[on behalf of Andrew Lan]
IMPORTANT DATES
- Paper submission deadline: September 30th, 2016
- Author notification: October 17th, 2016
- Final version of accepted submissions: November 4th, 2016
- Final workshop schedule: November 4th, 2016
- Workshop: December 10th, 2016
Please send your submissions via email to ml4ed.nips2016@xxxxxxxxx
<mailto:ml4ed.nips2016@xxxxxxxxx>
Questions about the workshop can be sent to ml4ed.nips2016@xxxxxxxxx
<mailto:ml4ed.nips2016@xxxxxxxxx>
WORKSHOP SUMMARY
The NIPS 2016 Workshop on Machine Learning for Educationwill be held in
conjunction with the Neural Information Processing Systems (NIPS)
conference on Saturday, December 10, 2016 in Barcelona, Spain. The
workshop will bring together machine learning experts and researchers in
education and cognitive psychology to discuss how we can solve
fundamental problems in learning and education. At this year's workshop,
we are highlighting the following areas of interest: (1) assessments and
grading, (2) content augmentation and understanding, (3) personalized
learning and active interventions, and (4) human-interpretability.
More details about the workshop can be found below, or on the workshop
website: http://ml4ed.cc
PAPER SUBMISSION
We welcome submissions with either results that have not been published
previously or a summary of the authors' previous work that has been
recently published or is under review in another conference or journal.
We also encourage authors to submit extended abstracts and
work-in-progress papers with only preliminary results.
Submissions will be judged on their novelty and potential impact in the
emerging field of machine learning for education. Submissions should
follow the regular NIPS paper format; there are no page limits. Papers
submitted for review need not be anonymized. Accepted papers will be
made publicly available on the workshop website, since there will be no
official proceedings. Accepted papers will be presented either as a talk
or a poster.
For more information about the workshop see below or visit the workshop
website: http://ml4ed.cc
For more information about the NIPS conference, please visit
https://nips.cc/
Looking forward to seeing you in Barcelona!
Richard G. Baraniuk
Jiquan Ngiam
Christoph Studer
Phillip Grimaldi
Andrew Lan
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WORKSHOP DETAILS
The goal of this workshop is to foster discussion and spur research
between machine learning experts and researchers in education fields
that can solve fundamental problems in education. We invite the
submission of papers on all topics related to the application of machine
learning to education. For this year's workshop, we are highlighting the
following areas of interest:
1) Assessments and grading: Assessments are core in adaptive learning,
formative learning, and summative evaluation. However, the creation and
grading of quality assessments remains a difficult task for instructors.
Machine learning methods can be applied to self-, peer-, auto-grading
paradigms to both improve the quality of assessments and reduce the
burden on instructors and students. These methods can also leverage the
multimodal nature of learner data (i.e.,
textual/programming/mathematical open-form responses, demographic
information, student interaction in discussion forums, video and audio
recording of the class), posing challenges of how to effectively and
efficiently fuse these different forms of data so that we can better
understand learners.
2) Content augmentation and understanding: Learning content is rich and
multimodal (e.g., programming code, video, text, audio). There has been
a growth of online educational resources in the past years, and we have
an opportunity to leverage them further. Recent advances in natural
language understanding can be applied to understand learning materials
better and connect different sources together to create better learning
experiences. This can help learners by providing them with more relevant
resources and instructors in the creation of content.
3) Personalized learning and active interventions: Personalized learning
through custom feedback and interventions can make learning much more
efficient, especially when we cater to the individual's background,
goals, state of understanding, and learning context. Methods such as
Markov Decision Processes and Multi-armed Bandits are applicable in
these context.
4) Human-interpretability: In education applications, transparency and
interpretability is important as it can help learners better understand
their learning state. Interpretability can provide instructors with
insights to better guide their activities with students. It can also
help education researchers better understand the foundations of human
learning; this can also be especially critical where models are deployed
in processes that grade students, as evaluation needs to demonstrate a
degree of fairness.
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yours,
Lan
