The Teacher in the Machine
This approach demands broad collaboration from many fields. Dr. Sejnowski contributes neuroscience expertise – his research team studies brain activity related to social interactions. They have identified brain machinery that supports perception and action elements of learning.
Their computer scientist collaborators use machine-learning algorithms to understand how children use computational skills to infer structured models of their environment. Developmental psychologists study specific social factors that appear to be essential to learning, such as imitation, shared attention and empathy. Together, these researchers use classrooms as laboratories where they put all of this research into action to find the most effective teaching practices.
“To understand how children learn, and to improve our education system, we need to understand what all of these fields can contribute,” says Dr. Sejnowski, an Advisory Committee Member of CIFAR’s Neural Computation and Adaptive Perception Program.
These insights advance our understanding of how children learn. They are also helping to develop machines that are themselves capable of learning and possibly even of teaching. These machines, known as surrogate teachers, would merge social and instructional strengths, helping preschool-age children master basic skills such as the names of colours, new vocabulary and songs. Tailored to the needs of each child, the machines may also help track the child’s progress – thus the term “personalized pedagogy.”
“Our hope is that applying this new knowledge to learning will enhance educators’ ability to provide a much richer and more interesting intellectual and cultural life for everyone,” says Dr. Sejnowski.
This story relates to our research program: Neural Computation and Adaptive Perception