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A Longitudinal View of Gender Balance in a Large Computer Science Program
10:45 AM - 11:10 AM
Thu Mar 12, 2020
D137

Description

Computer Science has a persistent lack of women's participation. In order to best effect change, we require a more fine-grain analysis of the gender disparity as it changes throughout an undergraduate Computer Science curriculum. In this paper, we use a quantitative approach to highlight, with greater specificity, the point in an undergraduate career where gender balance changes. We also examine the role of grades in students' decisions to stay in the course sequence. Our goal is to enable targeted interventions that will make Computer Science a more welcoming discipline. Our study examines 30,890 unique student records over ten years at a large, public research institution. The records include students who took a Computer Science course over the past ten years. The dataset contains information about gender, majors, minors, academic level, and GPA. The dataset also includes a record from each course taken by each student and their final grade. We observed a modest increase in women's participation in all Computer Science courses over the past ten years. Despite this increase, the gender disparity is still large. Through our analysis, we found that women consistently choose not to continue through the Computer Science sequence at a higher rate than men. This higher attrition could be linked to women receiving lower grades in most introductory CS courses despite having the same or higher GPAs than men. Our results reveal specific areas where intervention can be the most effective in changing the stubborn gender disparity in Computer Science.


Speakers
Amy Baer University of Michigan
Andrew DeOrio Lecturer, University of Michigan

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