Programming experience promotes higher STEM motivation among first-grade girls
Open accessAbstract
The gender gap in science, technology, engineering, and math (STEM) engagement is large and persistent. This gap is significantly larger in technological fields such as computer science and engineering than in math and science. Gender gaps begin early; young girls report less interest and self-efficacy in technology compared with boys in elementary school. In the current study (N=96), we assessed 6-year-old children's stereotypes about STEM fields and tested an intervention to develop girls' STEM motivation despite these stereotypes. First-grade children held stereotypes that boys were better than girls at robotics and programming but did not hold these stereotypes about math and science. Girls with stronger stereotypes about robotics and programming reported lower interest and self-efficacy in these domains. We experimentally tested whether positive experience with programming robots would lead to greater interest and self-efficacy among girls despite these stereotypes. Children were randomly assigned either to a treatment group that was given experience in programming a robot using a smartphone or to control groups (no activity or other activity). Girls given programming experience reported higher technology interest and self-efficacy compared with girls without this experience and did not exhibit a significant gender gap relative to boys' interest and self-efficacy. These findings show that children's views mirror current American cultural messages about who excels at computer science and engineering and show the benefit of providing young girls with chances to experience technological activities.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Master et al. (2017) reported higher technology interest and higher self-efficacy in students in the treatment group compared to the control group.
“The study reported higher technology interest and higher self-efficacy in students who were in the treatment group compared to students in the control group.”
From A Systematic Review of Studies on Educational Robotics (2019) · cites this paper as Master et al., 2017
Master et al. (2017) found no gender gap between boys and girls in technology interest and self-efficacy.
“Furthermore, the study found no gender gap between boys and girls in this regard.”
From A Systematic Review of Studies on Educational Robotics (2019) · cites this paper as Master et al., 2017
Cites
The four-phase model of interest development
Hidi, S., & Renninger, K. A. (2006) · Educational Psychologist · doi:10.1207/s15326985ep4102_4 · Cited by 4,702 · see citing works
Expectations of brilliance underlie gender distributions across academic disciplines
Leslie, S.-J., Cimpian, A., Meyer, M., & Freeland, E. (2015) · Science · doi:10.1126/science.1261375 · Cited by 1,516 · see citing works
Gender stereotypes about intellectual ability emerge early and influence children's interests
Bian, L., Leslie, S.-J., & Cimpian, A. (2017) · Science · doi:10.1126/science.aah6524 · Cited by 1,206 · see citing works
Stout, J. G., Dasgupta, N., Hunsinger, M., & McManus, M. A. (2011) · Journal of Personality and Social Psychology · doi:10.1037/a0021385 · Cited by 1,050 · see citing works
Indulging Our Gendered Selves? Sex Segregation by Field of Study in 44 Countries
Charles, M., & Bradley, K. (2009) · American Journal of Sociology · doi:10.1086/595942 · Cited by 996 · see citing works
Building Blocks for Developing Spatial Skills: Evidence From a Large, Representative U.S. Sample
Jirout, J. J., & Newcombe, N. S. (2015) · Psychological Science · doi:10.1177/0956797614563338 · Cited by 301 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Xia, Q., Chiu, T. K. F., Lee, M., Sanusi, I. T., Dai, Y., & Chai, C. S. (2022) · Computers & Education · doi:10.1016/j.compedu.2022.104582 · Cited by 469 · see citing works
A Systematic Review of Studies on Educational Robotics
Anwar, S., Bascou, N. A., Menekse, M., & Kardgar, A. (2019) · Journal of Pre-College Engineering Education Research (J-PEER) · doi:10.7771/2157-9288.1223 · Cited by 419 · see citing works
“These studies focused on increased participation or retention of females (Mason, Cooper, & Comber, 2011; Master et al., 2017; Terry, Briggs, & Rivale, 2011), minorities (Kafai, Searle, Martinez, & Brayboy, 2014; Searle, Fields, Lui, & Kafai, 2014; Shatz, Pieloch, & Shamieh, 2016; Zimmerman, Johnson, Wambsgans, & Fuent…”
Lin, P.-Y., Chai, C.-S., Jong, M. S.-Y., Dai, Y., Guo, Y., & Qin, J. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2020.100006 · Cited by 171 · see citing works
- Authors
- Master, A., Cheryan, S., Moscatelli, A., & Meltzoff, A. N.
- Affiliations
- University of Washington (US)
- Corresponding author
- Master, Allison
- Venue
- Journal of Experimental Child Psychology
- Publisher
- Elsevier BV
- Published
- 2017-08
- Volume
- 160
- Pages
- 92-106
- ISSN
- 0022-0965 (ISSN-L)
- OpenAlex
- W2606540748
- Cited by
- 372
- References
- 6
- Citations by year
- 2026: 20 · 2025: 33 · 2024: 52 · 2023: 36 · 2022: 49 · 2021: 55 · 2020: 84 · 2019: 18 · 2018: 22 · 2017: 2 · 2016: 1
- FWCI
- 72.25
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Experimental Child Psychology (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (SMA-0835854, DRL-1420351, SBIR-1346821)
- Topics
- Social Sciences › Gender Studies › Gender and Technology in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Social Sciences › Safety Research › Career Development and Diversity
- Keywords
- gender gap in STEM, programming experience, robot programming, gender stereotypes, self-efficacy, technology interest, early childhood, first grade students
- MeSH terms
- Child, Female, Humans, Male, Motivation, Programming Languages, Robotics, Sex Factors, Stereotyping, Technology, Self Efficacy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review