Graduation and participation rates in science, technology, engineering, or mathematics (STEM) careers are a worldwide concern because of the shortage of STEM professionals in STEM fields. When you disaggregate and look even further at computing fields it is clear that while there is a high need for computer professionals in the industry, enrollment in computing programs has not kept pace with that demand. This is further exacerbated when the data is disaggregated on the basis of race and gender. Exploring patterns regarding race/ethnicity and gender can help education researchers and the computing community reveal the hidden stories that help them provide guidelines, strategies, or mechanisms that lead to enhancing the persistence of underrepresented minority students in these fields. This study is a Work-In-Progress (WIP) that was conducted using a subset of a larger longitudinal database - Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) to determine the stickiness of students in computing fields across multiple U.S. institutions. In a degree program, “stickiness” measures the tendency for a program to retain students in the program until they graduate. Stickiness is distinct from the notion of performance. For the purposes of this study, stickiness measures the likelihood of graduation for students who have been enrolled in a computing program (the fraction that “stick” to the program or persist). In this study, we used the MIDFIELD database, which includes more than one and half million undergraduate students among 22 partner institutions across the U.S. This subset includes 50 thousand students among 14 partner institutions. We only included students who had the opportunity to graduate within 6 years of matriculation and identified students who at some point were enrolled in one of a set of computing disciplines; namely computer engineering, software engineering, computer science, information systems, and information technology. We then disaggregated students based on their race/ethnicity and gender to calculate their stickiness for each of these groups. Preliminary findings confirm variations in disciplinary stickiness by race/ethnicity and gender in computing majors, for example, Asian males and females, as well as White males, tend to have the highest stickiness. Meanwhile, Black males and females, as well as Hispanic females tend to have the lowest stickiness. Regardless of the different persistent rates among different race/ethnicity groups mentioned in this paper, the stickiness rate of computing majors is below the average stickiness of other STEM majors. This is an indication to revisit these majors to not only seek solutions to overcome the race/ethnicity and gender gaps, but also to investigate solutions to increase the stickiness rate for these majors. We anticipate findings from this ongoing research to be beneficial to the computing and education community, as well as education researchers. Computing students show different patterns of persistence from engineering students, so it is important to explore the pathways of computing students specifically. This research will help these groups to better understand the relative successes of computing students, which will be of interest to communities such as Grace Hopper Celebration of Women in Computing (GHC), the TAPIA Conference, the American Society of Engineering Education (ASEE) and etc.
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