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Advances and Opportunities: Machine Learning for Education
Kumar Garg · Neil Heffernan · Kayla Meyers

Fri Dec 11 05:30 AM -- 02:10 PM (PST) @
Event URL: https://www.ml4ed.org/ »

This workshop will explore how advances in machine learning could be applied to improve educational outcomes.

Such an exploration is timely given: the growth of online learning platforms, which have the potential to serve as testbeds and data sources; a growing pool of CS talent hungry to apply their skills towards social impact; and the chaotic shift to online learning globally during COVID-19, and the many gaps it has exposed.

The opportunities for machine learning in education are substantial, from uses of NLP to power automated feedback for the substantial amounts of student work that currently gets no review, to advances in voice recognition diagnosing errors by early readers.

Similar to the rise of computational biology, recognizing and realizing these opportunities will require a community of researchers and practitioners that are bilingual: technically adept at the cutting-edge advances in machine learning, and conversant in most pressing challenges and opportunities in education.

With representation from senior representatives from industry, academia, government, and education, this workshop is a step in that community-building process, with a focus on three things:
1. identifying what learning platforms are of a size and instrumentation that the ML community can leverage,
2. building a community of experts bringing rigorous theoretical and methodological insights across academia, industry, and education, to facilitate combinatorial innovation,
3. scoping potential Kaggle competitions and “ImageNets for Education,” where benchmark datasets fine tuned to an education goal can fuel goal-driven algorithmic innovation.

In addition to bringing speakers across verticals and issue areas, the talks and small group conversations in this workshop will be designed for a diverse audience--from researchers, to industry professionals, to teachers and students. This interdisciplinary approach promises to generate new connections, high-potential partnerships, and inspire novel applications for machine learning in education.

​This workshop is not the first Machine Learning for Education workshop; there has been several (ml4ed.cc), and the existence of these others speaks to recognition of the the obvious importance that ML will have for education moving forward!

Author Information

Kumar Garg (Schmidt Futures)
Neil Heffernan (Worcester Polytechnic Institute)

Dr. Neil Heffernan Biography Dr. Heffernan is a Professor of Computer Science and Director of the Learning Sciences and Technologies program at Worcester Polytechnic Institute. Before entering academia, Neil taught middle school math and science in the Teach for America program in Baltimore, where he met his wife Cristina. While completing his Ph.D. in Computer Science at Carnegie Mellon University, Neil incorporated his passion for education and focused on educational technologies. In 1997, Neil had a seizure and was told he had brain cancer and two years to live. This traumatic event helped Neil and Cristina learn what was important to them: making the world a better place. helped motivate Dr. Heffernan to make this platform a free public service. Neil and Cristina created the ASSISTments platform as a free service that is used by 50,000 across the United States for daily classwork and nightly homework. In October, 2016 Dr. Heffernan was asked to present at the White House on the reproducibility crisis in educational research and the need for pre-registration and open-data. In December 2016, the Heffernans presented at the White House for a second time on the SRI evaluation that found ASSISTments doubled student learning). He has received national press from U.S. News, Scientific American, The New York Times, The Boston Globe and NPR. Dr. Heffernan has written 60+ papers on learning analytics and over two dozen papers on the results of randomized controlled trials.

Kayla Meyers (The Learning Agency)

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