Prof. Brendan Franagan, Kyoto University, Japan
Title: Educational Data Science: Changes and Challenges in Learning
Abstract:
As education technology enters the age of large-scale digital platforms, the field faces informatics challenges that resonate with broader data science and AI research: how to capture heterogeneous traces of human activity, design interoperable infrastructures, and develop transparent models that scale across domains. In this keynote, I argue for a new research agenda that positions educational data as a frontier testbed for advancing informatics: demanding innovations in interoperability, explainability, and real-world deployment.
I will present recent work on learning analytics infrastructure and standards that enable cross-institutional data sharing while respecting privacy, explainable recommendation systems that allow learners to interrogate model outputs, and shared log architectures that support reproducibility and institutional analytics. These contributions highlight how education can drive technical advances in areas such as human-in-the-loop model design and in the future federated analysis at scale.
Drawing from experience organizing international data challenges and embedding systems in production environments, I will discuss lessons learned in domain adaptation, evaluation beyond benchmark metrics, and balancing algorithmic performance with human interpretability. These challenges parallel those faced across other areas of informatics: ensuring that models are not only accurate, but also trustworthy, explainable, and adaptable to dynamic, real-world contexts.
Bio: Brendan Flanagan is an Associate Professor at the Center for Innovative Research and Education in Data Science, Institute for Liberal Arts and Sciences, and the Data Science Department at the Graduate School of Informatics, Kyoto University. His research interests include Learning Analytics, Educational Data Science, Computer Assisted Language Learning, and the Application of Blockchain in Education. He has also hosted educational data challenges at prominent international conferences for more than 7 years to promote educational data science research. He is currently the Principal Investigator of several government-funded research projects that investigate knowledge and learning process analysis, and explainable predictions by learning systems, and has also part of a Japanese Cabinet Office (NEDO) funded large research project into educational symbiotic AI systems.