We are very excited to announce our engaging and innovative speakers for LAK16!

Learning as a machine. Cross-overs between humans and machines

by Professor Mireille Hildebrandt

Wednesday April 27, 2016, 8:30 AM – 10:00 AM

‘Learning as a machine’ can be ‘read’ in three ways. First, it can refer to the learning process itself, as a kind of machinery, as a mechanistic or deterministic process. I will call this the Pavlov approach and inquire whether Pentland’s and Helbing’s social physics continues this approach in the era of data-driven exploration. Second, it can refer to the learning process of machines, notably to ‘machine learning’ as one of the most promising techniques of artificial intelligence. I will call this the Herbert Simon approach, even though Simon was quite sceptical of machine learning. Third, ‘learning as a machine’ can refer to the fact that human beings increasingly live in a world saturated with data-driven applications that are more or less capable of machine learning. Since this will require human beings to anticipate how their intelligent environment learns, I will argue that – to some extent – humans will engage in ‘learning as if a machine’. In my keynote I will investigate what this could mean in terms of human liberty and human dignity – two key terms in the privacy debate – and explain how it relates to the employment of learning analytics to aid human learning processes. This will include a discussion of legal protection by design in the context of learning analytics, notably providing students with profile transparency while protecting their fundamental right to data protection. For an example of profile transparency in another context, see http://www.usemp-project.eu.

mMireilleMireille Hildebrandt is research professor at the Vrije Universiteit Brussels, with the research group on Law, Science, Technology & Society studies (LSTS). She is also professor of Smart Environments, Data Protection and the Rule of Law at Radboud University Nijmegen, at the Institute of Computing & Information Sciences. Her research is focused on interfacing law and technology, based on the nexus of philosophy of law and technology, mainly targeting the implications of smart technologies for democracy and the Rule of Law (machine learning, cyber-physical infrastructures, cloud robotics, neuroscience). She publishes widely on these subjects. She co-edited Profiling the European Citizen (Springer 2008) together with Serge Gutwirth, and a series of volumes that confront philosophers of law with philosophers of technology (Routledge 2011, 2013, forthcoming). In 2015 her Smart Technologies and the End(s) of Law was published with Edward Elgar. See http://works.bepress.com/mireille_hildebrandt/.

Learning Analytics: Utopia or Dystopia

by Professor Paul A. Kirschner

Thursday April 28, 2016, 8:30 AM – 10:00 AM

Learning Analytics (LA) is being greeted with the same adoration, praise, and/or joy (hosanna!) as we saw with many other information-technological innovations of the past few decades. There is, however, both a positive and a negative difference between LA and earlier innovations. First the good news. LA is probably the first in the long line of innovations and promises based upon them that can possibly achieve many of the educational futures that people are hoping for such as adaptivity, differentiation, tailor-made instruction, and so forth. And now the bad news. While most of the others failed and in their failure were innocuous (if you consider wasting time and money to be innocuous), LA has the potential to also do harm in a number of ways. This keynote will try to put both the possible utopian futures and their dystopian counterparts on the research and praxis agendas.

paul Paul A. Kirschner (1951) is University Distinguished Professor at the Open University of the Netherlands as well as Visiting Professor of Education with a special emphasis on Learning and Interaction in Teacher Education at the University of Oulu, Finland. He was previously professor of Educational Psychology and Programme Director of the Fostering Effective, Efficient and Enjoyable Learning environments (FEEEL) programme at the Welten Institute, Research Centre for Learning, Teaching and Technology at the Open University of the Netherlands. He is an internationally recognised expert in his field. He was President of the International Society for the Learning Sciences (ISLS) in 2010-2011, member of both the ISLS CSCL Board and the Executive Committee of the Society and he is an AERA Research Fellow (the first European to receive this honour). He is currently a member of the Scientific Technical Council of the Foundation for University Computing Facilities (SURF WTR) in the Netherlands and was a member of the Dutch Educational Council and, as such, was advisor to the Minister of Education (2000-2004). He is chief editor of the Journal of Computer Assisted Learning, associate editor of Computers in Human Behavior, and has published two very successful books: Ten Steps to Complex Learning (now in its second revised edition and translated/published in Korea and China) and Urban Legends about Learning and Education. He also co-edited two other books (Visualizing Argumentation and What we know about CSCL). His areas of expertise include interaction in learning, collaboration for learning (computer supported collaborative learning), and regulation of learning.

A Dispatch from the Psychometric Front

by Professor Robert J. Mislevy

Friday April 29, 2016, 8:30 AM – 10:00 AM

Psychometrics has a long history of creating methods to reason from students’ behavior to their proficiencies more broadly conceived and how they might be improved.  This talk discusses concepts from psychometrics I believe hold value for learning analytics.  Some are familiar, but others are quite new to the field itself.  Key insights that have evolved over the years have been (1) viewing the problem as one of  statistical inference,  (2) building models that suited the inferential problem as cast in then-current psychological theory, informed by then-current forms of data, and (3) developing methods to define and operationalize properties such as reliability, validity, comparability, generalizability, and fairness – not just measurement principles, Messick (1994) reminds us, but “social values that have meaning and force outside of measurement wherever evaluative judgments and decisions are made” (p. 13).  Rapid advances in technology and sociocognitive branches of psychology have sparked a renaissance in the field, as psychometricians work across disciplines to disentangle key insights from the particulars of historical models, forms of data, and psychological perspectives.  Current work seeks to synthesize hard-won psychometric understandings with computational data analysis methods; results from the learning sciences, domain-based research, and cognitive and sociocultural psychology; and simulation, game, and immersive environments.  Several of the challenges we are tackling connect with those faced in learning analytics.  I will note connections where I see them, with an anticipation of mutual benefit.

robertRobert Mislevy is the Frederic M. Lord Chair in Measurement and Statistics at ETS, and Emeritus Professor of Measurement and Statistics at the University of Maryland. His research applies developments in technology, statistics, and cognitive science to practical problems in assessment. He developed the ‘plausible values’ methodology for the National Assessment of Educational Progress, worked with Cisco Systems to develop simulation-based assessments of network engineering, and, with Linda Steinberg and Russell Almond, created an “evidence centered” assessment design framework. Dr. Mislevy’s publications include Bayesian networks in educational assessment, Automated scoring of complex tasks in computer-based testing, Psychometric considerations in game-based assessment, and the “Cognitive Psychology” chapter in Educational Measurement (4th Edition). He has received career contributions awards from the American Educational Research Association and the National Council on Measurement in Education (NCME), and three times received NCME’s Award for Technical Contributions to Measurement. He is a past-president of the Psychometric Society and a member of the National Academy of Education.