Children found to change their views on gender stereotypes when read books that give other views

Bob Yirka is a research scientist at Phys.org.

A team of psychology researchers from the University of Amsterdam, Western Washington University, the University of British Columbia and the University of Toronto have found that reading books that have female characters working with math to solve problems reduces stereotypes that have been found to turn women away from interest in science, technology, engineering The group has written a paper about the experiments they did that involved reading to children and have posted it on the open access site.

Women are less likely to enroll in science, technology, engineering and math classes because of stereotypes. The researchers wondered if children who were read books that portrayed female characters having an interest in and excelled in math and science might have a different view of the subject. They conducted a simple experiment to find out.

The experiment involved reading books to children between the ages of 6 and 11 and then asking them to take a test that measured gender-based stereotypes. The researchers read to hundreds of children. The first book was about a young male character who excelled in math, the second was about a young female character who excelled in math, and the third was about a character who was interested in sports.

After listening to a book, each child was given a test that involved sorting math and implicit terms into genders. The children were asked how much they liked doing math.

After listening to a book about a female character doing well in math, the researchers found that all of the children, regardless of gender, held less gendered stereotypes about women and their ability to work with math. If material is given to young people that challenges the status quo, they can change their gendered stereotypical thinking.

More information: Katharina Block et al, Exposure to stereotype-relevant stories shapes children's implicit gender stereotypes, PLOS ONE (2022). DOI: 10.1371/journal.pone.0271396 Journal information: PLoS ONE

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