Epistemic Network Analysis to Study Gender Equity in Cyber Security: Women's Contributions to Computational Thinking in Model-Eliciting
This study examines high school women's and men's cognitive engagement in computational thinking elicited through Model-Eliciting Activities (MEAs). The purpose is to understand potential gender differences that could inform strategies for increasing women's representation in STEM fields. Discourse analysis was used to examine conversations between an all-women team and an all-men team collaboratively solving a Tic-tac-toe MEA. Utterances were coded for computational thinking skills (decomposition, pattern recognition, abstraction, and algorithms) and indicators of cognitive engagement (self-regulation, justification, questioning, giving directions, and uptake). Epistemic network analysis (ENA) modeled relationships between codes based on their co-occurrence. ENA visualizations revealed the interconnections between computational thinking and engagement for each team. Subtracted ENA networks highlighted differences between the teams. The women frequently used questioning and justification around breaking problems into smaller parts (decomposition). The men relied more on justifying answers and directing each other in abstracting patterns and algorithms. Both teams succeeded in developing computational thinking models, but in different ways reflecting their unique collaborative engagement styles. Results suggest tailored strategies aligned with women's and men's distinctive learning processes are needed to optimize their computational thinking development. Model-eliciting activities show promise for facilitating cognitively engaging, collaborative STEM learning for diverse students. Ultimately, examining gender dynamics provides insights into creating supportive, empowering STEM learning environments where all students can thrive. This study offers a model for understanding and promoting gender equity in STEM education.