[on behalf of Jiquan Ngiam]
Call for Papers: NIPS 2016 Workshop on Machine Learning for Education
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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
Questions about the workshop can be sent to ml4ed.nips2016@xxxxxxxxx
WORKSHOP SUMMARY
The NIPS 2016 Workshop on Machine Learning for Education will 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://dsp.rice.edu/ml4ed_nips2016
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://dsp.rice.edu/ml4ed_nips2016
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.
