Research Statement

My research asks what it means to genuinely understand something. I am interested in the difference between knowledge that lets a person predict, explain, and adapt, and knowledge that simply names or labels the world. Much of my theoretical work develops a framework for describing this difference — what richer understanding actually consists of, how it can be measured, and why classrooms so often produce the thinner version even when test scores look strong.

​

A second line of work applies this to persistent problems in science education. Why do people hold onto beliefs in the face of clear counter-evidence? Why does instruction on topics like evolution, vaccination, or climate so often fail to move students whose identities are tied to their views? Drawing on cognitive science, I am exploring advanced network models that treat knowledge as a dynamic prediction/self-preservation mechanism. 

​

More recently, my work has turned toward methods for detecting these epistemic structures in language itself. If richer and thinner forms of understanding leave different signatures in how people speak, write, and reason aloud, then careful analysis of narrative and text should let us identify those signatures at scale — in student work, in public discourse, and in the materials we use to teach. I am interested in what such analysis can reveal about how understanding develops, where it breaks down, and how it spreads through communities, and in building approaches that make those patterns useful for educators and researchers.

​

​​

​