Area:
Tests and Measurements Education, Statistics
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High-probability grants
According to our matching algorithm, Weihua Fan is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 2021 — 2024 |
Meltzoff, Andrew
Cheryan, Sapna (co-PI) [⬀]
Fan, Weihua
Master, Allison |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Counteracting Stereotypes to Boost Girls' Interest and Participation in Computer Science
This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).
The goal of this project is to identify and promote critical factors that facilitate the development of girls’ interest in pursuing computer science. This will help broaden girls’ participation in computer science courses, programs, and college majors. Young women currently earn only 19% of bachelor’s degrees in computer science. There is a pervasive societal stereotype that women and girls are less interested in computer science than men and boys. This can harm young girls’ sense of belonging and interest in computer science starting from an early age. This project will experimentally examine the causal impact of these gender-interest stereotypes on girls’ interest in pursuing computer science. It will also investigate the sources that communicate these stereotypes to students. Finally, this project will test an intervention to reduce the impact of stereotypes on girls’ interest in computer science. Ultimately, this project will create a foundation for disseminating best practices on effective methods for motivating girls to pursue computer science. The findings will have implications for computer science educators who aim to broaden participation in computer science and others conducting research that aims to reduce educational inequalities linked to stereotypes. Findings will be shared with stakeholders working to promote students’ motivation in computer science.
Four experimental studies will be conducted to examine how gender-interest stereotypes influence 12 to 14 year-old students’ motivation for computer science through laboratory studies and one field intervention. Experiment 1 will experimentally manipulate gender-interest stereotypes to assess causal effects on girls’ interest in pursuing computer science. Experiment 2 will explore the cues that communicate interest stereotypes to students, such as the proportion of girls and boys in a computer science class. Experiment 3 will test whether providing information that counteracts stereotypes can increase girls’ interest in pursuing computer science. Experiment 4 will test a real-world intervention designed to reduce the impact of gender-interest stereotypes on girls’ enrollment in introductory computer science courses. Together, these studies will deepen understanding of the causes and consequences of gender stereotypes, how they influence girls’ motivation to learn computer science, and how educators can help students resist stereotypes and start the pathway to computer science careers.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
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0.964 |