😂 it looks like I’m not paying attention but I’m taking notes on your awesome presentation!!

That explains the follows overnight 😂 I’m honored 🥰

Thank you!! ☺️

This one is special to me – first paper as PI of my own lab, with phd student Anastasia Chouvalova and postdoc (now faculty) Anisha Navlekar as co-first authors, undergrad researchers Sami Daye, Mikayla Adams, & Fatima de Anda, and now I'm co-authors with my friend & stats wiz, Devin Mills!

Our results suggest that students who believe their intellectual abilities can improve are more likely to respond adaptively to making errors in the learning process and are more likely to space their study sessions out and use highly effective, error-prone study strategies

#socialpsyc #cogpsyc #cogsci

Could replicability problems in mindset lit be at least partially due to measurement error? I think it’s possible. Stay tuned for my next paper on this topic.

Coming next: we compared the predictive efficacy of the ULTrA vs. previously-existing mindset measure and found it reliably predicts more variance in all outcomes we checked for science & math undergrads.

I am beyond words to see this become a final product. I poured and heart into this project for >4 years and I am so proud of how it came out. The official acceptance coming in while I was on my honeymoon was such a nice cherry on top! Thanks CBE-LSE for the wedding gift!

We found each belief contributes unique predictive value for at least one outcome, and each outcome is predicted by a unique combination of beliefs

Take-away 3: if you’re interested in mindset, may I suggest taking an interest in universality and brilliance as well? These are also beliefs students have about their abilities and success, and our results indicate that they explain unique variance in outcomes we care about

Growth, but not fixed, explained variance in intent to persist and course grade. In contrast, fixed, but not growth, explained variance in evaluative concern and self-handicapping. This means it’s not enough to measure only growth or fixed, you could miss interesting nuance!

Take-away 2: Our data tell us that growth and fixed are not opposite ends of a single spectrum, but distinct factors that load onto a higher-order mindset factor. SEMs showed that growth & fixed related to outcomes differently, while controlling for demographics & other beliefs.

Take-away 1: if you’re interested in science & math undergraduates’ mindsets, you should use this measure! 5 items per beliefs, 25 items total. Freely available in table 3 and would love to answer any questions about how to use it.

We found that mindset, universality, and brilliance beliefs are distinct and that each uniquely predict psychosocial and academic outcomes. In the process we developed a new survey to measure these beliefs supported by extensive evidence of validity – the ULTrA Survey.