Increasing Women's Persistence in Computer Science by Decreasing Gendered Self-Assessments of Computing Ability
Increasing Women's Persistence in Computer Science by Decreasing Gendered Self-Assessments of Computing Ability
复制标题
通过减少对计算能力的性别自我评估来提高女性对计算机科学的坚持
DOI:
10.1145/3430665.3456374
复制
发表时间:
2021
期刊:
影响因子:
--
通讯作者:
Stolee, Kathryn T.
中科院分区:
文献类型:
--
作者:
Fisk, Susan R.;Wingate, Tiah;Battestilli, Lina;Stolee, Kathryn T.
Gender stereotypes about women's computing ability contribute to the dearth of women in computing by causing women to experience gender bias. These gender stereotypes are doubly disadvantaging to women because they create gender differences in self-assessments of computing ability, decreasing the likelihood that women will persist in Computer Science (CS). This is because students need to believe they have sufficient ability in a field in order to pursue it as a career.Building on decades of Sociological theory, we hypothesized that increasing top-performing women's self-assessments of computing ability would increase those women's intentions to persist in computing. To test this hypothesis, we conducted a field experiment in a CS1 class in which the top 50% of students were given additional performance feedback from their instructor via email. The intervention increased these women's and men's self-assessed CS ability but only increased the women's CS persistence intentions. In sum, sending a single email increased top-performing women's intentions to persist in CS by 18%. A mediation analysis found evidence for the proposed causal path; namely, that the intervention increased the women's self-assessments of computing ability, which then increased their intentions to persist in computing. This research furthers our knowledge of the processes around self-assessments of ability and career choice that contribute to the dearth of women in CS. It also provides evidence for a lightweight intervention that may increase the number of women in computing, as prior research finds that intentions to persist are highly predictive of actual persistence in STEM fields.
DOI:
--
发表时间:
1999
期刊:
影响因子:
--
作者:
J. Chafetz
通讯作者:
J. Chafetz
影响因子:
4.4
作者:
Correll, SJ
通讯作者:
Correll, SJ
DOI:
--
发表时间:
1935
期刊:
影响因子:
--
作者:
M. Sherif
通讯作者:
M. Sherif