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Key Observations of Enrollment Trends during the Pandemic in Early Programming Courses to Broaden Female Students’ Participation in Computing

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Conference

2023 ASEE Annual Conference & Exposition

Location

Baltimore , Maryland

Publication Date

June 25, 2023

Start Date

June 25, 2023

End Date

June 28, 2023

Conference Session

Computing and Information Technology Division (CIT) Technical Session 7

Tagged Division

Computing and Information Technology Division (CIT)

Tagged Topic

Diversity

Page Count

16

DOI

10.18260/1-2--43913

Permanent URL

https://peer.asee.org/43913

Download Count

132

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Paper Authors

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Jungsoo Lim California State University, Los Angeles Orcid 16x16 orcid.org/0000-0003-4683-9170

biography

Yilin Feng California State University, Los Angeles Orcid 16x16 orcid.org/0000-0001-8843-8987

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Yilin Feng is an assistant professor at California State University, Los Angeles. She received her Ph.D. degree from Purdue University. Her research interest is in airport simulation, operation, and management.

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biography

Eun-Young Kang California State University, Los Angeles

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Professor
Computer Science
California State University, Los Angeles

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Abstract

In an effort to increase the percentage of female students in the Computer Science (CS) Department at [College/Dept/Univ Name here], we have conducted an in-depth data analysis of student enrollment, persistence, and performance in early programming courses (CS1 through CS3) during the pandemic period (Fall 2019 to Spring 2022). Currently, the department has a female enrollment of less than 12%, which is below the national average of 20%. Through this study, we aim to identify the most appropriate strategies for female students to broaden their participation in computing.

As a part of the study, we collected data on the introductory course sequence, CS1 Programming I, CS2 Programming II, and CS3 Programming with Data Structure. The data included quasi-cohort course outcomes, quasi-cohort persistence, retention graduation, day 1 to census day enrollment, completion by transfer status, outcomes by major, and student support. In addition, to understand in-depth the level of preparedness and the level of satisfaction of women and minority students in computing, we also collected additional data. This effort included collecting the results of midterm exams, quizzes, course projects, assignments, and the final exam from CS1, CS2, and CS3. Moreover, we conducted surveys with these students to determine their satisfaction with interactions with peers and instructors, as well as their confidence level in the CS major.

Through the data analyses and discussion, we found that female students’ academic performance is as good as, or even better than, their male classmates. However, compared to male students, female students tend to be less confident and satisfied with their academic performance. The withdrawal rate is higher among female students than male students. The discussion of these results contributes toward identifying possible practices that would broaden participation in computer science for women.

Lim, J., & Feng, Y., & Kang, E. (2023, June), Key Observations of Enrollment Trends during the Pandemic in Early Programming Courses to Broaden Female Students’ Participation in Computing Paper presented at 2023 ASEE Annual Conference & Exposition, Baltimore , Maryland. 10.18260/1-2--43913

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