DL4Ed: 2019 KDD Workshop on Deep Learning for Education Dena’ina Convention Center and William Egan Convention Center Anchorage, AK, United States, August 5, 2019 |
Conference website | http://ml4ed.cc/2019-kdd-workshop |
Submission link | https://easychair.org/conferences/?conf=dl4ed |
Submission deadline | May 5, 2019 |
Acceptance notification | June 1, 2019 |
Camera-ready deadline | June 15, 2019 |
Our workshop “Deep Learning for Education” will focus on applying deep learning to solve important problems in education. We believe in education because of its power to maximize human potential, and transform lives. It is also a critical component of our society, since both school education and lifelong education play a central role in the development of the world’s workforce. However, compared to other applications of significant societal impact (e.g., healthcare, transportation, and social sciences), education remains a highly under-explored application field by the data mining and machine learning research communities. This workshop builds upon our previous Machine Learning for Education workshops.
Relevant topics to this workshop include but are not limited to:
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Utilizing machine learning to expand learning beyond the privileged few to remote corners of the world.
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Using data mining to create alternatives in higher education that will prepare adults for the careers they want without the burden of excessive student debt.
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Using data mining to help reverse the trend in K-12 of increasing spending to achieve the same learning outcomes.
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Improving the interpretability of deep learning methods and using them to extract insights on how to improve learning outcomes.
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Incorporating cognitive and behavioral science principles in the development of deep learning methods to improve their performance.
Call for Papers
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All submissions must be in PDF format
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Submissions are limited to a maximum of nine pages, including all content and references; shorter (at least four pages) submissions are allowed
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Paper submission must be in the format specified in KDD submission direction available at: https://www.kdd.org/kdd2019/Calls/view/kdd-2019-call-for-research-papers
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There is no need to anonymize your submission
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Final acceptance of a submission will be conditioned on providing a camera-ready version of the paper that fits the formatting instructions
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Accepted papers will be shown on the workshop website but not be archived, thus submission does not preclude publications in other venues
Organizers
Andrew S. Lan University of Massachusetts Amherst
Byung-Hak Kim Udacity
Richard Baraniuk Rice University
Michael Mozer University of Colorado Boulder
Jacob Whitehill Worcester Polytechnic Institute
Contact Email: andrewlan@cs.umass.edu