“But They Just Aren’t Interested in Computer Science” (Part Two)
Written by: Julie Smith
Note: this post is part of a series about the most-cited research studies related to K12 computer science education.
The study’s title says it all: “Gender stereotypes about interests start early and cause gender disparities in computer science and engineering.” It’s worth noting that the careful design of their studies bolsters the case: this work includes both surveys and experiments, allowing the researchers to comment on causality. The combination of surveys and interventions make it possible to conclude that it is the stereotype driving the lower interest rate, not a student’s inherent lower rate of interest causing them to generate a stereotype by imputing their attitude onto others. Additionally, their diverse subject pool makes it more likely that their findings are widely applicable.
The researchers found that stereotypes suggesting that boys are more interested in computer science exist from at least the third grade. Further, these stereotypes make it less likely for girls to study computer science, an effect mediated by the girls’ decreased sense of belonging.
Significantly, stereotypes about interest in computer science were a stronger predictor of a student’s intent to study computer science than stereotypes about ability. The authors do point out that there is a stronger cultural norm against expressing ability stereotypes than interest stereotypes, which may make it harder to root out the interest stereotypes. At the same time, the finding that student interest in studying computer science could be impacted by their experiences in an experiment imply that interventions designed to counteract stereotypes may very well be effective.
The fact that this study is one of the most-cited K12 computer science education research studies suggests that its message of the importance of recognizing the role of interest stereotypes has resonated with many other researchers. The next step is to determine which types of interventions are most effective at breaking down interest stereotypes.
Further Reading
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