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Gender, Self-Assessment, and Persistence in Computing: How gender differences in self-assessed ability reduce women’s persistence in computer science

Published: 03 August 2022 Publication History

Abstract

Are women less likely to persist in computer science because of gender differences in self-assessed computing ability? And why do gender differences exist in self-assessments among women and men who earn the same grades? We use a mixed-method research design to answer these questions, utilizing both quantitative survey data (n = 764) and qualitative interview data (n = 59) from students in introductory computing courses at a large U.S. state university. Quantitatively, we find that women self-assess their computing ability significantly lower than men who earn the same grades, and that these lower self-assessments reduce the likelihood that women enroll in future CS courses (relative to men who earn equivalent grades). Qualitatively, we explore how women and men perceive their own computing ability to understand why women self-assess their ability lower than men. Our interviews revealed that women were much less likely than men to make favorable comparative judgements about their ability relative to their classmates. Women also had higher personal performance standards than men. Lastly, women were more likely than men to experience disrespectful treatment, with an undertone of presumed incompetence, from their TAs and classmates. In sum, this research furthers our understanding of why gender differences exist in self-assessments of computing ability and how these differences can contribute to gender disparities in computing persistence. It also draws attention to the importance of feedback in computing courses and suggests that improving course feedback may reduce gender disparities in computing.

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cover image ACM Conferences
ICER '22: Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 1
August 2022
372 pages
ISBN:9781450391948
DOI:10.1145/3501385
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 August 2022

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Author Tags

  1. ability
  2. gender
  3. introductory course
  4. persistence
  5. self-assessments
  6. students

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ICER 2022
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ICER 2022: ACM Conference on International Computing Education Research
August 7 - 11, 2022
Lugano and Virtual Event, Switzerland

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Cited By

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  • (2025)CSEdD: CS Education Dashboard for Student Success, Retention, and Performance InvestigationsProceedings of the 56th ACM Technical Symposium on Computer Science Education V. 210.1145/3641555.3705156(1429-1430)Online publication date: 18-Feb-2025
  • (2024)Understanding the Reasoning Behind Students' Self-Assessments of Ability in Introductory Computer Science CoursesProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671094(1-13)Online publication date: 12-Aug-2024
  • (2024)Instructional Transparency: Just to Be Clear, It's a Good ThingProceedings of the 2024 ACM Conference on International Computing Education Research - Volume 110.1145/3632620.3671091(192-205)Online publication date: 12-Aug-2024
  • (2024)Predictors of university students’ intentions to enroll in computer programming courses: a mixed-method investigationDiscover Education10.1007/s44217-024-00232-53:1Online publication date: 9-Sep-2024
  • (2023)Retaining Black Women in Computing: A Comparative Analysis of Interventions for Computing PersistenceACM Transactions on Computing Education10.1145/363531324:2(1-25)Online publication date: 22-Dec-2023
  • (2023)Potential Factors for Retention and Intent to Drop-out in Brazilian Computing ProgramsACM Transactions on Computing Education10.1145/360753723:3(1-33)Online publication date: 12-Sep-2023
  • (2023)Engaging Novice Programmers: A Literature Review of the Effect of Code Critiquers on Programming Self-efficacy2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10342975(1-9)Online publication date: 18-Oct-2023
  • (2022)Gender Balance Ensuring in IT field: Ukrainian Study Case2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)10.1109/CSIT56902.2022.10000577(288-291)Online publication date: 10-Nov-2022

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