Keynotes

Professor Ryan Baker (University of Pennsylvania, USA)

Professor Ryan Baker

Ryan Baker is Associate Professor at the University of Pennsylvania, and Director of the Penn Center for Learning Analytics. His lab conducts research on engagement and robust learning within online and blended learning, seeking to find actionable indicators that can be used today but which predict future student outcomes. Baker has developed models that can automatically detect student engagement in over a dozen online learning environments, and has led the development of an observational protocol and app for field observation of student engagement that has been used by over 150 researchers in 4 countries. Predictive analytics models he helped develop have been used to benefit hundreds of thousands of students, over a hundred thousand people have taken MOOCs he ran, and he has coordinated longitudinal studies that spanned over a decade. He was the founding president of the International Educational Data Mining Society, is currently serving as Associate Editor of two journals, was the first technical director of the Pittsburgh Science of Learning Center DataShop, and currently serves as Co-Director of the MOOC Replication Framework (MORF). Baker has co-authored published papers with over 300 colleagues.


Professor Shirley Alexander (University of Technology Sydney, Australia)

Professor Shirley Alexander

Shirley Alexander is Professor of Learning Technologies at the University of Technology, Sydney where she is currently Deputy Vice-Chancellor & Vice President (Education and Students).  She has previously held the positions of Director of the Institute for Interactive Media and Learning, and Dean of the Faculty of Education. She is responsible for leading the achievement of the University’s key priorities in teaching and learning, the student experience and the use of data analytics in all aspects of the university’s work.  Her responsibilities also include increasing the opportunities for student and staff learning, and the development of a strong student culture across the University. Shirley’s long term research agenda has been on the effective use of information and communication technologies in learning in both the tertiary and schools sectors.  She was a member of two successive national government committees on teaching and learning in higher education from 1997 to 2004. The University of Technology Sydney is engaged in a major campus redevelopment project which will involves $1billion in expenditure and Shirley has led the teams designing the teaching and learning, and student space projects. She aims to drive changes to the student experience of university through the design of spaces. She has initiated and led the “Data Intensive University” project, a university-wide initiative to ensure the university makes best use of data in the full range of its activities. 


Professor Lise Getoor (University of California Santa Cruz, USA)

Professor Lise Getoor

Lise Getoor is a Professor in the Computer Science Department at UC Santa Cruz and founding Director of the UC Santa Cruz Data Science Research Center. Her research areas include machine learning and reasoning under uncertainty, with a focus on graph and network data. In addition, she works in data management, data integration, and visual analytics. She has over 250 publications, including 12 best paper awards. She is a Fellow of the Association for Artificial Intelligence, an elected board member of the International Machine Learning Society, has served on the board of the Computing Research Association (CRA) and AAAI Council, and has served as Machine Learning Journal Action Editor, Associate Editor for the ACM Transactions of Knowledge Discovery from Data, and JAIR Associate Editor. She was co-chair for ICML 2011, and has served on the senior PC of conferences including AAAI, ICML, ICWSM, KDD, NIPS, SIGMOD, UAI, VLDB, WSDM and WWW. She received her PhD from Stanford University in 2001, her MS from UC Berkeley, and her BS from UC Santa Barbara, and was a Professor in the Computer Science Department at the University of Maryland, College Park from 2001-2013. 

Important Dates

All deadlines are 23:59 GMT-11

Submission deadline for main track categories (Research, Practitioners, Workshops, Tutorials and Doctoral Consortium) 1 October 2018
Notification of acceptance for Workshops and Tutorials 15 October 2018
Accepted Workshop Open for Submission 29 October 2018
Notification of acceptance for Research, Practitioners, Doctoral Consortium 19 November 2018
Submission deadline for Posters/Demos and Workshop Papers 3 December 2018
Camera-ready papers for ACM Proceedings: Full Research Papers and Short Research Papers 17 December 2018
Notification of Acceptance for Posters/Demos and Workshop Papers 4 January 2019
Early-bird registration closes 8 January 2019
LAK19, Tempe, Arizona 4-8 March 2019