Keynote speakers

Dr. Alyssa Wise is Professor of Technology & Education and Director of LIVE, Vanderbilt’s Learning Innovation Incubator. Dr. Wise’s research combines the tools of data science, the theory of learning sciences and the practices of human-centered design to build and study the use learning analytics and artificial intelligence systems that promote equitable and effective education. Her recent efforts focus on developing alternatives to traditional dashboards that support data-informed decision-making and innovating novel asset-based generative AI applications. Dr. Wise holds a Ph.D. in Learning Sciences and M.S. in Instructional Systems Technology from Indiana University, as well as a B.S. in Chemistry from Yale University, and has previously worked with a wide range of non-profit organizations, educational technology start-ups, and classrooms across the Americas.

To make a positive difference in education we must consider how learning analytics and other data-powered technologies are not only technically robust but also the ways they integrate into and transform existing teaching and learning practices. Taking a socio-technical systems perspective I first examine what it means for a learning analytics tool to be actionable and how we might incorporate richer attention to this construct into our design processes and studies of use. I then take a broader lens to examine how we might shift our thinking around what it means to develop learning analytics with equity mind, offering three critical reorientations: an emphasis on strengths and growth as well as gaps to be overcome; a repositioning of tools in service to learners’ agency and solidarity; and the incorporation of a praxis of care. Central to both parts of the vision is an expansive role for participatory design practices that ensure that learning analytics initiatives are developed with and in response to the needs of the individuals and communities they are intended to support.

Mohammed Saqr is an Academy of Finland researcher who leads the lab of learning analytics at University of Eastern Finland, School of Computing which was, according to Scopus, Europe’s most productive learning analytics lab during the last five years (2019-2023). Mohammed had a PhD in learning analytics from Stockholm University, Sweden, before joining UEF in Finland, Mohammed had a postdoc at University of Paris, France, and holds the title of Docent in learning analytics from the University of Oulu, Finland. Mohammed’s research is interdisciplinary including learning analytics, AI, big data, network science, science of science and medicine. Mohammed has several awards, e.g., the PhD was awarded the best thesis, he also got several international research awards (e.g., best papers), obtained the University of Michigan Office of Academic Innovation fellowship. In 2023, the Society of Learning Analytics Research (SOLAR) awarded Mohammed Europe Emerging Scholar Award for the “noteworthy research leading to significant knowledge and understanding of learning analytics and the impact of research on learning analytics application, adoption, and professional development in Europe. Mohammed obtained funding from prestigious institutions: Academy of Finland (as PI) for Idiographic learning analytic and Swedish Research Council (as Co-PI) as well as several other grants. Mohammed is on the editorial board of several prestigious academic journals e.g., Transactions of Learning Technologies, British journal of Education Technologies and Plos One. Mohammed also organised and contributed to several international conferences and presented several invited keynotes. Mohammed’s current collaboration network includes Finland, Spain, Sweden, Germany, Serbia,Australia, France, Switzerland, UK, and USA and The Netherlands.

Current implementations of AI use aggregate data from many others to derive generalizable laws, and recommendations. The training data of these models reflects the average, the WEIRD or simply, the available. In doing so, it amplifies all the imperfections and shortcomings of our available world. Furthermore, such models paint the average as a “norm” and deviations as noise rather than natural manifestations of individual differences. Given that we are all different, have our peculiarities and unique characteristics, the average is rare and possibly non-existent. Therefore, recommendations using such models are likely to be useless for many, harmful for some and maybe –if at all– useful for some.

This keynote will explain why current AI implementations may be useful for some purposes, help accelerate some tasks or improve others but won’t solve our most pressing problems: how to personalize education, offer support when it is needed and how it should be. On the contrary, precision AI created using individualized models holds the promise for moving us forward. The keynote will demonstrate several examples of precision AI.

Professor Dr. Barbara Wasson is Director of the Centre for The Science of Learning and Technology (SLATE), the Norwegian national centre for learning analytics. She was one of the founders of Kaleidoscope, a European Network of Excellence on Technology Enhanced Learning. Wasson is/has been PI for numerous national and international projects, including the Erasmus+ project DALI: Data Literacy for Citizens. She was a member of the Norwegian Ministry of Education’s Expert commission on Learning Analytics, and is currently a member the Council of Europe Expert Group on AI in Education and the sub-group on Policy in Higher Education. She is also Norway’s representative to the European School Net’s Data Interest Group. Her research interests include learning analytics, AI and education, learning games, teacher inquiry, and data literacy.

Research in the field of Learning Analytics and Knowledge has thrived over the past 12 years, yet its impact on education remains limited. Why is it so hard to implement learning analytics in education? This talk shares the efforts made by the Norwegian Ministry of Education’s expert commission to address the technological, pedagogical, ethical, and legal aspects of the use of learning analytics and adaptive systems in the Norwegian educational sector. The commission’s findings include four dilemmas associated with the use of these systems, as well as recommendations for the government regarding the use of pupil and student data for learning analytics and adaptive learning systems. Drawing upon my own research projects and my personal experience as part of the commission, this talk provides valuable insights into the complex challenges surrounding learning analytics implementation in education.