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Tuesday, June 5 • 1:15pm - 2:00pm
Bayesian Inference in Education

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Student learning, course effectiveness, and teacher grading difficulty are examples of critical educational factors that cannot be directly observed and must be inferred.  In this talk, I will show how to use Bayesian inference in the educational domain to estimate these quantities, and discuss how we can use state space models to update our estimates as the underlying properties change over time.  In the process, I will introduce Stan, a probabilistic programming framework, and discuss best practices in model evaluation and model comparison.

avatar for Thomas Christie

Thomas Christie

Data Scientist, Infinite Campus
Thomas Christie is a data scientist at Infinite Campus, a student information system for 8 million students across the United States.  At Infinite Campus, Thomas applies state-of-the-art machine learning techniques to solve problems in education.  Thomas is also graduate student... Read More →
avatar for Taavi Taijala

Taavi Taijala

PhD Student, University of Minnesota

Tuesday June 5, 2018 1:15pm - 2:00pm
P0808 A&B Normandale Partnership Center, 9700 France Ave So, Bloomington, MN 55431