Putting the Pieces of the Puzzle Together in Modeling Gendered Educational Choices (Poster 9)

• In Event: Saturday Poster Session 3:05 pm
  In Poster Session: Explorations in Student Motivation, Self-Efficacy, and Educational Outcomes

Abstract

The underrepresentation of women in science, technology, engineering, and mathematics (STEM) fields is a persistent international problem. A large body of research has identified a broad range of potential causes. This study uses machine learning techniques to integrate different theoretical perspectives and identify the most potent predictors of women’s entry into STEM fields in higher education. We analyzed a German nationally representative longitudinal sample of 9-through-12-grade students (N= 4383). Gender differences in career aspirations emerged early, with female students having more diverse aspirations than male students. Gender-specific career aspirations and students’ math and language arts self-concepts were the most potent predictors of STEM college major selection four years after high school. Women’s underrepresentation in STEM is linked to multiple factors.

Authors

• Jakob Schwerter, University of Tübingen

• Fani Lauermann, University of Bonn

• Philipp Doebler, TU Dortmund University

• Marjolein Fokkema, Leiden University