Workshop Description:
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.
In the realm of higher education, rising college tuition accompanied
with cuts in funding to schools and an ever increasing world
population that desires high-quality education at low cost has spurred
the need to use technology to transform how we deliver education.

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
+ 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
+ Analysis of social networks of students and teachers
+ Automated, semi-automated, and crowdsourced methods for formative
and summative assessment.

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.

Confirmed speakers:
+ Ken Koedinger, CMU
+ Andrew Ng, Coursera/Stanford (tentative)
+ Peter Norvig, Google
+ Zoran Popovic, UW
+ Jascha Sohl-Dickstein, Stanford/Khan Academy
+ Daniel Seaton, MIT/EdX

Confirmed Panelists:
+ Eliana Feasley, Khan Academy,
+ Una-May O'Reilly, MIT,
+ Jim Bower UT Austin/Numedeon
+ and well as the invited speakers.

Organizers:
+ Jonathan Huang, Stanford (jhuang11@stanford.edu)
+ Sumit Basu, Microsoft Research (sumitb@microsoft.com)
+ Kalyan Veeramachaneni, CSAIL, MIT (kalyan@csail.mit.edu)

Submission details:
Submissions should follow the NIPS format and are encouraged to be
up to six pages. Papers submitted for review do not need to be
anonymized. There will be no official proceedings, but the accepted
papers will be made available on the workshop website. Accepted papers
will be either presented (both) as a poster and a short spotlight
presentation. We welcome submissions on novel research work as well as
extended abstracts on work recently published or under review in
another conference or journal (please state the venue of publication
in the latter case); we encourage submission of visionary position
papers on the emerging trends in data driven education. Please submit
papers in PDF format tonipsdde2013@gmail.com.

For more information, please visit: lytics.stanford.edu/datadriveneducation