Generalizable and Interpretable Models of Human Learning, by Prof. Tanja Käser

Inaugural Lecture - Generalizable and Interpretable Models of Human Learning
Abstract
Technology empowered by artificial intelligence has the potential to transform education by providing scalable and automated personalized tutoring to students and, at the same time, support teachers in classroom orchestration. However, current methods are limited: they are either defined for specific learning domains or lack interpretability and a foundation in learning theory. My group works on modeling human behavior and learning, with the goal to create models that are generalizable and explainable. In this talk, first discuss the key challenges in machine learning for education. I then discuss recent results from our lab, highlighting use cases spanning a diverse range of applications and complex data sets.
About the speaker
Tanja Käser is a tenure-track assistant professor in computer science at EPFL, heading the Machine Learning for Education Lab. Her research lies at the intersection of machine learning, data mining, and education. She is particularly interested in creating accurate models of human behavior and learning. Prior to joining EPFL, Tanja Käser was a senior data scientist with the Swiss Data Science Center at ETH Zurich and a postdoctoral researcher at the Graduate School of Education at Stanford University. Tanja Käser received her PhD degree from the Computer Science Department of ETH Zurich. In her dissertation, she focused on user modeling and data mining in education, which was honored with the Fritz Kutter Award in 2015.