NIPS Workshop on Data Driven Education (2013)

admin · 发表于 2014-2-6 11:17:57

Hotels Harrah's and Harveys Lake Tahoe, Nevada, USA
Tuesday December 10, 2013
Location: Harrah's Tahoe D

Schedule

TimeDurationEventSpeakerSlides
7:25-7:305 minWelcome and introductionOrganizers
7:30-8:0030 minDiscovering and removing barriers to learning Ken Koedinger (CMU)[pdf]
8:00-8:3030 minDemographics and learner behavior in MITx and HarvardX MOOCs Daniel Seaton (MIT, EdX)[pdf]
8:30-9:0030 minPersonalized Learning and Temporal Modeling at Khan Academy Jascha Sohl-Dickstein (Stanford, Khan Academy)[pdf]
9:00-9:3030 minCoffee Break
9:30-10:301 hrPoster Spotlights
10:30-3:30- Ski break and lunch -
3:00-3:3030 minCreating Infinitely Adaptable Courseware Zoran Popovic (UW)
3:30-4:0030 minWhat I've Learned about Learning Peter Norvig (Google)[pdf]
4:00-4:3030 minOpportunities and Challenges for Education Research on Coursera Andrew Maas (Stanford/Coursera)[pdf]
4:30-5:301 hrCoffee break and Poster Session
5:30-6:151 hrData PanelInvited panelists
ConclusionOrganizers

Workshop Description

Motivation and GoalsGiven 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.

Call for papers

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 [email protected].

Important Dates

Paper Submission --- October 16th, 2013 (extended deadline)
Author notification --- October 24th, 2013
Camera ready deadline for accepted submissions --- October 30th, 2013
Finalized workshop schedule out --- October 30th, 2013
Deadline for NIPS Early Registration --- November 8, 2013
Data Driven Ed Workshop --- December 10th, 2013

Invited Speakers

Ken Koedinger, CMU
Andrew Maas, Stanford/Coursera
Peter Norvig, Google
Zoran Popovic, UW
Jascha Sohl-Dickstein, Stanford/Khan Academy
Daniel Seaton, MIT/EdX

Panelists

Eliana Feasley, Khan Academy,
Una-May O'Reilly, MIT,
Jim Bower, Barshop Institute, University of Texas Health Science Center, San Antonio, Texas and Numedeon Inc.
Lori Breslow, MIT,

Accepted papers