
This study introduces a novel application of biometric testing to gain insight and direct evidence into the biases that exist among faculty and students engaging in engineering environments. Specifically, informed by literature on microaggressions and implicit bias, an eye-tracking paradigm is used to draw evidence on existing biases related to sexism, ageism, racism, ableism, classism and xenophobia. In this study, when prompted, participants are asked to make choices from a pool of options based on the information presented in a specific scenario. During this selection, the participant's eye movements, specifically their fixation regions and times, are collected to later corroborate with their selections and identify the presence of bias. Preliminary findings from this study enable the identification of individual specific implicit biases that exist. The insights of this work could complement efforts to create awareness of bias in moving toward the adoption of attitudes and behaviors more conducive for cultivating inclusive environments.
Scott, K. D., & Green, K., & Coley, B. C. (2019, April), Can Eye Tracking Detect Implicit Bias Among People Navigating Engineering Environments? Paper presented at 2019 CoNECD - The Collaborative Network for Engineering and Computing Diversity , Crystal City, Virginia. https://peer.asee.org/31747
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