In this competition, participants will address two fundamental causal challenges in machine learning in the context of education using time-series data. The first is to identify the causal relationships between different constructs, where a construct is defined as the smallest element of learning. The second challenge is to predict the impact of learning one construct on the ability to answer questions on other constructs. Addressing these challenges will enable optimisation of students' knowledge acquisition, which can be deployed in a real edtech solution impacting millions of students. Participants will run these tasks in an idealised environment with synthetic data and a real-world scenario with evaluation data collected from a series of A/B tests.
Tue 3:00 a.m. - 3:30 a.m.
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Overview of the competition
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Overview
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Overview of the competition, including the real-world causal challenges, introduction to the competition tasks and potential impact to the education industry. |
Simon Woodhead · Wenbo Gong 🔗 |
Tue 3:30 a.m. - 4:15 a.m.
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Invited talk
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Andrew Lan 🔗 |
Tue 4:15 a.m. - 4:30 a.m.
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Break
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Tue 4:30 a.m. - 5:15 a.m.
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Top Team Presentation 1
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Presentation
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Tue 5:15 a.m. - 5:50 a.m.
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Top Team Presentation 2
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Presentation
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Tue 5:50 a.m. - 5:55 a.m.
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Winner Announcement
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Announcement
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Tue 5:55 a.m. - 6:00 a.m.
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Closing Statement
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Presentation
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Fifteen-minute Competition Overview Video
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Overview
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SlidesLive Video » |
Jack Wang · Joel Jennings · Cheng Zhang · Wenbo Gong · Simon Woodhead · Nick Pawlowski · Digory Smith · Craig Barton 🔗 |