Call for Papers

NIPS 2016 Workshop on Machine Learning for Education

by Jiquan Ngiam

We are excited to co-organize a workshop at NIPS 2016 on applications of machine learning towards education. We are looking for contributions to the workshop and welcome you to join us at NIPS!

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:

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.
  • 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 Questions about the workshop can be sent to the same email address.

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:

For more information about the NIPS conference, please visit

Looking forward to seeing you in Barcelona!

Workshop organizers: * Richard G. Baraniuk * Jiquan Ngiam * Christoph Studer * Phillip Grimaldi * Andrew Lan

ML at Coursera

If you are interested in applying machine learning to education, we are hiring!

Originally published at on August 22, 2016.