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Fri Dec 11 05:30 AM -- 02:10 PM (PST)
Advances and Opportunities: Machine Learning for Education
Kumar Garg · Neil Heffernan · Kayla Meyers

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!

Welcome address (Remarks)
Opening Remarks from National Science Foundation Director Sethuraman Panchanathan (Remarks)
Panel discussion on effective partnerships to leverage machine learning and improve education (Panel)
Carolyn Rosé, Professor of Human-Computer Interaction at Carnegie Mellon University, The power of intelligent conversation systems in collaborative learning (Talk)
Jacob Whitehill, Assistant Professor of Computer Science at Worcester Polytechnic Institute, Using machine learning to create scientific instruments for classroom observation (Talk)
Panel discussion on ImageNets for education (Panel)
Spotlight on ImageNets for Education (Spotlight)
Joon Suh Choi, PhD Candidate at Georgia State University on research on ARTE (Talk)
Zachary Pardos, Associate Professor, Graduate School of Education, University of California, Berkeley, "Neural course embedding for recommendation" (Talk)
Alina von Davier, Chief of Assessment, Duolingo, Machine learning and next generation assessments (Talk)
Panel discussion of talent pipeline into education research and the learning engineering field (Panel)
Remarks from Burr Settles, Research Director, DuoLingo (Remarks)
Remarks from Candace Marie Thille, Director of Learning Sciences, Amazon.com (Remarks)
Ryan Baker, Assistant Professor of Economics and Education at the University of Pennsylvania, Predicting students’ affect and motivation through meta-cognitive data (Talk)
Discussion on how young technologists can contribute to learning engineering (Panel)
Remarks from Bryan Richardson, Senior Program Officer, the Bill & Melinda Gates Foundation’s K-12 program (Remarks)
Panel discussion on minimizing bias in machine learning in education (Panel)
Closing remarks from Fei-Fei Li, Sequoia Professor of Computer Science, Stanford University & Co-Director of Stanford’s Human-Centered AI Institute (Closing Remarks)
ImageNets for Math Handwriting Recognition: Aida Calculus Dataset (Spotlight)
ImageNets for Reading (Spotlight)
ImageNets for the Whole Child (Spotlight)
ImageNets for Teaching CS (Spotlight)
ImageNets for Math Errors (Spotlight)