research Projects & Grants

 In a nutshell, we LOVE learning and thinking about how people learn and think about math (very meta, we know!). How does mathematics knowledge develop over time? Why are so many people so afraid of math while others are so in love with it? Why do some people view math like a puzzle or game while others view them as confusing procedural rules and algorithms? Where do people who are "bad" at math get stuck? What are mathematical and contextual predictors of mathematics learning and achievement? What does good mathematics teaching look like? Why is teaching math so hard? What skills (other than math knowledge) are required to successfully teach and understand mathematics? These are just a few of the questions I try to answer in my research. 

My research aims to develop and evaluate classroom interventions that improve mathematics teaching and learning. My research is highly interdisciplinary and focuses on the intersections of educational, cognitive, and developmental psychology. My work is focused on the development, design, and testing of innovative classroom interventions and technologies that embed cognitive and educational principles of learning into everyday practice. I also use classroom observations, longitudinal data, and multi-level modeling to examine how mathematics and social-emotional learning (SEL) interventions in schools can enhance students’ opportunities to learn. Ultimately, I am interested in understanding how cognitive and non-cognitive pathways combine to produce learning and growth for all children in K-12 classrooms (and beyond!)

Some of my Funded Research Projects

Over the past five years, my work has been supported by over $7.5 million in research funding from the Institute of Education Sciences, National Science Foundation, Spencer Foundation, Hewlett Foundation, and the American Educational Research Association.

My work explores the ways that instructional interventions that integrate perceptual learning, gesture, and embodied cognition can improve student learning and engagement in mathematics. Many leading curricular programs in mathematics and science have often used more traditional algorithms and static representations to teach algebraic notation; however, there is some reason to think that students may better recognize the construction of algebraic notation if they dynamically transform expressions using manipulation of physical objects. In theory, perceptual training may help build students understanding of algebraic concepts by internalizing the appropriate way of visualizing and gesturing patterns in algebraic structure. Symbols are presented as literal (sometimes virtual) objects, which children can touch and move, and which respond in natural, object like ways (constrained by mathematical law). This novel, dynamic program allows students to explore patterns and properties of mathematics by rearranging, splitting, and manipulating numbers and expressions. To date, much of my work has focused on building and designing several instantiations of the approach and coordinating across multiple representations for flexible use in classrooms by both teachers and studentsWorking with an iterative design cycle has allowed me to evaluate what components of the intervention work best in applied classroom settings and has provided me great feedback about how to adapt the program to better fit the needs of teachers and students. My quantitative work provides evidence of the program’s efficacy and offers new and innovative insight into thinking about how we learn abstract concepts.


the design and development of GRASPABLE MATH

About 9 years ago, my colleague David Landy and I designed an initial prototype for a dynamic math notation that was based on theory and research in cognitive science and perceptual learning. What started as an initial idea has turned into a widely used dynamic algebra notation tool called Graspable Math. With the support by iES, we have developed several different initiations of this tool and my research has explored its impact on learning and the ways that we can use the data from students problem solving to understanding perceptual and mathematical mechanisms of learning.

Wearable Learning Cloud Platform


Automated Classroom Observation Using Machine Learning