Crystal Boyce

CIS 720 Seminar – A Talk on Critical Race Theory and Quantitative Analysis in Education Research: A Data Science Centric Investigation of the Perpetuation of whiteness

Please join us Monday April 8, 4:30-5:30 in Hamilton Library 3F, for a talk by CIS PhD student Crystal Boyce on Critical Race Theory and Quantitative Analysis in Education Research: A Data Science Centric Investigation of the Perpetuation of whiteness (purposefully lowercase).
 
In this seminar session, which is in partial fulfillment of the data science exam area, Crystal Boyce will describe her research on the development of frameworks for quantitative analyses based on critical race theory (CRT) in the field of education. Gloria Ladson-Billings and William Tate introduced CRT to education in 1995 and many authors have made it foundational to their work. A recent special issue of Race, Ethnicity, and Education (v. 21, i. 2, 2018) asked scholars to reflect specifically on the role of quantitative methodologies in educational studies, and one of those works provided the inspiration for this investigation. In this presentation, Crystal will discuss preliminary results where she visualized citation networks and performed text mining analyses. Her results suggest that whiteness perpetuates in scholarly literature, even when the focus is on race.

As a librarian at Illinois Wesleyan University, Crystal Boyce conducted research focused on academic library public services. Her publications include a data-focused investigation of the effect of a change in borrowing privileges on delinquent account management, an unobtrusive observation study of the two primary service desks in Ames Library, and a gap analysis of the expectations and perceptions of service at a joint IT-reference help desk. These studies grew out of her interest in library user experiences, specifically focusing on public services and service points. As a PhD student, Crystal is interested in exploring the intersections of libraries and information technology in higher education environments with a focus on how statistical analyses and algorithms reduce complexities and erase intersectional identities. Crystal is interested in studying how those reductions and erasures lead to discriminatory practices and policies in higher education environments, and what can be done about it.