Given the incredible technological leaps that have changed so many aspects of our lives in the last hundred years, it’s surprising that our approach to education today is much the same as it was a century ago. While successful educational technologies have been developed and deployed in some areas, we have yet to see a widespread disruption in teaching methods at the primary, secondary, or post-secondary levels. However, as more and more people gain access to broadband internet, and new technology-based learning opportunities are introduced, we may be witnessing the beginnings of a revolution in educational methods. With college tuitions rising, school funding dropping, test scores falling, and a steadily increasing world population desiring high-quality education at low cost, the impact of educational technology seems more important than ever. With these technology-based learning opportunities, the rate at which educational data is being collected has also exploded in recent years as an increasing number of students have turned to online resources, both at traditional universities as well as massively open-access online courses (MOOCs) for formal or informal learning. This change raises exciting challenges and possibilities particularly for the machine learning and data sciences communities. These trends and changes are the inspiration for this workshop, and our first goal is to highlight some of the exciting and impactful ways that our community can bring tools from machine learning to bear on educational technology. Some examples include (but are not limited to) the following: + Adaptive and personalized education + Assessment: automated, semi-automated, and peer grading + Gamification and crowdsourcing in learning + Large scale analytics of MOOC data + Multimodal sensing + Optimization of pedagogical strategies and curriculum design + Content recommendation for learners + Interactive Tutoring Systems + Intervention evaluations and causality modeling + Supporting collaborative and social learning + Data-driven models of human learning The second goal of the workshop is to accelerate the progress of research in these areas by addressing the challenges of data availability. At the moment, there are several barriers to entry including the lack of open and accessible datasets as well as unstandardized formats for such datasets. We hope that by (1) surveying a number of the publicly available datasets, and (2) proposing ways to distribute other datasets such as MOOC data in a spirited panel discussion we can make real progress on this issue as a community, thus lowering the barrier for researchers aspiring to make a big impact in this important area. Target Audience: + Researchers interested in analyzing and modeling educational data, + Researchers interested in improving or developing new data-driven educational technologies, + Others from the NIPS community curious about the trends in online education and the opportunities for machine learning research in this rapidly-developing area.
http://lytics.stanford.edu/datadriveneducation
