B. and J. Roberta, Grundlagen und Experimente. Fraunhofer IRB Verlag (cf, p.14, 2006.

B. , E. Stuart, and R. , RAPID : A reachable anytime planner for imprecisely-sensed domains, p.34, 2012.

B. , S. Nicolò, and C. , Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems, Foundations and Trends® in Stochastic Systems, p.38, 2012.

C. , M. , K. Vanlehn, D. Litman, and P. Jordan, Empirically evaluating the application of reinforcement learning to the induction of effective and adaptive pedagogical strategies, User Modeling and User-Adapted Interaction, vol.211, pp.137-180, 2011.

C. , A. T. Et, and J. R. Anderson, Knowledge tracing : Modeling the acquisition of procedural knowledge " . In : User modeling and user-adapted interaction 4, pp.253-278, 1994.

C. and I. Selega, Optimal experience : Psychological studies of flow in consciousness, p.33, 1992.

D. Frascati, M. P. Okubo, and Y. , Méthode type proposée pour les enquêtes sur la recherche et le développement expérimentalIndicateurs bibliométriques et analyse des systèmes de recherche : méthodes et exemples, Documents de travail de la DSTITrends and transitions in the institutional environment for public and private science, pp.49-50, 1997.

E. , S. Falko, and R. , Flow, performance and moderators of challenge-skill balance, Motivation and Emotion, vol.323, pp.158-172, 2008.

G. , R. M. Leslie, and J. Briggs, Principles of instructional design, p.33, 1974.

G. , A. S. Cristina, C. Kurt, and V. , Procedural help in Andes : Generating hints using a Bayesian network student model, pp.106-117, 1998.

G. José, P. Jack, and M. , Dynamic Cognitive Tracing : Towards Unified Discovery of Student and Cognitive Models, In : EDM, pp.49-56, 2012.

G. , J. Yun, H. Peter, and B. , General features in knowledge tracing : Applications to multiple subskills, temporal item response theory, and expert knowledge, Inter. Conf. on Educational Data Mining (cf, pp.35-59, 2014.

J. Gottlieb, O. Pierre-yves, L. Manuel, and B. Adrien, Information-seeking, curiosity, and attention: computational and neural mechanisms, Trends in Cognitive Sciences, vol.17, issue.11, pp.585-593, 2013.
DOI : 10.1016/j.tics.2013.09.001
URL : https://hal.archives-ouvertes.fr/hal-00913646

H. , M. Jacob, E. Shaaron, and . Ainsworth, Motivating children to learn effectively : Exploring the value of intrinsic integration in educational games, The Journal of the Learning Sciences, vol.20, issue.2, pp.169-206, 2011.

J. , S. , A. Kato, and L. Knipping, In : The Engineers of Tomorrow : Teaching Robotics to Primary School Children, Proceedings of the 36th SEFI Annual Conference (cf, p.13, 2008.

K. Peter and K. O. Rourke, Guide to curriculum design : Enquirybased learning, In : Higher Education Academy, vol.303, p.17, 2004.

K. , K. R. Emma, B. , R. Sjd, B. Elizabeth et al., New Potentials for Data-Driven Intelligent Tutoring System Development and Optimization, pp.34-59, 2013.

K. , K. R. , J. R. Anderson, W. H. Hadley, and M. A. Mark, Intelligent tutoring goes to school in the big city, In : Inter. Journal of Artificial Intelligence in Education (IJAIED), vol.8, issue.34, pp.30-43, 1997.
URL : https://hal.archives-ouvertes.fr/hal-00197383

K. , M. Jacqueline, S. Inanç, and B. , Circos : an information aesthetic for comparative genomics, In : Genome research, vol.199, pp.1639-1645, 2009.

L. , J. I. Et, and E. Brunskill, The Impact on Individualizing Student Models on Necessary Practice Opportunities, Proceedings of the 5th International Conference on Educational Data Mining, pp.118-125, 2012.

L. , J. I. Et, and E. Brunskill, The Impact on Individualizing Student Models on Necessary Practice Opportunities, Inter. Conf. on Educational Data Mining (EDM) (cf, p.35, 2012.

L. and L. Lego, MINDSTORMS® for Schools in Early Years Education, 2005.

L. , M. Pierre-yves, and O. , The Strategic Student Approach for Life-Long Exploration and Learning, IEEE Inter. Conf. on Development and Learning (ICDL'12), p.32, 2012.

R. Luckin, In : Information Technology in Childhood Education Annual, pp.57-85, 2001.

M. , S. , P. Rétornaz, M. Bonani, V. Longchamp et al., ASEBA : A modular architecture for event-based control of complex robots, IEEE/ASME Transactions on Mechatronics 16.2. 13, pp.321-329, 2011.

M. , S. , F. Riedo, M. Bonani, and F. Mondada, A programming workshop using the robot " Thymio II " : The effect on the understanding by children, IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO, issue.11, pp.24-29, 2012.

M. , S. , J. Shin, F. Riedo, R. Siegwart et al., Teaching a Core CS Concept through Robotics, 19th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE), pp.315-320, 2014.

M. and K. Pilot, Activities : LEGO WeDo at Primary School, 3rd International Workshop, Teaching Robotics, Teaching with Robotics, pp.32-39, 2012.

M. , R. , M. Nussbaum, and A. Soto, An autonomous educational mobile robot mediator, Autonomous Robots, vol.254, pp.367-382, 2008.

M. , O. , C. J. Stevens, S. Shahid, A. A. Mahmud et al., A Review of the Applicability of Robots in Education. Technology for Education and, Learning, 2013.

N. , R. Riichiro, M. , and J. Bourdeau, Advances in intelligent tutoring systems, T, vol.308, issue.34, p.31, 2010.

O. , P. Y. Et, and F. Kaplan, What is intrinsic motivation ? a typology of computational approaches, In : Frontiers in Neurorobotics, vol.1, p.40, 2007.

O. , G. , Y. Granader, A. Humphrey, and S. Baron-cohen, LEGO therapy and the social use of language programme : an evaluation of two social skills interventions for children with high functioning autism and Asperger Syndrome, Journal of Autism and Developmental Disorders, vol.38, issue.10, p.14, 2008.

R. , A. , E. Brunskill, T. Griffiths, and P. Shafto, Faster teaching by POMDP planning, In : Artificial Intelligence in Education, pp.280-287, 2011.

R. , F. , M. Chevalier, S. Magnenat, and F. Mondada, Thymio II, a robot that grows wiser with children, IEEE Workshop on Advanced Robotics and its Social Impacts (ARSO). 10, pp.187-193, 2013.

R. , F. , P. Rétornaz, L. Nyffeler, B. et al., A two years informal learning experience using the thymio robot, Advances in Autonomous Mini Robots. 12, pp.37-48, 2012.

C. Schatten, J. Ruth, M. Mavrikis, and L. Schmidt-thieme, Matrix Factorization Feasibility for Sequencing and Adaptive Support in ITS, 7th International Conference on Educational Data Mining EDM 2014, p.35, 2014.

Y. Semet, Y. Yamont, R. Biojout, E. Luton, and P. Collet, Artificial ant colonies and e-learning : An optimisation of pedagogical paths, International Conference on Human-Computer Interaction (cf, p.35, 2003.

S. , J. , R. Siegwart, and S. Magnenat, Visual Programming Language for Thymio II Robot, In : Interaction Design and Children, pp.14-16, 2014.

V. J. Shute, Stealth assessment in computer-based games to support learning " . In : Computer games and instruction 55, pp.503-524, 2011.

V. J. Shute, G. Eric, . Hansen, G. Russell, and . Almond, You Can't Fatten A Hog by Weighing It?Or Can You ? Evaluating an Assessment for Learning System Called ACED, International Journal of Artificial Intelligence in Education, vol.184, pp.289-316, 2008.

W. , Y. Neil, and H. , Extending knowledge tracing to allow partial credit : using continuous versus binary nodes, In : Artificial Intelligence in Education, pp.181-188, 2013.