UPDATES Sign up to receive periodic updates from the Student Experience Research Network.
Andrei Cimpian is a Professor of Psychology at New York University. He earned his Ph.D. in psychology from Stanford University. One of Dr. Cimpian’s main areas of expertise is academic achievement and motivation. Among other topics, he has investigated gender stereotypes, gender gaps in achievement and representation, people’s beliefs about ability and talent, and the influence of praise and criticism on children’s achievement. Dr. Cimpian’s research has been funded by several agencies, including the National Science Foundation, the American Psychological Foundation and the Spencer Foundation, and has been published in some of the top journals in psychology and education. Media outlets such as the New York Times, the Washington Post, and the Economist have covered his work.
Visit our library to view Andrei Cimpian's papers related to learning mindsets.
Associated Publications
- Teachers’ belief that math requires innate ability predicts lower intrinsic motivation among low-achieving students
- A neurobiological investigation of the relationship between early adverse experiences and growth mindset among children
- Evidence of bias against girls and women in contexts that emphasize intellectual ability
- The brilliance trap: How a misplaced emphasis on genius subtly discourages women and African-Americans from certain academic fields
- Messages about brilliance undermine women’s interest in educational and professional opportunities
- Gender stereotypes about intellectual ability emerge early and influence children’s interests
- Student Experience Research Network’s portfolio of research on mindsets and the learning environment
- The frequency of “brilliant” and “genius” in teaching evaluations predicts the representation of women and African Americans across fields
- When words really do matter: Subtle language cues convey stereotypes and activate mindsets that diminish young children’s performance
- Inductive generalization relies on category representations