Advancing pedagogy responsibly using big data to power our mission to help every child build the skills and confidence they need to thrive in a rapidly changing world.

About us

Eedi hosts the largest repository of diagnostic questions in the world. With over 120 million answers we have a unique data set with which to understand students’ misconceptions. In collaboration with Microsoft Research we have developed ground-breaking machine learning models to learn from this data and understand and direct students’ learning journeys to help improve learning outcomes. We are committed to sharing our findings and data to further the field

Open calls

Open call for 25 researchers

Deadline: 1st May 2022
Find out more


In 2020 we hosted the NeurIPS Education Challenge to predict student responses, determine question quality, and identify a personalised set of questions for each student. Our competition received 3696 entries from 382 teams, and many research papers have been published using the competition data set. In 2021 the data set that we released won the Best Publicly Available Educational Data Set Prize awarded by the Educational Data Mining Society. We continue to support the data set as part of our ongoing learning engineering project.

Our team

Simon Woodhead

Data scientist and co-founder

Digory Smith

Data scientist

Craig Barton

Teacher, best-selling author and co-founder

Rachel Kidson

Teacher and Educational Psychology Lead


VICause: Simultaneous Missing Value Imputation and Causal Discovery with Groups

Morales-Alvarez, P., Lamb, A., Woodhead, S., Jones, S. P., Allamanis, M., & Zhang, C. (2021)

arXiv preprint arXiv:2110.08223

CoRGi: Content-Rich Graph Neural Networks with Attention

Kim, J., Lamb, A., Woodhead, S., Jones, S. P., Zheng, C., & Allamanis, M. (2021)

arXiv preprint arXiv:2110.04866

School students’ confidence when answering diagnostic questions online

Foster, C., Woodhead, S., Barton, C., & Clark-Wilson, A. (2021)

Educational Studies in Mathematics, 1-31

Contextual HyperNetworks for Novel Feature Adaptation

Lamb, A., Saveliev, E., Li, Y., Tschiatschek, S., Longden, C., Woodhead, S., ... & Zhang, C. (2021)

arXiv preprint arXiv:2104.05860

Results and Insights from Diagnostic Questions: The NeurIPS 2020 Education Challenge

Wang, Z., Lamb, A., Saveliev, E., Cameron, P., Zaykov, J., Hernandez-Lobato, J. M., ... & Zhang, C. (2021, August)

In NeurIPS 2020 Competition and Demonstration Track (pp. 191-205). PMLR

Diagnostic questions: The neurips 2020 education challenge

Wang, Z., Lamb, A., Saveliev, E., Cameron, P., Zaykov, Y., Hernández-Lobato, J. M., ... & Zhang, C. (2020)

arXiv preprint arXiv:2007.12061

Large-scale educational question analysis with partial variational auto-encoders

Wang, Z., Tschiatschek, S., Woodhead, S., Hernández-Lobato, J. M., Jones, S. P., & Zhang, C. (2020)

arXiv preprint arXiv:2003.05980

On Formative Assessment in Math: How Diagnostic Questions Can Help

Barton, C. (2018)

American Educator, 42 (2), 33.