NeurIPS 2020 Workshop
Advances and Opportunities: Machine Learning for Education
December 11th, 8:30am ET to 5:10pm ET
Summary:
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:
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.
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:
- identifying what learning platforms are of a size and instrumentation that the ML community can leverage,
- building a community of experts bringing rigorous theoretical and methodological insights across academia, industry, and education, to facilitate combinatorial innovation,
- 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.
Call for Ideas:
Deadline: October 9th
Registration & Participation:
For registration and pricing see here. Registration for the conference is mandatory.
If you would like to participate in our workshop, RSVP using the button below and we will share details closer to the day of the workshop.
If you would like to participate in our workshop, RSVP using the button below and we will share details closer to the day of the workshop.
Schedule:
All times in U.S. eastern time. Schedule is subject to change.
8:30am: Opening keynote from National Science Foundation Director Sethuraman Panchanathan
8:45am: Panel discussion on effective partnerships to leverage machine learning and improve education.
9:45am: Talk from Carolyn Rosé (Carnegie Mellon University) on The power of intelligent conversation systems in collaborative learning
10:00am: Talk from Jacob Whitehill (Worcester Polytechnic Institute) on using machine learning to create scientific instruments for classroom observation
10:15am: Talk from Sidney D'Mello (University of Colorado, Boulder) Towards Natural Social Interaction: Multiparty, Multimodal Machine Learning for Education
10:30am: Panel discussion of ImageNets for education
11:30am: Breakout session on ImageNets for Education [see Call for Ideas] with Jim Larimore (Riiid Labs)
12:40pm: Talk from Zachary Pardos, (University of California, Berkeley) on customizing course sequence with machine learning
1:10pm: Talk from Alina von Davier (Duolingo) on machine learning and next generation assessments
1:30pm: Panel discussion on the talent pipeline into education research and the learning engineering field
2:40pm: Remarks from Burr Settles (DuoLingo)
3:00pm: Remarks from Candace Marie Thille (Amazon.com)
3:15pm: Talk from Ryan Baker (University of Pennsylvania) on predicting students’ affect and motivation through meta-cognitive data
3:30pm: Talk from Ashok Krishnamurthy (University of North Carolina, Chapel Hill) on building research infrastructure across disciplines
3:50pm: Remarks from Bryan Richardson (Bill & Melinda Gates Foundation)
4:00pm: Panel discussion on minimizing bias in machine learning in education
5:00pm: Closing remarks from Fei-Fei Li (Stanford University & Stanford’s Human-Centered AI Institute)
8:30am: Opening keynote from National Science Foundation Director Sethuraman Panchanathan
8:45am: Panel discussion on effective partnerships to leverage machine learning and improve education.
- Moderator: Kumar Garg (Schmidt Futures)
- Panelists: Steve Ritter (Carnegie Learning), Heejae Lim (TalkingPoints) and Jeremy Roschelle (Digital Promise)
9:45am: Talk from Carolyn Rosé (Carnegie Mellon University) on The power of intelligent conversation systems in collaborative learning
10:00am: Talk from Jacob Whitehill (Worcester Polytechnic Institute) on using machine learning to create scientific instruments for classroom observation
10:15am: Talk from Sidney D'Mello (University of Colorado, Boulder) Towards Natural Social Interaction: Multiparty, Multimodal Machine Learning for Education
10:30am: Panel discussion of ImageNets for education
- Moderator: Kumar Garg (Schmidt Futures)
- Panelists: John Whitmer (ACTnext), Scott Andrew Crossley (Georgia State University), and Aigner Piccou (The Learning Agency Lab)
11:30am: Breakout session on ImageNets for Education [see Call for Ideas] with Jim Larimore (Riiid Labs)
12:40pm: Talk from Zachary Pardos, (University of California, Berkeley) on customizing course sequence with machine learning
1:10pm: Talk from Alina von Davier (Duolingo) on machine learning and next generation assessments
1:30pm: Panel discussion on the talent pipeline into education research and the learning engineering field
- Moderator: Kumar Garg (Schmidt Futures)
- Panelists: Richard Tang (University of California Berkeley), Ajoy Vase (Teachers College Columbia University), and Ken Koedinger (Carnegie Mellon University)
2:40pm: Remarks from Burr Settles (DuoLingo)
3:00pm: Remarks from Candace Marie Thille (Amazon.com)
3:15pm: Talk from Ryan Baker (University of Pennsylvania) on predicting students’ affect and motivation through meta-cognitive data
3:30pm: Talk from Ashok Krishnamurthy (University of North Carolina, Chapel Hill) on building research infrastructure across disciplines
3:50pm: Remarks from Bryan Richardson (Bill & Melinda Gates Foundation)
4:00pm: Panel discussion on minimizing bias in machine learning in education
- Moderator: Neil Heffernan (ASSISTments)
- Panelists: Osonde Osoba (RAND Corporation), Emma Brunskill (Stanford University), and Kathi Fisler (Brown University)
5:00pm: Closing remarks from Fei-Fei Li (Stanford University & Stanford’s Human-Centered AI Institute)
Organizers:
- Neil Heffernan: William Smith Dean's Professor of Computer Science at Worcester Polytechnic Institute, and Co-Founder of ASSISTments.
- Kumar Garg: Managing Director and Head of Partnerships at Schmidt Futures where he oversees the Learning Engineering portfolio.
Invited Speakers:
- Ryan Baker: Associate Professor in the Graduate School of Education at University of Pennsylvania
- Emma Brunskill: Assistant Professor in the Computer Science Department at Stanford University
- Scott Andrew Crossley: Professor of Linguistics and Applied Learning Sciences at Georgia State University
- Alina von Davier: Chief of Assessment at Duolingo
- Sidney D’Mello: Associate Professor in the Institute of Cognitive Sciences at the University of Colorado Boulder
- Kathi Fisler: Research Professor of Computer Science at Brown University
- Ken Koedinger: professor of Human Computer Interaction and Psychology at Carnegie Mellon University
- Ashok Krishnamurthy: Deputy Director of the Renaissance Computing Institute (RENCI), Research Professor of Computer Science at the University of North Carolina, Chapel Hill, and Director for the Biomedical Informatics Service at NC TraCS
- Jim Larimore: Chief Officer for Equity in Learning at Riiid Labs
- Fei-Fei Li: Sequoia Professor of Computer Science at Stanford University & the Co-Director of Stanford’s Human-Centered AI Institute.
- Heejae Lim: Founder & CEO of TalkingPoints
- Osonde Osoba: Senior Information Scientist at the RAND Corporation
- Sethuraman Panchanathan: Director, National Science Foundation
- Zachary Pardos: Associate Professor, Graduate School of Education, University of California, Berkeley
- Aigner Piccou: Program Director at The Learning Agency Lab
- Bryan Richardson: Senior Program Officer at the the Bill & Melinda Gates Foundation’s K-12 Program
- Steve Ritter: Founder & Chief Scientist at Carnegie Learning
- Jeremy Roschelle: Executive Director of Digital Promise
- Carolyn Rosé: Professor in Human-Computer Interaction Institute and Language Technologies Institute at Carnegie Mellon University
- Burr Settles: Research Director at Duolingo
- Richard Tang: Student at the University of California, Berkeley
- Candace Marie Thille: Director of Learning Sciences at Amazon.com
- Ajoy Vase: COO of the Learning Collider at Teachers College, Columbia University
- Jacob Whitehill: Assistant Professor of Computer Science at Worcester Polytechnic Institute
- John Whitmer: Former Senior Director of Data Science & Analytics at ACTnext