Understanding Undergraduate Computing Student Perceptions of Race (2023-2024)

Background

Computer science (CS) is overwhelmingly dominated by white and Asian, able-bodied, middle- to upper-class cisgender men. Effects of this lack of diversity are evident in academic and workplace cultures and biased, harmful technologies that negatively impact nondominant identities (e.g., facial recognition, predictive policing, healthcare and financial software). In CS, this racial “othering” is apparent through not only biased technologies but also courses, departments and organizational cultures. 

The duality of invisibility and hypervisibility of historically underrepresented groups (both as technology creators and consumers) presents several challenges that extend beyond uncomfortable and into life-threatening. The events of 2020, including the COVID-19 pandemic and the national spotlight on systemic racism, highlighted the consequences of excluding race, racism and white supremacy from both societal and CS conversations.

While numerous efforts to broaden the participation of Black, Indigenous, Native Hawaiian/Pacific Islander and Hispanic/Latinx students in computing exist, these programs fail to address the systemic inequities that students face from educators, peers and academic cultures. They also fail to capture how students (especially those from the dominant white and Asian identities) perceive the impacts of race, racism and white supremacy on their academic experiences and others’.

Project Description

Building on the work of the 2022-2023 project team, this team will collect quantitative and qualitative data to address the following research questions:

  1. How do CS undergraduate students perceive race in university computing departments?
  2. What factors influence CS undergraduate students' understanding of and experiences with race in the context of computing departments?

Special focus will be placed on the recruitment of participants, distribution of surveys and implementation of interviews across a range of university types and student racial identities to ensure that researchers capture all possible variation in student perceptions and experiences. 

The team will collect quantitative and qualitative data via a survey instrument that will be developed to capture student perceptions of race in the context of their experiences as CS students within their department and university. Qualitative data will be collected via semistructured interviews of CS undergraduates. Finally, the team will perform topic sentiment analysis on relevant Twitter hashtags. Hashtags such as #BlackInTheIvory, #ShutDownStem, #BlackInSTEM, #BlackAFInSTEM, #BlackAndSTEM, #LatinosInSTEM, #LatinxInSTEM and #IndigeousInSTEM that are being used to speak up about student experiences will be collected and analyzed for sentiment as well as to pull together experiential themes.

Following data collection, phenomenographic, thematic and content analysis will be implemented to answer the research questions. This analysis will also be compared against themes identified in the analysis of hashtags and external statistical data collected via the Cultural Competence in Computing assessment.

Anticipated Outputs

Qualitative and quantitative research instrument for collecting data on CS student perceptions of race; evidence of how inclusion/exclusion of race in undergraduate CS departments impacts the experiences highlighted in social media protests; publications in conferences and journals

Student Opportunities

Ideally, this team will include 2 graduate and 4 undergraduate students. Students from a variety of disciplines are encouraged to apply, including those studying CS and social sciences such as anthropology, sociology, psychology, and gender, sexuality and feminist studies. Undergraduate students must be rising juniors or seniors in order to ensure that they bring foundational, discipline-specific knowledge to the interdisciplinary team. Graduate students are expected to provide more experience on not only the research topic, but also the research methodology and methods. 

Throughout this project, students will have the opportunity to work with and interact with leading researchers who are defining the field at the intersection of CS and race. More tangibly, all students will complete a review of background material on race, racism, white supremacy and technology bias/harm from a predefined set of core books, journal/conference papers and podcast episodes. 

Team members will also be required to attend the Race Workshop, a student-run workshop that explores issues of race, that meets throughout the academic year and features speakers from across the country conducting research on race. Students will have opportunities to learn about qualitative and quantitative research including instrument development, data collection and analysis, and publication. Some students will be funded to attend conferences and present research results.

Crystal Peoples will serve as project manager.

In Fall 2023, the team will meet on Wednesdays from 3 to 5 p.m.

Timing

Fall 2023 – Spring 2024

  • Fall 2023: Analyze data collected during 2022-2023 academic year; revise and distribute qualitative and quantitative data collection instruments; learn about semantic analysis and Twitter scraping; conduct literature reviews
  • Spring 2024: Continue data collection and analysis; identify publication venues; develop and write manuscript

Crediting

Academic credit available for fall and spring semesters

See earlier related team, Understanding Perceptions of Race Among Computer Science Undergraduates (2022-2023).

 

Image: Professor Shaundra Daily teaches Human-Centered Computing in the Duke Engineering Wilkinson Building, by Jared Lazarus/Duke University

Female professor lectures in classroom with two large screens showing her slides.

Team Leaders

  • Eduardo Bonilla-Silva, Arts & Sciences-Sociology
  • Shaundra Daily, Pratt School of Engineering-Electrical & Computer Engineering
  • Crystal Peoples, Arts & Sciences-Computer Science
  • Nicki Washington, Arts & Sciences-Computer Science

/graduate Team Members

  • Fatima Fairfax, Sociology-PHD
  • Jabari Kwesi, Computer Science-PHD
  • Alexander Rogers, Sociology-PHD

/undergraduate Team Members

  • Elyse McFalls, Statistical Science (BS)
  • Reagan Razon, Computer Science (BS)
  • Alexandra Thursland, Computer Science (BS)

Theme(s):