Computing Whether She Belongs: Stereotypes Undermine Girls’ Interest and Sense of Belonging in Computer Science

A classroom with a non-stereotypical look creates more inclusive signals of who belongs—increasing high school girls’ interest in computer science without deterring boys.

FindingsMethodology

Women and girls remain greatly underrepresented in science, technology, engineering, and mathematics (STEM) fields, especially computer science, where men completing undergraduate degrees outnumber women four to one. Previous studies have found that male stereotypes about STEM fields contribute as a social factor discouraging college women from computer science. These stereotypes can be perpetuated through multiple avenues including academic environments. Such stereotypes may have a particularly negative effect in adolescence, when girls and boys develop their social identities, and schools increasingly offer introductory courses required to advance. With national initiatives to add computer science courses in over a thousand high schools and middle schools, it is especially important to establish programs that offer girls and boys equal opportunities to explore their interest in this field. In this study, the authors investigated the impact of stereotypes in the academic environment on interest in computer science courses among high school girls and boys. They compared enrollment interest, sense of belonging, and concerns about perpetuating or being judged according to negative stereotypes (known as “negative stereotype concerns”), depending on whether the classroom contained either stereotypical or non-stereotypical objects.

Findings

Girls were less interested than boys in a computer science course when the classroom contained stereotypical objects such as electronics, video games, and science fiction books. Girls and boys were equally interested in a computer science course when the classroom contained non-stereotypical or neutral objects such as plants, art, and a coffee maker. Participants were asked to compare the computer science courses based on photos or written descriptions of the classrooms.

  • Before viewing the photos, girls expressed less interest, less feeling of belonging, and greater negative stereotype concerns in a computer science course, compared to boys, with about a 1-point gap in each measure on a 7-point scale.
  • Viewing a photo of the stereotypical classroom had no significant effect on either girls’ or boys’ ratings of interest, belonging, and negative stereotype concerns compared to their ratings before the experiment.
  • Viewing a photo of the non-stereotypical classroom photo had no significant effect on boys’ ratings, but increased girls’ interest and belonging—closing the gap between girls and boys—and decreased girls’ negative stereotype concerns, reducing the gap between girls and boys, compared to their ratings before the experiment and to the stereotypical classroom.
  • When asked to choose one of the courses, girls were more likely to choose the course with the non-stereotypical classroom (68% vs. 48%), with over a third expressing positive interest (35.4% vs. 13.3%) in taking the course. Boys were equally likely to choose either course. Notably, while there was initially more interest by boys than girls before the experiment, there was equal interest after participants viewed the non-stereotypical classroom.
  • When asked about how their personalities fit with the stereotype of a computer scientist, girls were less likely than boys to report that they fit the stereotype. Girls who reported greater fit had greater interest in taking the course and a greater sense of belonging both before the experiment and after the stereotypical classroom than girls who reported a lower fit.
  • When students were offered a written description of only one course, girls who read about the non-stereotypical classroom expressed more interest and belonging than girls who read about the stereotypical classroom, while boys rated the two courses similarly.

The more students felt they belonged in a course environment, the more interest they expressed in the computer science course, regardless of their gender. A classroom with a non-stereotypical look can create more inclusive signals of who belongs—increasing girls’ interest in computer science without deterring boys.

Methodology

The first experiment recruited 165 high school students from two high schools (one public, one private) for a survey. Participants viewed photos of two computer science classrooms in randomized order, one containing stereotypical and one non-stereotypical objects, and were told that both courses had the same material, a male teacher, and equal numbers of female and male students. Participants used a 7-point scale to rate their enrollment interest, feelings of belonging, and concerns about negative stereotypes at three points: before viewing either classroom, and in response to each classroom. Students were then asked to choose one of the courses, and to rate their personal fit with the stereotype of a computer scientist. The second experiment recruited 104 high school students from the same public school. Participants viewed a written description of a single computer science course, randomly assigned to include a female or a male teacher, and stereotypical or non-stereotypical objects. After reading the description, participants answered the same questions about interest and belonging as in the first experiment. They also rated their expectations of success and the value they placed on computer science. The photographs and written descriptions presented the same two sets of objects. The stereotypical objects were electronics, software, computer parts, tech magazines, Star Wars and Star Trek items, computer books, science fiction books, and video games. The non-stereotypical objects were nature pictures, water bottles, plants, wall art, general magazines, pens, a coffee maker, and lamps. Stereotype associations were validated by ratings from an independent group of 106 high school students.



MLA: Master, Allison, Sapna Cheryan, and Andrew N. Meltzoff. “Computing Whether She Belongs: Stereotypes Undermine Girls’ Interest and Sense of Belonging in Computer Science.” Journal of Educational Psychology, vol. 108, no. 3, 2016, pp. 424–437.
APA: Master, A., Cheryan, S., & Meltzoff A.N. (2016). Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science. Journal of Educational Psychology, 108(3), 424–437.
Chicago: Master, Allison, Sapna Cheryan, and Andrew N. Meltzoff. “Computing Whether She Belongs: Stereotypes Undermine Girls’ Interest and Sense of Belonging in Computer Science.” Journal of Educational Psychology 108, no. 3 (2016): 424–437.