Webinars and Recordings
Improving Automated Detection of Student Disengagement and Affect
December 11, 2020 3:15 pm ET
Ryan Baker (University of Pennsylvania)
“This talk is part of the NeurIPS 2020 Workshop Advances and Opportunities: Machine Learning for Education. This workshop explored 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!”