Abstract: Whether we are trying to improve educational quality in the developed world or make a formal education available to a wider population, the salient challenge is dealing with variance and change in students' backgrounds and learning objectives. I argue that an automated, adaptive learning experience is the fastest way to improve education, and describe a formalization of content and student interactions that enables adaptation. I will present some of the technical challenges in delivering adaptation at web scale, and how Knewton is addressing them. Finally, I will propose where a platform like Knewton's fits into existing educational experiences, and invite discussion of the social implications of educational technology.

Bio: George Davis is lead data scientist at Knewton, Inc., a VC- backed learning platform startup aimed at bringing adaptive educational content to every student in the world. His team develops scalable statistical models of the learning process in order to inform recommendations such as supplementary content or study groups. George holds a PhD in Computation, Organizations and Society from Carnegie Mellon University’s School of Computer Science, where his researched focused on network analysis, game theory, and probabilistic graphical models as applied to problems in shipping, security, and economic networks.