Increasing Women's Participation in CS at Large Public Universities: Issues and Insights

Author:

Rezwana Jeba1,Maher Mary Lou1

Affiliation:

1. University of North Carolina at Charlotte

Abstract

Despite rapidly growing interest in Computer Science (CS), CS has the second lowest participation rate for women of all science and engineering degrees, according to a recent report by the US National Science Board [16]. The reasons for women's underrepresentation and their experiences may differ from university to university. Universities are very different in terms of types of students, student profiles, students' socio-economic status, race, number of students, admission requirements, and resources. For example, some universities can create gender balance in introductory courses through selective admission. However, typically, admission practices in large public universities are not able to achieve gender balance for CS majors. As a result, there are lower percentages of women in the CS major in large public universities than in selective schools. In addition, large public universities tend to have a larger number of CS students when compared to private universities. To understand women students' experiences and struggles in computer science at a large public university in the United States and to find ways to intervene in favor of gender equity in computer science, we conducted in-depth interviews with women students in introductory programming courses. In this article, we present women students' experiences, struggles, expectations and offer potential interventions based on the findings to encourage women to consider CS as their major and stay in the CS major. Our interview data shows there is a persistent effect of lack of prior programming experience and gender bias that leads to a negative experience for women students in introductory CS courses. This article presents the challenges faced by women in CS and provides their recommendations for attracting and retaining women students in CS at large public universities.

Funder

Northeastern University Center for Inclusive Computing

Publisher

Association for Computing Machinery (ACM)

Subject

Education,General Computer Science

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4. Gender Differences in Seff-Perceptions: Convergent Evidence from Three Measures of Accuracy and Bias

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