April 13, Friday's class: George Davis

[Elaine]

Title: Massively Adaptive Education


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.