F. B. Baker, Item Response Theory: Parameter Estimation Techniques., Biometrics, vol.50, issue.3, 1992.
DOI : 10.2307/2532822

A. Birnbaum, Some latent trait models and their use in infering an examinee's ability, Statistical theories of mental test scores, pp.397-472, 1968.

P. Brusilovsky, J. Eklund, and E. Schwarz, Web-based education for all: a tool for development adaptive courseware, Proceedings of Seventh International World Wide Web Conference, pp.291-300, 1998.
DOI : 10.1016/S0169-7552(98)00082-8

P. Desmarais, Comparison of POKS with Item Response Theory

J. Cheng, R. Greiner, J. Kelly, D. Bell, and W. Liu, Learning Bayesian networks from data: An information-theory based approach, Artificial Intelligence, vol.137, issue.1-2, pp.43-90, 2002.
DOI : 10.1016/S0004-3702(02)00191-1

J. A. Collins, Adaptive testing with granularity, 1996.

J. A. Collins, J. E. Greer, and S. X. Huang, Adaptive assessment using granularity hierarchies and bayesian nets, pp.569-577, 1996.
DOI : 10.1007/3-540-61327-7_156

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.31.5745

C. Conati, A. Gertner, and K. Vanlehn, Using bayesian networks to manage uncertainty in student modeling, User Modeling and User-Adapted Interaction, vol.12, issue.4, pp.371-417, 2002.
DOI : 10.1023/A:1021258506583

R. Conejo, E. Guzman, E. Millán, M. Trella, J. L. Pérez-de-la-cruz et al., SIETTE: A web-based tool for adaptive teaching, International Journal of Artificial Intelligence in Education, vol.14, pp.29-61, 2004.

M. C. Desmarais, A. Maluf, and J. Liu, User-expertise modeling with empirically derived probabilistic implication networks, User Modeling and User-Adapted Interaction, vol.23, issue.3, pp.3-4, 1995.
DOI : 10.1007/BF01126113

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.1488

M. C. Desmarais, P. Meshkinfam, and M. Gagnon, Bayesian modeling with strong vs. weak assumptions in the domain of skills assessment, 2005.

T. J. Eggen, Item Selection in Adaptive Testing with the Sequential Probability Ratio Test, Applied Psychological Measurement, vol.23, issue.3, pp.249-261, 1998.
DOI : 10.1177/01466219922031365

J. Falmagne, M. Koppen, M. Villano, J. Doignon, and L. Johannesen, Introduction to knowledge spaces: How to build, test, and search them., Psychological Review, vol.97, issue.2, pp.201-224, 1990.
DOI : 10.1037/0033-295X.97.2.201

J. Giarratano and G. Riley, Expert systems: Principles and programming, 1998.

J. P. Gonçalves, S. M. Aluisio, L. H. De-oliveira, and O. N. Oliveira, A Learning Environment for English for Academic Purposes Based on Adaptive Tests and Task-Based Systems, Lecture Notes in Computer Science, vol.3220, pp.1-11, 2004.
DOI : 10.1007/978-3-540-30139-4_1

D. Heckerman, A tutorial on learning with bayesian networks, 1995.

A. Jameson, Numerical uncertainty management in user and student modeling: An overview of systems and issues, User Modeling and User-Adapted Interaction, vol.37, issue.3, pp.3-4, 1995.
DOI : 10.1007/BF01126111

F. Jensen, U. B. Kjaerul, M. Lang, and A. L. Madsen, Hugin -the tool for bayesian networks and influence diagrams, Proceedings of the First European Workshop on Probabilistic Graphical Models, pp.2002-211, 2002.

M. Kambouri, M. Koppen, M. Villano, and J. Falmagne, Knowledge assessment: tapping human expertise by the QUERY routine, International Journal of Human-Computer Studies, vol.40, issue.1, pp.119-151, 1994.
DOI : 10.1006/ijhc.1994.1006

C. Lewis and K. Sheehan, Using Bayesian Decision Theory to Design a Computerized Mastery Test, Applied Psychological Measurement, vol.14, issue.4, pp.367-386, 1990.
DOI : 10.1177/014662169001400404

J. Liu and M. Desmarais, A method of learning implication networks from empirical data: Algorithm and Monte-Carlo simulation-based validation, IEEE Transactions on Knowledge and Data Engineering, vol.9, issue.6, pp.990-1004, 1997.

F. M. Lord and . Novick, Statistical theories of mental test scores, 1968.

J. Martin and K. Vanlehn, Student assessment using Bayesian nets, International Journal of Human-Computer Studies, vol.42, issue.6, pp.575-591, 1995.
DOI : 10.1006/ijhc.1995.1025

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.9969

M. Mayo and A. Mitrovic, Optimising ITS behaviour with bayesian networks and decision theory, International Journal of Artificial Intelligence in Education, vol.12, pp.124-153, 2001.

R. P. Mcdonald, Normal-Ogive Multidimensional Model, Handbook of modern item response theory, pp.257-286, 1997.
DOI : 10.1007/978-1-4757-2691-6_15

E. Millán, E. Garcia-herve, A. Rueda, and J. P. Cruz, Adaptation and Generation in a Web-Based Tutor for Linear Programming, Lecture Notes in Computer Science, pp.2722-124, 2003.
DOI : 10.1007/3-540-45068-8_23

E. Millán and J. L. Pérez-de-la-cruz, A bayesian diagnostic algorithm for student modeling and its evaluation, User Modeling and User-Adapted Interaction, vol.12, pp.2-3, 2002.

E. Millán, M. Trella, J. Pérez-de-la-cruz, and R. Conejo, Using Bayesian Networks in Computerized Adaptive Tests, Computers and education in the 21st century, pp.217-228, 2000.
DOI : 10.1007/0-306-47532-4_20

R. Mislevy and H. Chang, Does adaptive testing violate local independence? Psychometrika, pp.149-156, 2000.
DOI : 10.1037/e650592011-001

R. J. Mislevy and D. Gitomer, The role of probability-based inference in an intelligent tutoring system, User Modeling and User-Adapted Interaction, vol.42, issue.5, pp.253-282, 1995.

R. E. Neapolitan, Probabilistic reasoning in expert systems: Theory and algorithms, 1998.

M. D. Reckase, A linear logistic multidimensional model, Handbook of modern item response theory, pp.271-286, 1997.
DOI : 10.1007/978-1-4757-2691-6_16

J. Reye, Student modelling based on belief networks, International Journal of Artificial Intelligence in Education, vol.14, pp.63-96, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00197306

L. M. Rudner, An examination of decision-theory adaptive testing procedures, Proceedings of American Educational Research Association, pp.437-446, 2002.

W. J. Van-der-linden and R. K. Hambleton, Handbook of modern item response theory, 1997.
DOI : 10.1007/978-1-4757-2691-6

K. Vanlehn, C. Lynch, K. Schulze, J. Shapiro, R. Shelby et al., The andes physics tutoring system: Five years of evaluation, pp.678-685, 2005.

K. Vanlehn and J. Martin, Evaluation of an assessment system based on bayesian student modeling, International Journal of Artificial Intelligence in Education, vol.8, pp.179-221, 1997.

K. Vanlehn and Z. Niu, Bayesian student modeling, user interfaces and feedback: A sensitivity analysis, International Journal of Artificial Intelligence in Education, vol.12, pp.154-184, 2001.

K. Vanlehn, Z. Niu, S. Siler, and A. S. Gertner, Student modeling from conversational test data: A bayesian approach without priors, ITS'98: Proceedings of the 4th International Conference on Intelligent Tutoring Systems, pp.434-443, 1998.

W. N. Venables, D. M. Smith, . Development-core, and . Team, An introduction to R, notes on R: A programming environment for data analysis and graphics, 2004.

J. Vomlel, BAYESIAN NETWORKS IN EDUCATIONAL TESTING, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol.12, issue.supp01, pp.83-100, 2004.
DOI : 10.1142/S021848850400259X

H. J. Vos, Applications of Bayesian Decision Theory to Sequential Mastery Testing, Journal of Educational and Behavioral Statistics, vol.24, issue.3, pp.271-292, 1999.
DOI : 10.3102/10769986024003271

J. Zapata-rivera and J. E. Greer, Interacting with bayesian student models, International Journal of Artificial Intelligence in Education, vol.14, issue.2, pp.127-163, 2004.