Computing whether she belongs: Stereotypes undermine girls’ interest and sense of belonging in computer science

• Autores: Allison Master, Sapna Cheryan, Andrew N. Meltzoff
• Localización: Journal of educational psychology, ISSN-e 1939-2176, ISSN 0022-0663, Vol. 108, Nº. 3, 2016 (Ejemplar dedicado a: Motivating Classroom Learning), págs. 424-437
• Idioma: inglés
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• Resumen

  • Computer science has one of the largest gender disparities in science, technology, engineering, and mathematics. An important reason for this disparity is that girls are less likely than boys to enroll in necessary “pipeline courses,” such as introductory computer science. Two experiments investigated whether high-school girls’ lower interest than boys in enrolling in computer science courses is influenced by stereotypes of the field. We further tested whether these stereotypes can be communicated by the physical classroom environment, and whether changing this environment alters girls’ interest. In 2 experiments (N = 269), a computer science classroom that did not project current computer science stereotypes caused girls, but not boys, to express more interest in taking computer science than a classroom that made these stereotypes salient. The gender difference was mediated by girls’ lower sense of belonging in the course, even beyond the effects of negative stereotype concerns, expectations of success, and utility value. Girls’ lower sense of belonging could be traced to lower feelings of fit with computer science stereotypes. Individual differences in fit with stereotypes predicted girls’ belonging and interest in a stereotypical, but not a nonstereotypical, classroom. Adolescence is a critical time for career aspirations. Girls may avoid computer science courses because current prevailing stereotypes of the field signal to them that they do not belong. However, providing them with an educational environment that does not fit current computer science stereotypes increases their interest in computer science courses and could provide grounds for interventions to help reduce gender disparities in computer science enrollment. (PsycINFO Database Record (c) 2016 APA, all rights reserved)