latest News

Jun 1, 2015
Category: General
Posted by: felisa
End of late on-line registration June 18, 2015
Jun 18, 2014
Category: General
Posted by: emmanuelle
The AIED conference in 2015 in Madrid will be at the UNED Computer Science School

Invited Speakers

Pierre Dillenbourg

Pierre Dillenbourg

Brief biography

Professor of learning technologies at Swiss Federal Institute of Technology in Lausanne (EPFL) and academic director of the Center for Digital Education. Former teacher in elementary school, Pierre graduated in educational science (University of Mons, Belgium).

He started to conduct research in learning technologies in 1984. He obtained a PhD in computer science from the University of Lancaster (UK), in the field of educational applications of artificial intelligence. He is past president of the International Society for the Learning Sciences.

His work covers various domain of computer-supported collaborative learning (CSCL), ranging from novel interfaces for face-to-face collaboration (tangibles, paper computing) to educational robotics and dual eye tracking. His lab conducts projects in elementary schools, vocational education and university teaching. The Center for Digital Education is both the EPFL MOOC factory, having obtained 650’000 registrations in two years of activity, and a team of scientists doing research on MOOCs. Pierre is also the director of the Swiss Leading House for Technologies in Vocational Education.

He recently authored a book entitled ‘Orchestration graphs’ (EPFL Press / Routledge). The book proposes a modelling language for bringing at large scale rich pedagogical scenarios that are usually restricted to small classes. The formal model is also intended to enhance learning analytics.

Talk: Orchestration Graphs: How to scale up rich pedagogical scenarios?

The goal of orchestration graphs is to describe how rich learning activities, often designed for small classes, can be scaled up to thousands of participants, as in MOOCs. A sequence of learning activities is modeled as a graph with specific properties. The vertices or nodes of the graph are the learning activities. Learners perform some of these activities individually, some in teams and other ones with the whole class. The graph has a geometric nature, time being represented horizontally and the social organization (individual, teams, class) vertically. These activities can be inspired by heterogeneous learning theories: a graph models the integration of heterogeneous activities into a coherent pedagogical scenario. The edges of the graph connect activities. They represent the two-fold relationship between activities: how they relate to each other from a pedagogical and from an operational viewpoint. From the operational viewpoint, edges are associated to operators that transform the data structures produced during a learning activity into the data structures needed to run the next activity.

From the pedagogical viewpoint, an edge describes why an activity is necessary for the next activity: it can for instance be a cognitive pre-requisite, a motivational trick, an advanced organizer or an organizational constraint. The extent to which an activity is necessary for the next one is encompassed in the weight of an edge. The transition between two activities is stored as a matrix: the cell (m,n) of a transition matrix stores the probability that a learner in cognitive state m will evolve to state n in the next activity.. The transition matrix can be summarized by a parameter that constitutes the edge weight: an edge between two activities has a heavy weight if the learner performance in an activity is very predictive of his success of the connected activity. The graph also constitutes a probabilistic network that allows predicting the future state of a learner. An orchestation graph describes how the scenario can be modified, stretched, cut, extended.

Prof. Pierre DillenbourgCHILI Lab. EPFL. Center for Digital Education. Swiss Federal Institute of Technology, Lausanne.

Bror Saxberg

Bror Saxber

Brief biography

Bror Saxberg is responsible for the research and development of innovative learning strategies, technologies and products across Kaplan's full range of educational services offerings. He also oversees future developments and adoptions of innovative learning technologies and maintains consistent academic standards for Kaplan's products and courses.

Saxberg most recently served as Senior Vice President and Chief Learning Officer at K12, Inc., where he was responsible for designing both online and off-line learning environments and developing new student products and services. Prior to joining K12, Inc., he was Vice President at Knowledge Universe, where he co-founded the testing and assessment division that became known as Knowledge Testing Enterprise (KTE). Saxberg began his career at McKinsey & Company, Inc. and later served as Vice President and General Manager for London-based DK Multimedia, part of DK Publishing, and education and reference publisher.

Saxberg holds a B.A. in Mathematics and a B.S. in Electrical Engineering from the University of Washington. As a Rhodes Scholar, he received an M.A. in Mathematics from Oxford University. He also received a Ph.D. in Electrical Engineering and Computer Science from M.I.T. and an M.D. from Harvard Medical School.

Talk: Learning engineering: the art and science of improving learning performance.

There's a ton of research out about how learning can be enhanced by the right kinds of learning experiences, including how technology can help.  However, very little of that is getting to students at scale, compared with random walks with technology  (e.g., "Video is great, right? Must have more!").  This talk is about what is being done at Kaplan to try to be "learning engineers," applying learning science at scale in practical circumstances.   Kaplan is trying to work in Pasteur's Quadrant for the domain of learning sciences:  to see what works and doesn't at scale with careful data collection, and to become a test-bed and a source of new questions about lifting student performance in the field.

Bror Saxberg  MD & PhD   Kaplan, Inc.  Chief Learning Officer.  

Vania Dimitrova

Vania Dimitrova

Brief biography

Vania Dimitrova is an Associate Professor at the School of Computing, the University of Leeds, UK. She is a member of the Artificial Intelligence Group where she leads the research activity on user-adaptive intelligent systems, focusing on knowledge-enriched user modelling and adaptation, crowdwisdom, interactive data exploration, knowledge capture and ontological modelling.

She has co-authored more than 100 papers, many of which are presented at key conferences and journals in intelligent learning and user adaptive systems. She chaired the premier international conferences on user modelling (UMAP) and intelligent learning environments (AIED, ECTEL), as well as a series of international workshops on key topics related to user modelling, social systems, and intelligent data exploration.

She is a member of the editorial boards of the personalisation journal (UMUAI) and the International Journal on Artificial Intelligence in Education (IJAIED). She was an associate editor of IEEE Transactions on Learning Technologies (IEEE-TLT) and served on the advisory board for the UK programme in technology-enhanced learning.

She led the Leeds research activities in several interdisciplinary projects, and coordinated the recently completed EU project ImREAL ( which developed intelligent services to link experience in virtual learning environments with experience in the real world.

Talk: Open, Interactive, Social: Intelligent Mentors that Embrace Diversity of Real World Experiences

This talk will draw lessons from a journey in designing and developing intelligent learning environments for adult learners. Early work on interactive open learner modelling showed potential for computer tutors that help learners to understand themselves. While this is necessary for effective adult learning systems, it is not sufficient. Intelligent learning environments have to consider the way adults learn: self-directed, experienced-based, goal- and relevancy oriented.

A key factor is how well the learner connects their learning with the real world, which brings forth the key challenge of modelling real world experiences. Crowdwisdom - digital traces left in social media - can offer a cost-effective, scalable and reusable way to ‘sense’ the real world. I will show how we leverage semantic technologies to gain deeper insights into social content and to model different viewpoints. This paves the way towards intelligent mentoring that harnesses diversity by leveraging the interaction in socio-cyber-physical systems.

Professor Vania Dimitrova. School of Computing. University of Leeds. UK.