Workshop
Advances and Opportunities: Machine Learning for Education
Kumar Garg · Neil Heffernan · Kayla Meyers
Fri 11 Dec, 5:30 a.m. PST
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!
Schedule
Fri 5:25 a.m. - 5:30 a.m.
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Welcome address
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Remarks
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Kumar Garg 🔗 |
Fri 5:30 a.m. - 5:40 a.m.
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Opening Remarks from National Science Foundation Director Sethuraman Panchanathan
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Remarks
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SlidesLive Video |
Sethuraman Panchanathan 🔗 |
Fri 5:45 a.m. - 6:45 a.m.
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Panel discussion on effective partnerships to leverage machine learning and improve education
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Panel
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SlidesLive Video |
Kumar Garg · Steve Ritter · Heejae Lim · Jeremy Roschelle 🔗 |
Fri 6:45 a.m. - 7:15 a.m.
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Carolyn Rosé, Professor of Human-Computer Interaction at Carnegie Mellon University, The power of intelligent conversation systems in collaborative learning
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Talk
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SlidesLive Video |
Carolyn Rosé 🔗 |
Fri 7:15 a.m. - 7:30 a.m.
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Jacob Whitehill, Assistant Professor of Computer Science at Worcester Polytechnic Institute, Using machine learning to create scientific instruments for classroom observation
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Talk
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SlidesLive Video |
Jacob Whitehill 🔗 |
Fri 7:50 a.m. - 8:50 a.m.
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Panel discussion on ImageNets for education
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Panel
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SlidesLive Video |
Kumar Garg · John Whitmer · Aigner Picou · Scott Crossley 🔗 |
Fri 9:00 a.m. - 9:30 a.m.
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Spotlight on ImageNets for Education
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Spotlight
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Fri 9:30 a.m. - 9:40 a.m.
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Joon Suh Choi, PhD Candidate at Georgia State University on research on ARTE
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Talk
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SlidesLive Video |
Joon Suh Choi 🔗 |
Fri 9:40 a.m. - 10:10 a.m.
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Zachary Pardos, Associate Professor, Graduate School of Education, University of California, Berkeley, "Neural course embedding for recommendation"
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Talk
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SlidesLive Video |
Zachary Pardos 🔗 |
Fri 10:10 a.m. - 10:30 a.m.
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Alina von Davier, Chief of Assessment, Duolingo, Machine learning and next generation assessments
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Talk
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SlidesLive Video |
Alina von Davier 🔗 |
Fri 10:30 a.m. - 11:30 a.m.
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Panel discussion of talent pipeline into education research and the learning engineering field
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Panel
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SlidesLive Video |
Kumar Garg · Richard Tang · Ajoy Vase · Ken Koedinger 🔗 |
Fri 11:40 a.m. - 12:00 p.m.
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Remarks from Burr Settles, Research Director, DuoLingo
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Remarks
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SlidesLive Video |
Burr Settles 🔗 |
Fri 12:00 p.m. - 12:10 p.m.
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Remarks from Candace Marie Thille, Director of Learning Sciences, Amazon.com
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Remarks
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Candace Marie Thille 🔗 |
Fri 12:15 p.m. - 12:30 p.m.
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Ryan Baker, Assistant Professor of Economics and Education at the University of Pennsylvania, Predicting students’ affect and motivation through meta-cognitive data
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Talk
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SlidesLive Video |
Ryan S. Baker 🔗 |
Fri 12:30 p.m. - 12:40 p.m.
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Discussion on how young technologists can contribute to learning engineering
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Panel
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SlidesLive Video |
Kumar Garg · Michelle Park · Katherine Binney · Jonathan J Mak 🔗 |
Fri 12:40 p.m. - 1:00 p.m.
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Remarks from Bryan Richardson, Senior Program Officer, the Bill & Melinda Gates Foundation’s K-12 program
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Remarks
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SlidesLive Video |
Bryan Richardson 🔗 |
Fri 1:00 p.m. - 2:00 p.m.
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Panel discussion on minimizing bias in machine learning in education
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Panel
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SlidesLive Video |
Neil Heffernan · Osonde A. Osoba · Emma Brunskill · Kathi Fisler 🔗 |
Fri 2:00 p.m. - 2:15 p.m.
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Closing remarks from Fei-Fei Li, Sequoia Professor of Computer Science, Stanford University & Co-Director of Stanford’s Human-Centered AI Institute
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Closing Remarks
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SlidesLive Video |
Li Fei-Fei 🔗 |
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ImageNets for the Whole Child
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Spotlight
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SlidesLive Video |
Daniel Jarratt · Paola Martinez 🔗 |
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ImageNets for Math Errors
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Spotlight
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SlidesLive Video |
Nishchal Shukla · Sam Ching 🔗 |
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ImageNets for Teaching CS
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Spotlight
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SlidesLive Video |
Tiffany Barnes · Thomas Price · Jim Larimore 🔗 |
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ImageNets for Math Handwriting Recognition: Aida Calculus Dataset
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Spotlight
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SlidesLive Video |
Zachary Hancock · Chase Thomas 🔗 |
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ImageNets for Reading
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Spotlight
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SlidesLive Video |
John Gabrieli · Perpetual Baffour 🔗 |