Organizers

Juan D. Pinto is a PhD student at the University of Illinois Urbana‐Champaign. His research involves the development of learner models using machine learning methods and tackling issues of AI interpretability in education. He is currently conducting work as a member of the Human‐centered Educational Data Science (HEDS) Lab and the NSF AI Institute for Inclusive Intelligent Technologies for Education (INVITE).

Luc Paquette is an associate professor in the department of curriculum & instruction at the University of Illinois Urbana-Champaign. His research focuses on the usage of machine learning, data mining and knowledge engineering approaches to analyze and build predictive models of the behavior of students as they interact with digital learning environments such as MOOCs, intelligent tutoring systems, and educational games. He is interested in studying how those behaviors are related to learning outcomes and how predictive models of those behaviors can be used to better support the students’ learning experience.

Vinitra Swamy is a PhD student at EPFL. Her research with the ML4ED lab involves explainable AI for education, especially through the lens of reducing adoption barriers for neural networks. Her recent work focuses on uncovering disagreement in post-hoc explainers, using learning science experts to validate explainer accuracy and actionability, and proposing interpretable-by-design neural network architectures.

Tanja Käser is an assistant professor at the EPFL School of Computer and Communication Sciences (IC) and head of the Machine Learning for Education (ML4ED) laboratory. 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, with a focus on building models that are generalizable, interpretable, and fair.

Qianhui (Sophie) Liu is a PhD student at the University of Illinois Urbana-Champaign. Her research in the HEDS lab focuses on applying data mining methods in combination with learning science theories to help improve the efficiency of teaching and learning in various educational settings. She is interested in closing the loop of machine learning to humans for actionable insights through explainable models and techniques.

Lea Cohausz is a PhD student at the University of Mannheim. Her recent work includes research on how demographic variables influence predictions in EDM and the consequences for fairness (EDM 2023) as well as identifying causal structures in educational data and their relationship to algorithmic bias (LAK 2024). She is interested in advancing our understanding of the complex relationships of factors that influence students’ learning outcomes.

Program committee

• Giora Alexandron, Assistant Professor, Weizmann Institute of Science, Israel
• Ryan Baker, Professor, University of Pennsylvania, USA
• Anthony Botelho, Assistant Professor of Educational Technology, University of Florida, USA
• Nigel Bosch, Assistant Professor of Information Sciences, University of Illinois Urbana-Champaign, USA
• Cristina Conati, Professor of Computer Science, The University of British Columbia, Canada
• Jibril Frej, Postdoctoral Researcher, EPFL, Switzerland
• Ashish Gurung, Postdoctoral Researcher, Carnegie Mellon University, USA
• Paul Hur, PhD Student, University of Illinois Urbana-Champaign, USA
• HaeJin Lee, PhD Student, University of Illinois Urbana-Champaign, USA
• Mirko Marras, Assistant Professor, University of Cagliari, Italy
• Anna Rafferty, Associate Professor of Computer Science, Carleton College, USA
• Yang Shi, PhD Student, North Carolina State University, USA
• Diego Zapata-Rivera, Director of LAFI research center, Educational Testing Service, USA