We are scientists who study knowledge acquisition.
We develop learning theories by drawing on our diverse backgrounds—in psychology, computer science, artificial intelligence, neuroscience, linguistics, and human-computer interaction.
We are learning scientists. We draw inspiration from classic learning theories in education and psychology, like those by Jean Piaget, Maria Montessori, and Lev Vygotsky. We build computational models inspired by these classic theories that allow us to make specific predictions and generate testable competing hypotheses about learning dynamics (for example, the relationship between the learning context and learning outcomes).
We also design behavioral experiments to empirically differentiate between competing learning theories. Our experiments measure how learners attend and explore throughout the process of learning. Different experiments measure how humans look, explore, and play, starting in infancy and continuing throughout childhood.
We use eye-trackers to measure visual fixations to screens during passive viewing, and touchscreens to study touch-based exploration in kid-friendly apps, in addition to studying more traditional play behaviors.
Our results are quantitative theories about how data interacts with learners’ growing knowledge. These formal theories can function as the “back-end” for learning technologies, in addition to informing parenting, educational, and clinical practices.