Machine Learning for Education: Student Models and Conceptual Understanding

This lecture covers the use of student models for individualization and insights in education, focusing on interaction data such as key strokes and speech. It also explores the early prediction of conceptual understanding using machine learning models and the transferability and explainability of these models in different educational contexts.