NeurIPS Competition Instructions and Guide: Causal Insights for Learning Paths in Education
This addresses the problem of improving educational outcomes through causal insights, but it is incremental as it focuses on a competition framework rather than presenting new research results.
The competition tackles two causal challenges in education using time-series data: identifying causal relationships between learning constructs and predicting the impact of learning one construct on others, with the goal of optimizing student knowledge acquisition for potential deployment in edtech solutions affecting millions of students.
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.