posted by user: kalyanv || 3659 views || tracked by 15 users: [display]

NIPS- DDE 2013 : NIPS Workshop on Data Driven Education


When Dec 9, 2013 - Dec 9, 2013
Where Lake Tahoe, Nevada, USA
Submission Deadline Oct 9, 2013
Notification Due Oct 23, 2013
Final Version Due Oct 28, 2013
Categories    education   machine learning   moocs

Call For Papers

It is our pleasure to invite contributions to the NIPS 2013 Workshop on

Data Driven Education
December 9-10, 2013
Lake Tahoe, Nevada, USA

Important Dates:
+ Paper Submission --- October 9th, 2013
+ Author notification --- October 23rd, 2013
+ Camera ready deadline for accepted submissions --- October 28th, 2013
+ Finalized workshop schedule out --- October 30th, 2013
+ Data Driven Ed Workshop --- December 9th or 10th, 2013 (TBA)

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
+ 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.

Confirmed speakers:
+ Ken Koedinger, CMU
+ Andrew Ng, Coursera
+ 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,
+ and well as the invited speakers.

+ Jonathan Huang, Stanford (
+ Sumit Basu, Microsoft Research (
+ Kalyan Veeramachaneni, CSAIL, MIT (

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 to

For more information, please visit:

Related Resources

ANCRiSST 2019   14th International Workshop on Advanced Smart Materials and Smart Structures Technology
ICML 2019   36th International Conference on Machine Learning
COMPSAC 2019   COMPSAC 2019: Data Driven Intelligence for a Smarter World
ICCV 2019   International Conference on Computer Vision
ICMLA 2019   18th IEEE International Conference on Machine Learning and Applications
RecSys 2019   13th ACM Conference on Recommender Systems
MLDM 2019   15th International Conference on Machine Learning and Data Mining MLDM 2019
ICDMML 2019   【ACM ICPS EI SCOPUS】2019 International Conference on Data Mining and Machine Learning
Journal Special Issue 2019   Machine Learning on Scientific Data and Information
IEEE BigData 2019   IEEE International Conference on Big Data