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Gender differences in STEM choices in Taiwanese higher education: The male-things and female-people interest hypothesis

Abstract

This study proposes a conceptual framework for gendered processes based on the male-things vs. female-people interest (MTFPI) hypothesis, by identifying gender differences in factors relating to science, technology, engineering, and mathematics (STEM) choices in higher education. Longitudinal data were from the Taiwan Education Panel Survey (TEPS) for Grade 7 (n = 20,055), 9, 11, and 12 and the follow-up TEPS-Beyond (TEPS-B) for 24-25-year-olds (n = 2,700). Correlation analysis was conducted with weights so that the result can represent that of the original Grade 7 population. The results generally support the MTFPI hypothesis. Males’ STEM choice is related to high mathematics achievement and low frustration in mathematics in all stages of secondary education, high gender stereotyping of their jobs, and low confidence in people-smart skills (e.g., leadership, collaboration with others, and oral expression). Females’ STEM choice is related to mathematics teachers’ clear explanations and desirable interactions in mathematics classrooms. These results generally support the MTFPI hypothesis that males are more interested in things (including achievement), while females enjoy engaging in interaction with people. Educators need to pay attention to the differential interests between genders for adaptive teaching to invite both genders to study STEM in higher education.

Keywords

gender difference, longitudinal data, mathematics achievement, mathematics teaching, STEM choice

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Author Biography

Mei-Shiu Chiu

Dr. Mei-Shiu Chiu is currently a professor of education at National Chengchi University, Taiwan. She received a B. A. and an M. A. Degree in Education from National Taiwan Normal University and completed her doctoral study at the Faculty of Education, Cambridge University, U.K. Her research interests focus on the design, implementation, and effectiveness evaluation of learning, teaching, and assessment in a variety of areas of knowledge (e.g. mathematics, science, and energy); interactions between emotions, cognition, and culture; and multiple research methods and data analysis methods (including educational and data science methods). She has developed several research-based educational theories, relevant assessment tools, as well as school and teacher development courses for educational and research practices.

Maurizio Toscano

Senior Lecturer in Science Education, Faculty of Education, University of Melbourne