Luis von Ahn and Matt Streeter
Talk: Behind the scenes of Duolingo
With over 100 million users, Duolingo is the most popular education app in the world in Android and iOS. In the first part of this talk, we will describe the motivation for creating Duolingo, its philosophy, and some of the basic techniques used to successfully teach languages and keep users engaged. The second part will focus on the machine learning and natural language processing algorithms we use to model student learning.
George Siemens, Ryan Baker, Dragan Gasevic
George Siemens. George Siemens researches, technology, networks, analytics, and openness in education. Dr. Siemens is the Executive Director of the Learning Innovation and Networked Knowledge Research Lab at University of Texas, Arlington and cross-appointed with the Centre for Distance Education at Athabasca University. He has delivered keynote addresses in more than 35 countries on the influence of technology and media on education, organizations, and society. His work has been profiled in provincial, national, and international newspapers (including NY Times), radio, and television. His research has received numerous awards, including honorary doctorates from Universidad de San Martín de Porres and Fraser Valley University for his pioneering work in learning, technology, and networks.
Dr. Siemens is the founding President of the Society for Learning Analytics Research (http://www.solaresearch.org/). He has advised government agencies Australia, European Union, Canada and United States, as well as numerous international universities, on digital learning and utilizing learning analytics for assessing and evaluating productivity gains in the education sector and improving learner results. In 2008, he pioneered massive open online courses (sometimes referred to as MOOCs). He blogs at http://www.elearnspace.org/blog/ and on Twitter: @gsiemens
Ryan Baker, is Associate Professor of Cognitive Studies at Teachers College, Columbia University, and Program Coordinator of TC's Masters of Learning Analytics. He earned his Ph.D. in Human-Computer Interaction from Carnegie Mellon University. Dr. Baker was previously Assistant Professor of Psychology and the Learning Sciences at Worcester Polytechnic Institute, and served as the first technical director of the Pittsburgh Science of Learning Center DataShop, the largest public repository for data on the interaction between learners and educational software. He is currently serving as the founding president of the International Educational Data Mining Society, though not for much longer, and as associate editor of the Journal of Educational Data Mining.
He has taught two MOOCs, Big Data and Education, and Data, Analytics, and Learning. His research combines educational data mining and quantitative field observation methods to better understand how students respond to educational software, and how these responses impact their learning. He studies these issues within intelligent tutors, simulations, multi-user virtual environments, MOOCs, and educational games.
Dragan Gasevic, is a Professor and Chair in Learning Analytics and Informatics in the Schools of Education and Informatics at the University of Edinburgh. As the President (2015-2017) and a co-founder of the Society for Learning Analytics Research (SoLAR), he has had the pleasure to serve as a founding program co-chair of the International Conference on Learning Analytics & Knowledge (LAK) in 2011 and 2012, founding program co-chair of the Learning Analytics Summer Institute (LASI) in 2013 and 2014, and a founding editor of the Journal of Learning Analytics since 2013.
Computer scientist by formal education, Dragan considers himself a learning scientist whose research centers on learning analytics, self-regulated and social learning, higher education policy, and data mining. The award-winning work of his team on the LOCO-Analytics software is considered one of the pioneering contributions in the growing area of learning analytics. Recently, he has founded ProSolo Technologies Inc that developed a software solution for tracking, evaluating, and recognizing competencies gained through self-directed learning and social interactions. He is a frequent keynote speaker and a (co-)author of numerous research papers and books.
Talk: Personal Knowledge/Learning Graph
Educational data mining and learning analytics have to date largely focused on specific research questions that provide insight into granular interactions. These insights have bee abstracted to include the development of predictive models, intelligent tutors, and adaptive learning. While there are several domains where holistic or systems models have provided additional explanatory power, work around learning has not created holistic models with the level of concreteness or richness required. The need for both granular and integrated high-level view of learning is further influenced by distributed, life long, multi-spaced learning that today defines education. Drawing on social and knowledge graph theory, we propose the development of a Personal Knowledge/Learning Graph (PKLG) - an open and learner-owned profile that addresses cognitive, affective, and related elements that reflect what a learner knows, is able to do, and processes through which she learns best. This talk will introduce PKLG, detail required technical infrastructure, and articulate how it would interact with established learning software.
Pekka Räsänen is a director and a researcher in the Niilo Mäki Institute, which is the largest and the most influential research and development center for learning disorders in Finland. His research activities extend from assessment and longitudinal studies of learning disorders to computer-assisted and other interventions. He has developed the widely used standardized tests of mathematical achievement in Finland and intervention programs to early education and school age.
Talk: Educational neuroscience as a tool to understand learning and learning disabilities in mathematics
Becoming numerate is considered as one of the fundamental skills needed in the modern technology-driven society. The latest OECD (2013) report states that “The way we live and work has changed profoundly – and so has the set of skills we need to participate fully in and benefit from our hyper-connected societies and increasingly knowledge-based economies.“ The societies invest a lot on education with varying results. For some reasons there still are persons do not reach even a basic level of skills in numeracy or literacy irrespective of the recent advances in education, educational research and educational technologies.
Persons who fail in learning numeracy, even though they have had an opportunity to learn and who, based on their other skills, should have learnt, we call as having specific learning disabilities (SLD), developmental dyscalculia (DD). This discrepancy between learning opportunities, general skills and poor performance in mathematics, has intrigued researchers now more than a century. From the early beginning of the research there has been ideas that it has something to do how the brain of these persons have organized, failed to develop or damaged.The recent developments in research methodologies, especially in brain imaging and statistical technologies, have opened new windows to analyze these brain related hypotheses. In my presentation I will open some of these windows with examples from functional brain imaging to longitudinal studies based on multivariate statistical analysis.
The new windows show different views from the DD. From one perspective the DD looks like a unitary construct with very specific symptoms in numerical processing. This view has been more typical within the brain imaging research. The other views show a complex where myriad of factors from genetic to learning experiences each contribute with a small share to the large variation of the individual skills. This view has been more typical in behavioural and cognitive studies, especially in longitudinal research. Whether a common ground can be reached, and what it needed for that, is discussed.