Professor Ryan Baker (University of Pennsylvania, USA)

Professor Ryan Baker

Some Challenges for the Next 18 Years of Learning Analytics

After nine years of learning analytics conferences, we have accurate models of constructs many didn’t think we could model, dashboards and interventions and (some) evidence they work, and scaled solutions that are being used to change student outcomes. Learning analytics has been unusually successful in a short time. Let’s pat ourselves on the back. And after that, let’s reflect. We have solved some challenging problems. So, what’s next? Where should we go — and are we actually going there? I have a few thoughts. And a few concerns. In this talk, I’ll discuss a few hard problems that I see looming in the path of an optimally beneficial learning analytics; some of the big goals I think we can strive to achieve; some of the grand challenges we will need to — and I think can — solve; and perhaps most importantly — how we’ll know if we’ve gotten there.

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 Lise Getoor (University of California Santa Cruz, USA)

Professor Lise Getoor

Scalable Collective Reasoning for Richly Structured Socio-Behavioral Data

Learning analytics requires making sense of large, complex, and heterogeneous data. There are often data alignment and integration challenges. Data can be missing or noisy. Many times there is auxiliary domain knowledge, and often there is rich socio-behavioral data as well. In this talk, I will describe some common challenges for dealing with richly structured heterogeneous data. I will provide an introduction to probabilistic soft logic (PSL), an open-source toolkit being developed in my group that is well suited to the data integration, domain understanding, and performance analysis common in learning analytics. I will ground the presentation with several learning domain examples, including analysis of engagement and learning in online college and high-school MOOCs.

Lise Getoor is a professor in the Computer Science Department and director of the Data, Discovery and Decisions Data Science Research Center at the University of California, Santa Cruz. Her research areas include machine learning, data integration and reasoning under uncertainty, with an emphasis on graph and network data. She has over 200 publications and extensive experience with machine learning and probabilistic modeling methods for graph and network data. She is a Fellow of the Association for Artificial Intelligence, has served as an elected board member of the International Machine Learning Society, the board of the Computing Research Association (CRA), and was co-chair for ICML 2011. She is the recipient of an NSF Career Award and twelve best paper and best student paper awards. 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.

Professor Shirley Alexander (University of Technology Sydney, Australia)

Professor Shirley Alexander

The Data-Intensive University: A Field Guide

The former CEO of Hewlett Packard Lew Platt is reported to have once said “if only Hewlett Packard knew what Hewlett Packard knows, we would be three times more productive.” The same could well be said of many universities. Managing a large contemporary university in the 21st Century is a challenging endeavour requiring ever increasingly complex decision making. Yet the ways in which these decisions are made are often devoid of the standard and well used research processes of drawing upon what is already known (published research in the area), as well as collecting and analysing the data the institution already has. This talk will be in two parts: the first will outline the ways in which a large urban university in Australia is achieving its goal of becoming a “data intensive university” by using existing research and data to make data-driven decisions. The second part will look at the role that data and analytics might play in meeting some of the emerging challenges facing the higher education sector over the next decade.

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. 

Important Dates

All deadlines areĀ 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
Camera-ready papers for Companion Proceedings 4 February 2019
LAK19, Tempe, Arizona 4-8 March 2019