Organizers

Dr. Laura Brandl, LMU Munich
Dr. Laura Brandl is an Assisstant Professor in the Department of Psychology at Ludwig-Maximilians-Universität (LMU) Munich. Her research focuses on learning analytics, individual differences in learning, and the role of motivation and self-regulation in technology-enhanced education. She investigates how data-driven tools can be informed by learning theory to better account for cognitive and motivational learner variability. At LMU’s Chair of Empirical Educational Research, she has contributed to several interdisciplinary projects connecting educational psychology, data science, and instructional design. Laura is particularly interested in developing ethically grounded, person-centric models of personalized learning analytics.

Prof. Dr. Oleksandra (Sasha) Poquet, Technical University of Munich (TUM)

Prof. Dr. Oleksandra (Sasha) Poquet is Professor of Learning Analytics at the TUM School of Social Sciences and Technology at the Technical University of Munich. Her research focuses on understanding learning as a social and data-rich process, combining computational and theoretical approaches to studying online learning, collaboration, and educational networks. She has contributed influential work on large-scale forum analytics, network structures in learning analytics, and the integration of theory into the measurement and modeling of learning processes. Prof. Poquet’s research bridges education, data science, and the learning sciences, advancing both methodological rigor and practical insight into how analytics can meaningfully support learners and educators. 

Prof. Dr. Matthias Stadler, LMU University Hospital, Munich
Prof. Dr. Matthias Stadler is Professor of Learning Analytics in Medicine and Deputy Director at the Institute for Medical Education (DAM). His research focuses on cognitive, motivational, and metacognitive processes in complex learning and reasoning, especially in digital and simulated environments. He leads multiple national and international research initiatives that combine psychological theory with data-driven learning analytics to improve adaptive support and feedback for learners. As principal investigator in projects funded by the DFG and the Stiftung Innovation in der Hochschullehre, he explores how learning theories can inform the design and personalization of educational technologies. His current work aims to align analytic models with theoretical constructs such as self-regulated learning, goal orientation, and cognitive load.