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.

翻译：在这场竞赛中,参与者将利用时间序列数据解决教育中机器学习方面的两个根本因果挑战,第一个挑战是确定不同结构之间的因果关系,其中将建筑定义为最小的学习要素;第二个挑战是预测学习一个结构对回答其他结构问题的能力的影响;应对这些挑战将使学生获得知识的工作最优化,这些知识可以运用于影响数百万学生的真正技术解决方案中;参与者将在一个理想的环境中,利用合成数据和从一系列A/B测试中收集的评价数据执行这些任务;现实世界情景将利用从一系列A/B测试中收集的评价数据。