Abstract:
Improving the underrepresentation of women and
racial minorities in computer science requires a holistic understanding of how
early experiences and mindsets in K-12 classrooms influence decisions to pursue
careers in computing. We conducted a national longitudinal study of students in
advanced placement computer science courses to understand how student mindsets
(belonging, personal relevance, math/intelligence mindset) impact behavioral
engagement, how their mindsets evolve over time, and how contextual factors at
the teacher, classroom, and school level can influence these temporal dynamics.
We find that mindsets differentially impact engagement and vary with gender and
race. Some mindsets change over time due to performance feedback, and these
changes affect subsequent engagement and performance. Class characteristics
(e.g., class size, gender ratio) and school characteristics (e.g., share of
low-income and racially underrepresented students) moderate the effect of
mindsets on performance. Our findings have implications for learning theories
and equity-focused educational practices.
Abstract:
The trend for large-scale collaboration has the potential to improve researcher, cultural, and participant diversity. Conducting in-person research requires the examination of challenges and payoffs of competing priorities in what constitutes 'good' research. We describe these challenges and potential resolutions and payoffs in advancing big-team science.
Ph.D. Communication, Cornell University.
M.S. Communication, Cornell University.
M.A.
Psychology, New York University.
B.Sc. (Hons), Financial Economics, Essex University.
Institutional Affiliation: Teaching Assistantships at Cornell University & New York University