Opening keynote, Zoran Popovic

Professor and Director the Center for Game Science, University of Washington
Founder and Chief Scientist, Enlearn
Achieving 96% mastery at national scale through inspired learning and generative adaptivity
Most of the current research on improving learning outcomes focuses on a small subset of variables of an immensely multi-dimensional space of the learning ecosystem. Most digital learning tools primarily focus on individual students, other research focuses only on teacher professional development, or only on curriculum improvement. In this talk I will describe our efforts on how to discover optimal parameters of the entire ecosystem that considers student factors (engagement and mastery), classroom factors (blended learning variations and group learning variations), curriculum factors (multidimensional variation of existing curricula), and teacher factors (in-class tools that mitigate weaknesses, and promote teacher development). I will describe our work on algorithms to discover optimal learning pathways in this high-dimensional space. I will conclude with the outcomes of deploying a portion of our platform on algebra challenges conducted on two US states and the country of Norway.

Zoran Popovic is a Director of Center for Game Science at University of Washington and founder of Enlearn. Trained as a computer scientist his research focus is on creating interactive engaging environments for learning and scientific discovery. His laboratory created Foldit, a biochemistry game that produced three Nature publications in just two years, an award-winning math learning games played by over five million learners worldwide. He is currently focusing on engaging methods that can rapidly develop experts in arbitrary domains with particular focus on revolutionizing K-12 math education. His Algebra Challenges conducted in Washington, Minnesota, and Norway, have shown that 96% of children even in elementary school can learn key algebra concepts in 1.5 hours. He has recently founded Enlearn to apply his work on generative adaptation to any curricula towards the goal of achieving full mastery by 95% of students. His contributions to the field of interactive computer graphics have been recognized by a number of awards including the NSF CAREER Award, Alfred P. Sloan Fellowship and ACM SIGGRAPH Significant New Researcher Award.

Closing keynote, Peter Norvig

Open to CSCW attendees

Director of Research, Google Inc.
Machine Learning for Learning at Scale
There is great enthusiasm for the idea that massive amounts of data from online interactions of learners with material can lead to a rapid improvement cycle, driven by analysis of the data, experimentation, and intervention to do more of what works and less of what doesn't. This talk discusses techniques for working with massive amounts of data.

Peter Norvig is a Director of Research at Google Inc. Previously he was head of Google's core search algorithms group, and of NASA Ames's Computational Sciences Division, making him NASA's senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has taught at the University of Southern California and the University of California at Berkeley, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He was co-teacher of an Artifical Intelligence class that signed up 160,000 students, helping to kick off the current round of massive open online classes. His publications include the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world's longest palindromic sentence. He is a fellow of the AAAI, ACM, California Academy of Science and American Academy of Arts & Sciences.