Directors: Antonio M. Battro and Kurt W. Fischer
Program officer: María Lourdes Majdalani
Rethinking knowledge transfer: insights from an artificial neural network model
Knowledge transfer has been called the "holy grail" of education. In part, this is because an understanding of its basic operation eludes researchers despite a century of psychological and behavioral research, frustrating efforts to design educational materials and experiences that produce significant transfer reliably. In this presentation, I discuss insights about possible neural mechanisms of knowledge transfer gleaned through analysis of an artificial neural network model. Based on mechanisms identified in the model, I propose a framework for understanding how knowledge transfer operates at the neural level. I apply this framework to provide tentative answers to such longstanding puzzles as:
• Why do we see a lot of near transfer and not a lot of far?
• How can A transfer to B much more than B transfers to A?
• How can we define “distance” of transfer?
My aim in characterizing the underlying mechanisms of transfer in this way is to identify promising opportunities to exploit them in educational research, design and practice.