Limeri, L. B., Carter, N. T., Lyra, F., Martin, J., Mastronardo, H., Patel, J., & Dolan, E. L. (2023) Undergraduate Lay Theories of Abilities: Mindset, universality, and brilliance beliefs uniquely predict undergraduate educational outcomes. CBE – Life Sciences Education, 22(4) ar40. https://www.lifescied.org/doi/10.1187/cbe.22-12-0250
Students’ beliefs about the nature of their abilities (collectively called “lay theories”) affect their motivations, behaviors, and academic success.
There remain many open questions about how each of these beliefs influences students' academic trajectories and outcomes. To explore these areas, we need a valid and reliable tool to measure these beliefs. We developed the ULTrA Survey to measure these three lay theories in undergraduate science & math students.
The manuscript reporting the measure's development and validity evidence is published open-access:
Limeri, L. B., Carter, N. T., Lyra, F., Martin, J., Mastronardo, H., Patel, J., & Dolan, E. L. (2023) Undergraduate Lay Theories of Abilities: Mindset, universality, and brilliance beliefs uniquely predict undergraduate educational outcomes. CBE – Life Sciences Education, 22(4) ar40. https://www.lifescied.org/doi/10.1187/cbe.22-12-0250
The ULTrA Survey was designed specifically for the context of Science & Math undergraduate education. In a follow-up study, I found that it has greater predictive power for student outcomes in this context than the context-general measure. The ULTrA predicted a wider range of outcomes and with greater effect sizes than the context-general measure. In fact, it predicted students' intent to persist and grades, which the context-general measure failed to do. Mindset research has faced criticism for failures to replicate and small effect sizes - these results suggest that in undergraduate science and math, these issues could be due, in part, to measurement error.
You can read more about this study and its findings in this open-access publication:
Limeri, L. B. (2025). Intelligence in context: A context-specific mindset measure better predicts outcomes for science and math undergraduates. CBE-Life Sciences Education, 24(1), ar19. https://doi.org/10.1187/cbe.24-09-0229
Growth mindset
I can become as good at analyzing information as highly successful STEM professionals if I try hard enough.
If I want to, I can become as effective at applying knowledge as STEM experts.
I can become excellent at applying knowledge to solve challenging problems.
If I try, I can become as effective at learning as STEM experts.
I could improve my intellectual abilities to the same level as successful STEM professionals.
Fixed mindset
At the end of college, my ability to analyze information will be at about the same level that it is now.
How well I learn is something that I cannot change very much.
My ability to apply knowledge will change very little over time.
I will never be able to reach the highest level of intellectual ability.
It would be very difficult for me to improve how well I can apply knowledge.
Brilliance belief
Excelling in STEM requires natural talent.
People who are highly successful in STEM have a natural talent for it.
Becoming a top student in STEM requires an innate talent that just can’t be taught.
People have to be naturally talented to excel in challenging STEM courses.
Being a highly successful STEM professional requires natural talent that just can’t be taught.
Universal belief
With enough hard work, anyone could become as good at analyzing information as highly successful STEM professionals.
Anyone who tries could become as good at applying knowledge as STEM experts.
Anyone could become as effective at learning as highly successful STEM students.
Everyone has the intellectual ability to become a successful STEM professional if they want to.
With enough motivation, anyone can become as good at applying knowledge as high achieving STEM students.
Non-universal belief
Even if they try, some people could never become as effective at analyzing information as their peers.
Only people with a natural talent can become good enough at applying knowledge to solve the most difficult problems.
Only people with a natural talent can become excellent at analyzing information.
Some people will always be less effective at learning than those who have a natural talent for it.
Only some people have the intellectual ability to become a successful STEM professional.