CIS – “Get to know your neighbors” Seminar Series
“Machine Learning to Understand and Improve Human Learning“
Prof. Tanja Käser, Tenure Track Assistant Professor, Head of Machine Learning for Education Laboratory ML4ED
Monday, Nov. 7, 2022 3:15 – 4:15pm | Hybrid
or on-site INF 328
Note: As this event is reserved to the EPFL community please register with your @epfl.ch email address.
Over the past 10 years ML/AI has deeply revolutionized fields where data can be easily collected and used to discover complex patterns and relationships that would otherwise escape the human mind. Recent advances in attention-based models, autoencoders, and LSTMs are enabling personalization at scale in applied domains. ML is also transforming research in the learning sciences. It is, however, still scarcely used in educational practice.
In this talk, I will discuss the challenges and the potential of ML for education. I will describe efforts at designing generalizable deep learning models for performance prediction by encoding meta-information. Then, I will discuss work in developing interpretable early prediction models in unstructured environments. Finally, I will also present our efforts on detecting, representing, and interpreting human strategies.
Tanja Käser is an assistant professor at the School of Computer and Communication Sciences (IC) at EPFL. 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. Before that, she was a postdoctoral researcher with the AAALab at the Graduate School of Education of Stanford University. Tanja Käser received her PhD degree from the Computer Science Department of ETH Zurich;her thesis was distinguished with the Fritz Kutter Award for the best Computer Science thesis at a Swiss university.