Socioeconomic Disparity Factors in Computer Science Education

This project explores the role that socioeconomic status plays in a computer science student’s academic career. While many papers surrounding broadening participation in computer science education are concerned with race and gender disparity gaps, socioeconomic status (SES) provides a direct lens for understanding disparities in access to educational resources, opportunities, and support systems. This project is primarily led by Jennifer Alexandra Thompson.

In 2022, we started trying to understand the relationship between a student’s high school SES and their performance in first-year college computer science courses. We published a poster titled “High School Socioeconomic Neighborhood Status and CS1 Performance” at the 2023 ACM Special Interest Group on Computer Science Education (SIGCSE) conference (Thompson et al., 2023).

The authors of the poster standing in front of the poster at a conference.

Poster presentation at SIGCSE 2023.
Left to right: Margaret Ellis, Jennifer Alexandra Thompson, Sara Hooshangi

The poster presented at SIGCSE 2023.

A Youtube video of this presentation is available at https://youtu.be/_e9x3tLqu4c.

Later in 2023, we published a full-length version of this work that introduced another disparity index measurement and that examined second-year and third-year CS courses, in addition to first-year college CS course. This paper is titled “The Impact of High School Region Socioeconomic Status on Computer Science Student Performance” and is published as a part of 2023 IEEE ASEE Frontiers in Education Conference (Thompson et al., 2023).

This project looks at developing and validate a survey to determine which components of a student’s SES may be most correlated to their academic performance, self-efficacy, and sense of belonging. This work will be presented at the ACM Conference on International Computing Education Research (ICER) 2025 as part of the Doctoral Consortium.

References

2023

High School Socioeconomic Neighborhood Status and CS1 Performance

Jennifer Alexandra Thompson, Margaret Ellis, and Sara Hooshangi

In Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 2, Toronto ON, Canada, Mar 2023

Abs DOI

CS1 student success rates are a longstanding issue in the computer science community. Indicators of performance prior to CS1 continue to be investigated in research, especially concerning prior programming and math courses taken at the high school level. This study aims to take a look at students’ high school socioeconomic neighborhood status and determines whether there is a correlation to CS1 performance. Specifically, we examine the Area Deprivation Index (ADI) of the high schools that CS1 students attended and the passing rates in CS1 based on the socioeconomic status of these high schools. The goal is to compare the performance of students from socioeconomic disadvantaged high schools to students from advantaged high schools. In this research, we find that students from the top 15% high schools ADI percentile pass CS1 at a higher rate with a significant difference.
The Impact of High School Region Socioeconomic Status on Computer Science Student Performance

Jennifer Alexandra Thompson, Margaret Ellis, and Sara Hooshangi

In 2023 IEEE Frontiers in Education Conference (FIE), Oct 2023

Abs DOI

Research in computing education has been steered towards understanding early indicators of what leads students to succeed in introductory programming courses (CS1). A major finding of these research efforts has been the impact that high school courses and prior programming experience have in predicting success in a post-secondary CS1. However, the socioeconomic status surrounding CS1 students has not been well explored as an indicator of performance. Specifically, a student’s high school socioeconomic status (SES) has not been well investigated in this area, despite the intuition that more socioeconomically advantaged high schools will better prepare students for college computing courses. In this research, we propose a method to examine a student’s prior high school regional socioeconomic status and determine whether this SES has a correlation to their post-secondary CS1 performance. This paper investigates the socioeconomic status of the neighborhood, census tract, and county the high school resides. To understand the socioeconomic statuses of these regions, we utilize multiple socioeconomic indices such as the Area Deprivation Index and the Social Deprivation Index. Some of the factors that create a deprivation index are the housing values of the region, poverty rate, adult educational completion, and household resources. After proposing a method to examine if there are any correlations between a student’s attended high school regional SES and the student’s performance in CS1, we perform a case study using seven years of CS1 student records from our institution. From the 4863 student records we use in this study, our initial findings indicate that students from more advantaged high school regions tend to pass CS1 more frequently across all surrounding region sizes we examined. Since our findings indicate that high school regional socioeconomic status may be a factor in a student’s performance, we argue that future computing education researchers should consider a student’s SES as a demographic factor of course performance in order to advocate for interventions that mitigate this disparity gap.