Socioeconomic status (SES) continues to be an underexplored identity factor in computer science education research. My research plan is to identify socioeconomic status factors that have the greatest impact on a computer science student’s course performance and self-efficacy in computing. I plan to examine these factors by looking at populations of college-level computer science students from both community colleges and universities. To explore this, I will create a student survey that will collect information on a wide variety of socioeconomic status factors, demographics, self-efficacy, and beliefs about computing. This survey would also be used to acquire a student’s computer science course grades. Additionally, I hope to validate the socioeconomic status portion of the survey to create a standard way for fellow computing education researchers to measure the socioeconomic status of their students. By creating a more standard way to inquire about socioeconomic status, I hope to encourage more researchers in computer science education to explore socioeconomic disparity gaps. Lastly, using the results of the survey, I would like to conduct student interviews and utilize qualitative analysis techniques to further understand the impact of socioeconomic status on students’ learning outcomes, sense of belonging, and academic career in computing.

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.

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 number of online classroom environments in universities has been steadily rising. As a result, it has become more important to understand how students utilize these online environments, as well as to identify the most effective learning strategies. This research identifies strategies used by students in an online tool associated with a first-year university computer science class. Utilizing sequential pattern mining, we identify common strategies used by high- and low-performing students. The goal is to understand how higher performing students utilize an unsupervised online learning environment to identify effective strategies in this setting. This will allow instructors to direct students toward effective methods of using online classroom tools.