I am a Lecturer in the Educational Psychology department at the University of Minnesota, and the Lab Coordinator and Affiliate Faculty of the Learning Informatics Lab.
How do children and adults learn and think about math, data, science, and programming? How do we form abstract knowledge from concrete experiences, and when is that useful? What can this tell us about learning and teaching in the classroom and beyond? My research blends cognitive science, learning science, and educational psychology approaches to try to answer these questions. I am passionate about teaching these and related topics, and helping social scientists learn open-source programming for reproducible research workflows.
My academic background is grounded in computational cognitive science and experimental psychology. As an undergraduate at Pomona College, I received a BA in Cognitive Science with a subconcentration in Computer Science and minor in Philosophy. I then received my MA and PhD in Cognitive Psychology at UCLA, with a specialization in Computational Cognition and minor in Quantitative Psychology.
During graduate school, I was president of Psychology in Action, where I also blogged and organized five interdisciplinary symposia.
PhD in Cognitive Psychology (Computational Cognition), 2016
University of California, Los Angeles
MA in Cognitive Psychology, 2011
University of California, Los Angeles
BA in Cognitive Science, 2009
Pomona College
A novel response time paradigm allows us to ‘decode’ individual differences in solving one-step algebra problems (e.g., x - 7 = 2). We find that adults vary in their strategy choices and flexibility for these problems. We also extend problem size effects from arithmetic to the algebraic context, but only for addition (not subtraction) problems.
Statistically trained graduate students are more likely to judge two p-values as being different (vs. similar) when they cross the traditional .05 significance boundary (e.g., .046 vs. .052), compared to when they did not (.026 vs. .032), after controlling for known numerical cognition effects. This is consistent with a categorical perception effect for p-values at the .05 boundary.
When asked to identify one or more matches to a target picture from an array of four options, the frequency with which preschoolers and adults identify a numerosity-based match varies as a function of the features on which the remaining match options are based (i.e., how salient they are).
Varying instructional method across conditions, we developed multimedia story sequences with systems-of-equations problems whose solution requires using variables.
An R package with Ethan C. Brown for tidyverse-friendly simulations and power analysis.