NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education

• URL: http://arxiv.org/abs/2208.12610v2
• Date: Wed, 31 Aug 2022 19:55:54 GMT
• Title: NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
• Authors: Wenbo Gong, Digory Smith, Zichao Wang, Craig Barton, Simon Woodhead, Nick Pawlowski, Joel Jennings, Cheng Zhang
• Abstract summary: 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.
• Score: 15.014508164142075
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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