Tice 2014
La conférence des Technologies de l'Information et de la Communication pour l'Enseignement

Keynote Speakers

Conférenciers invités : Keynote Speakers

Mardi 18 novembre 2014

Jack MOSTOW


Jack MOSTOWJack MOSTOW, Director

Project LISTEN (www.cs.cmu.edu/~listen)

School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA

Jack Mostow is a Research Professor at Carnegie Mellon University in Robotics, Machine Learning, Language Technologies, and Human-Computer Interaction, and serves on the Steering Committee for CMU's doctoral Program in Interdisciplinary Educational Research (www.cmu.edu/pier). In 1992 he founded Project LISTEN (www.cs.cmu.edu/~listen) to develop an automated Reading Tutor that listens to children read aloud. Project LISTEN won the Outstanding Paper Award at the Twelfth National Conference on Artificial Intelligence in August 1994, a United States patent in 1998, inclusion in the National Science Foundation's "Nifty Fifty" research projects in 2000, and the Allen Newell Medal of Research Excellence in 2003.

After earning his A.B. cum laude in Applied Mathematics at Harvard and his Ph.D. in Computer Science at Carnegie Mellon, Dr. Mostow held faculty positions at Stanford, University of Southern California's Information Sciences Institute, and Rutgers. He has served as an Editor of Machine Learning Journal and of IEEE Transactions on Software Engineering, as Program Co-chair of the 1998 National Conference on Artificial Intelligence, and as Conference Chair of the 2010 International Conference on Intelligent Tutoring System and the 2013 International Conference on Artificial Intelligence in Education. He has given invited talks at such diverse venues as the Association for Computational Linguistics, the National Science Foundation Workshop on Optimal Teaching, and the International Symposium on Automated Detection of Errors in Pronunciation Training. He is a Voting Member of the Society for the Scientific Study of Reading, at whose annual meetings he regularly presents his research. In 2010 he was elected President of the International Artificial Intelligence in Education Society.  

Keynote : What Can We Learn from a Reading Tutor that Listens?

 Project LISTEN’s Reading Tutor listens to children read aloud, and helps them learn to read. It displays text on a computer screen, uses automatic speech recognition to help analyze a child’s oral reading, and responds with spoken and graphical assistance modeled after expert reading teachers but adapted to the limitations and affordances of the technology. The Reading Tutor logs its interactions in detail to a database that we mine in order to assess students’ performance, model their learning, and harvest within-subject experiments embedded in the Reading Tutor to compare alternative tutorial actions. This talk will illustrate a few of the Reading Tutor’s tutorial interactions, student models, and experiments.

 


Mercredi 19 novembre 2014

Gilles DOWEK

Gilles DOWEK (INRIA) Gilles DOWEK :

Je suis chercheur à Inria dans l'équipe Deducteam et dans le Mooc Lab.

Je m'intéresse à la formalisation des mathématiques (la théorie des types, la théorie des ensembles, les logiques nominales, etc.), aux systèmes de traitement des démonstrations (vérification de démonstration, démonstration automatique, etc.), à la conception de langages de programmation pour le calcul quantique et à la sûreté des systèmes aéronautiques et spatiaux

Keynote : Retour d'expérience sur les MOOC (titre provisoire)

 

 

 

 


Jeudi 20 novembre 2014

Dr Manuel LOPES

Manuel LOPES (INRIA)

Manuel LOPES :

I am a researcher with the Flowers' team at INRIA. I am interested in the use of machine learning, control theory to create robots that are more robust and can interact and work with people. My research interests are on learning by imitation, artificial development and active learning approaches.. 

 Keynote : Efficient Online Optimization for Intelligent Tutoring Systems

We present an approach to Intelligent Tutoring Systems which adaptively personalizes sequences of learning activities to maximize skills acquired by each student, taking into account limited time and motivational resources. At a given point in time, the system proposes to the student the activity which makes him progress best. We introduce two algorithms that rely on the empirical estimation of the learning progress, one that uses information about the difficulty of each exercise RiARiT and another that does not use any knowledge about the problem ZPDES. We show how this system is currently being used in several users studies in Gironde. https://flowers.inria.fr/research/kidlearn/