Professor David Williamson Shaffer (University of Wisconsin-Madison, USA)
The ability to teach and assess the development of complex thinking skills is crucial for 21st century educational research. In the age of Big Data, we have more information than ever about what students are doing and how they are thinking. However, the sheer volume of data available can overwhelm traditional qualitative and quantitative research methods. This talk looks at the importance of modeling complex thinking by connecting the study of culture with statistical tools to understand learning. A look at how to take a critical step in the field of learning analytics: to go beyond looking for patterns in mountains of data and tell textured stories at scale.
David Williamson Shaffer is the Vilas Distinguished Professor of Learning Sciences at the University of Wisconsin-Madison in the Department of Educational Psychology, the Obel Professor of Learning Analytics at Aalborg University in Copenhagen, and a Data Philosopher at the Wisconsin Center for Education Research. Before coming to the University of Wisconsin, Professor Shaffer taught grades 4-12 in the United States and abroad, including two years working with the US Peace Corps in Nepal. His M.S. and Ph.D. are from the Media Laboratory at the Massachusetts Institute of Technology. Professor Shaffer taught in the Technology and Education Program at the Harvard Graduate School of Education, and was a 2008-2009 European Union Marie Curie Fellow. He studies how to develop and assess complex and collaborative thinking skills, and is the author of How Computer Games Help Children Learn and Quantitative Ethnography.
Professor Cristina Conati (University of British Columbia, Vancouver, Canada)
As digital information continues to accumulate in our lives, information visualizations have become an increasingly relevant tool for discovering trends and shaping stories from this overabundance of data. Education is not an exception, with learner and teacher visualization dashboards being extensively investigated as new means to change pedagogy and learning. Visualizations are typically designed based on the data to be displayed and the tasks to be supported, but they follow a one size-fits-all approach when it comes to users’ individual differences such as expertise, cognitive abilities, states and preferences. There is, however, mounting evidence that these characteristics can significantly influence user experience during information visualization tasks. These findings have triggered research on user-adaptive visualizations, i.e., visualizations that can track and adapt to relevant user characteristics and specific needs. In this talk, I will present results on which user individual differences can impact visualization processing, and on how these differences can be captured using predictive models based on eye-tracking data. I will also discuss how to leverage these models to provide personalized support that can improve the user’s experience with a visualization.
Cristina Conati received a M.Sc. in Computer Science at the University of Milan, as well as a M.Sc. and Ph.D. in Intelligent Systems at the University of Pittsburgh. Conati’s research goal is to integrate research in Artificial Intelligence (AI), Human Computer Interaction (HCI) and Cognitive Science to create intelligent interactive systems that can dynamically adapt to the needs of individual users. Her areas of interest include User-Adaptive Interaction, User Modeling, Educational Data Mining, Intelligent Tutoring Systems. She has over 100 peer-reviewed publications in these fields, and her research has received awards from a variety of venues, including UMUAI, the Journal of User Modeling and User Adapted Interaction (2002), the International Conference on Intelligent User Interfaces ( IUI 2007), the International Conference of User Modeling, Adaptation and Personalization (UMAP 2013, 2014), TiiS, ACM Transactions on Intelligent Interactive Systems (2014), and the International Conference on Intelligent Virtual Agents (IVA 2016). Dr. Conati is an associate editor for UMUAI, TiiS, IEEE Transactions on Affective Computing, and the Journal of Artificial Intelligence in Education. She served as President of AAAC, (Association for the Advancement of Affective Computing), as well as Program or Conference Chair for several international conferences including UMAP, IUI, EDM (Educational Data Mining) and AI in Education.
Professor Neil Selwyn (Monash University, Melbourne)
For many people outside of the LAK community, the idea of ‘learning analytics’ is an uneasy and sometimes controversial proposition. As an outsider himself, Neil Selwyn’s presentation considers some of the key concerns that are being raised as learning analytics becomes more embedded in education settings. These range from familiar debates over data reductionism and algorithmic transparency through to growing unease that learning analytics are implicit in the devaluing and demoralization of education as a human pursuit. In the spirit of constructive (rather than hostile!) criticism, the presentation explores the extent to which these issues offer a basis from which to further improve LAK. For example, to what extent are these concerns simply misunderstandings about what ‘learning analytics’ is – and therefore point to ways in which the LAK community needs to better present itself and its work to a skeptical world? Alternately, which concerns might be worth taking seriously – i.e. to what extent might some of these issues point to current blind spots and unresolved tensions in the field?
Neil Selwyn works in the Faculty of Education, Monash University. His research and teaching focuses on the place of digital media in everyday life, and the sociology of technology (non)use in educational settings. Neil has written extensively on a number of issues, including digital exclusion, education technology policymaking and the student experience of technology-based learning. He has carried out funded research on digital technology, society and education for the Australian Research Council (ARC), Economic and Social Research Council (ESRC), British Academy, the BBC, Nuffield Foundation, the Spencer Foundation, Gates Foundation, Microsoft Partners in Learning, Becta, Australian Government Office of Learning and Teaching (OLT), Australian Communications Consumer Action Network (ACCAN), Save The Children, Centre for Distance Education, the Welsh Office, National Assembly of Wales and various local authorities in the UK. Neil was editor of the journal ‘Learning, Media and Technology‘ (2010-2016), and is a regular keynote speaker at international conferences. Neil is a core member of the ‘Learning with New Media‘ research group within Monash.