A meta-analytical comparison of gender differences in computational thinking education

Gender disparities in Computational Thinking (CT) are well-documented, yet few meta-analyses examine how males and females respond differently to educational interventions. This three-level meta-analysis synthesized 37 studies (62 effect sizes) for females and 35 studies (56 effect sizes) for males to explore gender-specific patterns in CT education programs. Results show significant overall effects for both females ( g = 0.867) and males ( g = 0.807), confirming the effectiveness of CT education across genders within the reviewed studies. However, moderator analyses reveal that males demonstrated consistent gains across educational levels, assessment tools, and learning outcomes, while females showed more variation. Females improved most in CT performance and attitudes, but showed limited gains in self-efficacy and self-perceptions. Pedagogical approaches such as unplugged activities, robotics, and project-based learning were effective for both genders. The findings also suggest a need for age-appropriate and culturally relevant pedagogies, particularly for girls in primary and secondary education. While this meta-analysis provides practical implications for future program development, it also highlights the need for broader research across diverse sociocultural and technological environments to better understand and address the complex dynamics of gender in CT education.