Timezone: »

Causal Insights for Learning Paths in Education
Wenbo Gong · Digory Smith · Jack Wang · Simon Woodhead · Nick Pawlowski · Joel Jennings · Cheng Zhang · Craig Barton

Tue Dec 06 03:00 AM -- 06:00 AM (PST) @ Virtual
Event URL: https://codalab.lisn.upsaclay.fr/competitions/5626 »

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.

Author Information

Wenbo Gong (Microsoft)
Digory Smith (Eedi)
Jack Wang (Rice University)
Simon Woodhead (Eedi)
Simon Woodhead

Head of Research and co-founder of Eedi, and host of the Data Science in Education meetup in London. He leads machine learning research at Eedi, and turns this into new product features. With experience leading both product development and research, he has created award-winning edtech solutions with strong data science foundations.

Nick Pawlowski (Microsoft Research)
Joel Jennings (Microsoft Research)
Cheng Zhang (Microsoft Research, Cambridge, UK)

Cheng Zhang is a principal researcher at Microsoft Research Cambridge, UK. She leads the Data Efficient Decision Making (Project Azua) team in Microsoft. Before joining Microsoft, she was with the statistical machine learning group of Disney Research Pittsburgh, located at Carnegie Mellon University. She received her Ph.D. from the KTH Royal Institute of Technology. She is interested in advancing machine learning methods, including variational inference, deep generative models, and sequential decision-making under uncertainty; and adapting machine learning to social impactful applications such as education and healthcare. She co-organized the Symposium on Advances in Approximate Bayesian Inference from 2017 to 2019.

Craig Barton (Eedi)

More from the Same Authors