Keynote Speakers

Professor Shane Dawson
– University of South Australia, Australia –

Shane Dawson is the Director of the Teaching Innovation Unit, Co-Director of the Centre for Change and Complexity in Learning (C3L) and Professor of Learning Analytics at the University of South Australia.

As a founding executive member of the Society for Learning Analytics Research, past program and conference chair of the International Learning Analytics and Knowledge conference and an inaugural co-editor of the Journal for Learning Analytics, Shane has been supporting the development of Learning Analytics over the past decade and more.

He has published widely on topics from creative capacity to social network analysis and the application of learner ICT interaction data to inform and benchmark teaching and learning quality. His current research interests relate to complex systems and academic leadership to aid adoption and application of learning analytics at scale.

With the support of many talented colleagues, Shane has been involved in the development of numerous open source software including the Online Video Annotations for Learning (OVAL), OnTask (a personalised learner feedback tool), and SNAPP, a social network visualization tool designed for teaching staff to better understand, identify and evaluate student learning, engagement, academic performance and creative capacity.

Talk: Learning Analytics – A field on the verge of relevance?

A decade ago the first learning analytics and knowledge conference brought together a range of disciplinary experts to explore how the science of learning could be advanced through analytics. From this initial small gathering, the field of learning analytics has risen in prominence and, by any measure, can be considered a resounding success.

This talk evaluates the key accomplishments of the field to date: what kinds of questions have researchers answered? What has been the impact? After reviewing the legacy of LA over the past decade, this talk will turn its focus to current and future states of the field. How society and researchers view data has changed dramatically over the past decade as concerns of equity, privacy, ethics, machine learning and AI have become public conversations.

The long term impact of LA will depend on how the field engages with dissenting voices while retaining a scientific view of learning and knowledge processes. As LA-based research, funding options, new degree programs, and commercialization of LA technologies develop, implications will continue to move from lab environments into classrooms and into society.

There is a clear imperative to examine the longer term relevance of the field. What is our role as researchers in this transition? What roles should leadership, including SoLAR executive, play in anticipating and responding to public misinformation and misunderstanding regarding what is possible and what is ethical with educational data?

This talk will conclude by addressing the pressures confronting education and how the LA field can best respond.

 

Professor Milena Tsvetkova

– London School of Economics and Political Science, The United Kingdom –

Milena Tsvetkova is Assistant Professor at the Department of Methodology at the London School of Economics and Political Science.

She is a sociologist by training and her research interests reside in the field of computational social science. In her work, Milena uses large-scale web-based social interaction experiments, network analysis of online data, and agent-based modeling to investigate fundamental social phenomena such as cooperation, social contagion, segregation, and inequality.

Milena’s research on online collaboration and crowdsourcing platforms such as Wikipedia and Topcoder has direct relevance to online learning communities. Currently, she is  collaborating with computer scientists, physicists, organizational scientists, and game developers to incorporate gamification and citizen science into student learning.

Talk: Group Learning Analytics

Much of learning analytics, and machine learning in the social sciences in general, focuses on the individual as a fundamental unit of analysis.
Yet, both in traditional classroom settings and online, learning is embedded in social networks, groups, and cohorts. It is by now a well-established fact that group effects matter: the size and composition of the group, the structure of the communication and collaboration network, and collective or other-based incentives affect individual perceptions, behavior, and outcomes.
Moreover, group outcomes matter too: for example, the dispersion of engagement and learning in the course or cohort is just as important as the individual average. However, analyzing groups involves a new set of methodological challenges related to gathering data, reducing data heterogeneity, and addressing the non-independence of observations.
In this talk, I will use examples from my own work to demonstrate that group-level analysis can provide new valuable insights about learning and to propose some ways to move forward regarding data collection and analytics. 

Professor Allyson Hadwin

– The University of Victoria, Canada –

Dr. Allyson Hadwin is a Full Professor in the Department of Educational Psychology and Leadership Studies at the University of Victoria and co-director of CFI/BCKDF-funded Technology Integration and Evaluation (TIE) Research Lab. Her work focuses on: (a) theoretical modelling of self-regulation and regulation in collaborative learning contexts, and (b) examining instructional tools and technologies for promoting metacognitive awareness and adaptive regulation. Her research team draw from multiple methodologies to explore the dynamic and social nature of regulated learning as it evolves over time and through interaction with others. They are particularly interested in learning analytics that promote student success by providing information for learners about their own adaptive or maladaptive regulation. 

Talk: Smart Learners versus Smart Systems: Leveraging Learning Analytics for Self-regulated learning

Self-regulated learning is not new to the learning analytics community, however the potential of learning analytics for optimizing self-regulated learning has been largely uncharted.
Learning analytics have been used to trace and model collaborative learning, motivation, engagement, metacognition, self-regulated learning, and more recently affect and emotions in learning.
There are many examples of success in terms of advancing predictions of students at risk and implementing adaptive learning systems (Baker, 2019 LAK keynote).
However, the potential for learning analytics to promote the kind of metacognitive awareness and control that are hallmarks of self-regulated learning and academic success has been largely ignored.
In this talk, I will explore (a) key challenges for promoting the development of self-regulated learning skills and competencies that are essential for academic success and lifelong learning, and (b) possibilities for a new decade of learning analytics fuelling metacognitive awareness and control needed for lifelong learning.