Abstract

ABSTRACTFor decades, American researchers have brought intellectual, financial and labor resources to understanding minority underrepresentation in engineering, including through studies of persistent racial and gender discrimination in higher engineering education. This paper considers prevailing standards for legitimate and significant research in this area and the persistent stigma associated with the study of small populations. The preference among many engineering education research producers and consumers for the ‘large-n’ brings with it presumptions about human differences including ideas of race, gender, disability and other categories by which subjects are customarily sorted for analytic purposes. This paper asks how such epistemic preferences enact power, showing how taxonomic inclinations may prevent incisive understanding of demographic privilege in U.S. higher technical education. We offer an illustrative contrast to such studies, describing a qualitative research project on underrepresented minorities in U.S. engineering schools, called ‘Learning from Small Numbers’. This project shows the analytic value of intersectional, Queer, and Disabilities Studies theories to interrogate inequity in engineering education. We argue that the reflexivity and indeterminacy supported by these theories illuminates the ruling relations of academic social sciences overall, while also reflecting on our own research preferences. There is no feature of an investigative project, including definitions of subject populations and choice of research methodology, that is not actively chosen by researchers, and it is the profound social consequences of these choices in equity-focused engineering education research that we want to consider.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.