H. Le-modèle and . Genma, qui combine MIRT et une q-matrice, p.83

V. Aleven, B. M. Mclaren, J. Sewall, M. Van-velsen, O. Popescu et al., Example-Tracing Tutors: Intelligent Tutor Development for Nonprogrammers, International Journal of Artificial Intelligence in Education, vol.26, pp.40593-40608, 2016.
DOI : 10.1007/s40593-015-0088-2

C. Altiner, Integrating a computer-based flashcard program into academic vocabulary learning, Iowa State University (cit. on pp, vol.19, issue.111, 2011.

O. Anava, S. Golan, N. Golbandi, Z. Karnin, R. Lempel et al., Budget-Constrained Item Cold-Start Handling in Collaborative Filtering Recommenders via Optimal Design, Proceedings of the 24th International Conference on World Wide Web, WWW '15, pp.45-54, 2015.
DOI : 10.1145/2736277.2741109
URL : http://arxiv.org/abs/1406.2431

R. Baker, P. S. Shaun, and . Inventado, Educational data mining and learning analytics, Learning Analytics, pp.61-75, 2014.
DOI : 10.1017/cbo9781139519526.016

T. Barnes, The q-matrix method: Mining student response data for knowledge, American Association for Artificial Intelligence 2005 Educational Data Mining Workshop, 2005.

D. J. Bartholomew, F. Steele, J. Galbraith, and I. Moustaki, Analysis of multivariate social science data, 2008.

Y. Bergner, K. Colvin, and D. E. Pritchard, Estimation of ability from homework items when there are missing and/or multiple attempts, Proceedings of the Fifth International Conference on Learning Analytics And Knowledge, LAK '15, pp.118-125, 2015.
DOI : 10.1145/2723576.2723582

Y. Bergner, S. Droschler, G. Kortemeyer, S. Rayyan, D. Seaton et al., Model-Based Collaborative Filtering Analysis of Student Response Data: Machine-Learning Item Response Theory, In: International Educational Data Mining Society, vol.29, pp.39-42, 2012.

. Cablé, N. Baptiste, M. Guin, and . Lefevre, « Un outil auteur pour une génération semi-automatique d'exercices d'auto-évaluation, 6e Conférence sur les Environnements Informatiques pour l'Apprentissage Humain, pp.155-111, 2013.

L. Cai, High-dimensional Exploratory Item Factor Analysis by??A??Metropolis???Hastings Robbins???Monro Algorithm, Psychometrika, vol.12, issue.1, pp.33-57, 2010.
DOI : 10.1007/s11336-009-9136-x

R. P. Chalmers, mirtCAT: computerized adaptive testing with multidimensional item response theory. R package version 0, 2015.

R. Chalmers and . Philip, Environment, Journal of Statistical Software, vol.48, issue.6, pp.1-29, 2012.
DOI : 10.18637/jss.v048.i06

H. Chang, Psychometrics Behind Computerized Adaptive Testing, Psychometrika, vol.22, issue.4, pp.1-20, 2014.
DOI : 10.1007/s11336-014-9401-5

M. Chatti, A. L. Amine, U. Dyckhoff, H. Schroeder, and . Thüs, A reference model for learning analytics, International Journal of Technology Enhanced Learning, vol.4, issue.5/6, pp.5-6, 2012.
DOI : 10.1504/IJTEL.2012.051815

S. Chen, A. Choi, and A. Darwiche, Computer Adaptive Testing Using the Same-Decision Probability, 12th Annual Bayesian Modeling Applications Workshop (BMAW) (cit, p.26, 2015.

Y. Cheng, When Cognitive Diagnosis Meets Computerized Adaptive Testing: CD-CAT, Psychometrika, vol.71, issue.4, pp.619-632, 2009.
DOI : 10.1007/s11336-009-9123-2

B. Clement, D. Roy, P. Oudeyer, and M. Lopes, Multi-Armed Bandits for Intelligent Tutoring Systems, JEDM-Journal of Educational Data Mining 7, pp.20-48, 2015.
URL : https://hal.archives-ouvertes.fr/hal-00913669

M. Davier, A general diagnostic model applied to language testing data, ETS Research Report Series 2005.2, p.35, 2005.
DOI : 10.1348/000711007X193957

L. T. Decarlo, On the Analysis of Fraction Subtraction Data: The DINA Model, Classification, Latent Class Sizes, and the Q-Matrix, Applied Psychological Measurement, vol.35, issue.1, 2010.
DOI : 10.1177/0146621610377081

M. C. Desmarais, Conditions for effectively deriving a q-matrix from data with non-negative matrix factorization, 4th International Conference on Educational Data Mining, EDM, pp.41-50, 2011.

M. C. Desmarais, S. J. Ryan, and . Baker, A review of recent advances in learner and skill modeling in intelligent learning environments, User Modeling and User-Adapted Interaction, vol.61, issue.4, pp.1-2, 2012.
DOI : 10.1007/s11257-011-9106-8

J. Doignon and J. Falmagne, Knowledge spaces, p.35, 2012.

J. Dunlosky, K. A. Rawson, E. J. Marsh, M. J. Nathan, and D. T. Willingham, Improving Students??? Learning With Effective Learning Techniques, Psychological Science in the Public Interest, vol.3, issue.8, pp.4-58, 2013.
DOI : 10.1080/09658210244000414

J. Falmagne, E. Cosyn, J. Doignon, and N. Thiéry, The assessment of knowledge, in theory and in practice, IEMC '03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No.03CH37502), pp.61-79, 2006.
DOI : 10.1109/KIMAS.2003.1245109

M. Feldman, S. A. Friedler, J. Moeller, C. Scheidegger, and S. Venkatasubramanian, Certifying and Removing Disparate Impact, Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, pp.259-268, 2015.
DOI : 10.1145/2783258.2783311
URL : http://arxiv.org/abs/1412.3756

R. Ferguson, Learning analytics: drivers, developments and challenges, International Journal of Technology Enhanced Learning, vol.4, issue.5/6, pp.5-6, 2012.
DOI : 10.1504/IJTEL.2012.051816

L. Gautier, rpy2: A simple and efficient access to R from Python, 2008.

S. P. Goggins, W. Xing, X. Chen, B. Chen, and B. Wadholm, Learning Analytics at " Small " Scale: Exploring a Complexity-Grounded Model for Assessment Automation, In: J. UCS, vol.21, issue.1, pp.66-92, 2015.

N. Golbandi, Y. Koren, and R. Lempel, Adaptive bootstrapping of recommender systems using decision trees, Proceedings of the fourth ACM international conference on Web search and data mining, WSDM '11, pp.595-604, 2011.
DOI : 10.1145/1935826.1935910

R. K. Hambleton and H. Swaminathan, Item response theory: Principles and applications, 1985.
DOI : 10.1007/978-94-017-1988-9

K. T. Han, Item Pocket Method to Allow Response Review and Change in Computerized Adaptive Testing, Applied Psychological Measurement, vol.37, issue.4, pp.259-275, 2013.
DOI : 10.1177/0146621612473638

S. C. Hoi, R. Jin, J. Zhu, and M. R. Lyu, Batch mode active learning and its application to medical image classification, Proceedings of the 23rd international conference on Machine learning , ICML '06, pp.417-424, 2006.
DOI : 10.1145/1143844.1143897
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.411.9683

A. Huebner, An Overview of Recent Developments in Cognitive Diagnostic Computer Adaptive Assessments, In: Practical Assessment Research & Evaluation, vol.153, issue.35, p.24, 2010.

M. D. Kickmeier-rust and D. Albert, Competence-Based Knowledge Space Theory, Measuring and Visualizing Learning in the Information-Rich Classroom, p.109, 2015.

K. R. Koedinger, A. Elizabeth, J. C. Mclaughlin, and . Stamper, Automated Student Model Improvement, In: International Educational Data Mining Society, vol.110, issue.111, 2012.

M. Korn, Imagine Discovering that your Teaching Assistant is Really a Robot The Wall Street Journal In: The Wall Street Journal Accédé le 4 octobre 2016. url: http : / / www . wsj . com / articles / if -your -teacher - sounds-like-a-robot-you-might-be-on-to-something-1462546621, 2016.

A. Kulesza and B. Taskar, Determinantal Point Processes for Machine Learning, Foundations and Trends?? in Machine Learning, vol.5, issue.2-3, pp.96-98, 2012.
DOI : 10.1561/2200000044
URL : http://arxiv.org/abs/1207.6083

S. Lallé, « Assistance à la construction et à la comparaison de techniques de diagnostic des connaissances ». Thèses, 2013.

A. S. Lan, C. Studer, A. E. Waters, and R. G. Baraniuk, Tag-aware ordinal sparse factor analysis for learning and content analytics, 2014.

A. S. Lan, E. Andrew, C. Waters, R. G. Studer, and . Baraniuk, Sparse factor analysis for learning and content analytics, The Journal of Machine Learning Research, vol.15, issue.100, pp.1959-2008, 2014.

S. Lee, J. Z. Huang, and J. Hu, Sparse logistic principal components analysis for binary data, The annals of applied statistics 4.3, pp.1579-1601, 2010.
DOI : 10.1214/10-AOAS327SUPP
URL : http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2992445

J. P. Leighton, J. Mark, S. M. Gierl, and . Hunka, The Attribute Hierarchy Method for Cognitive Assessment: A Variation on Tatsuoka's Rule-Space Approach, Journal of Educational Measurement, vol.21, issue.3, pp.205-237, 2004.
DOI : 10.1016/0010-0285(92)90018-W

W. J. Linden, C. A. Van-der, and . Glas, Elements of adaptive testing, 2010.

D. Lynch and C. P. Howlin, Real world usage of an adaptive testing algorithm to uncover latent knowledge (cit, pp.25-27, 2014.

D. Magis, Empirical comparison of scoring rules at early stages of CAT, 2015.

D. Magis and G. Raîche, Random Generation of Response Patterns under Computerized Adaptive Testing with the R Package catR, 2012.

S. Mandin and N. Guin, Basing Learner Modelling on an Ontology of Knowledge and Skills, 2014 IEEE 14th International Conference on Advanced Learning Technologies, pp.321-323, 2014.
DOI : 10.1109/ICALT.2014.98
URL : https://hal.archives-ouvertes.fr/hal-01301064

N. Manouselis, H. Drachsler, R. Vuorikari, H. Hummel, and R. Koper, Recommender systems in technology enhanced learning " . In: Recommender systems handbook, pp.387-415, 2011.

R. J. Mislevy, T. John, K. E. Behrens, R. Dicerbo, and . Levy, Design and discovery in educational assessment: Evidence-centered design, psychometrics, and educational data mining, In: JEDM-Journal of Educational Data Mining, vol.4, issue.115, pp.11-48, 2012.

Z. K. Papamitsiou, V. Terzis, and A. A. Economides, Temporal learning analytics for computer based testing, Proceedins of the Fourth International Conference on Learning Analytics And Knowledge, LAK '14, pp.31-35, 2014.
DOI : 10.1145/2567574.2567609

A. Peña-ayala, Educational data mining: A survey and a data mining-based analysis of recent works " . In: Expert systems with applications 41, pp.1432-1462, 2014.
DOI : 10.1007/978-3-319-02738-8

M. Reckase, Multidimensional item response theory, 2009.
DOI : 10.1007/978-0-387-89976-3

C. Redecker and Ø. Johannessen, Changing Assessment - Towards a New Assessment Paradigm Using ICT, European Journal of Education, vol.27, issue.3, pp.79-96, 2013.
DOI : 10.1111/ejed.12018
URL : http://onlinelibrary.wiley.com/doi/10.1111/ejed.12018/abstract

D. Rizopoulos, ltm: An R Package for Latent Variable Modeling and Item Response Analysis, Journal of Statistical Software, vol.175, 2006.

A. Robitzsch, T. Kiefer, A. C. George, and A. Ünlü, CDM: Cognitive diagnosis modeling, 2014.

A. Rupp, R. Levy, K. E. Dicerbo, S. J. Sweet, A. V. Crawford et al., Putting ECD into practice: The interplay of theory and data in evidence models within a digital learning environment, In: JEDM-Journal of Educational Data Mining, vol.4, issue.114, pp.49-110, 2012.

V. J. Shute, Stealth assessment in computer-based games to support learning, Computer games and instruction 55, pp.503-524, 2011.
DOI : 10.1037/e501582008-001

V. J. Shute, J. P. Leighton, E. E. Jang, and M. Chu, Advances in the Science of Assessment, Educational Assessment, vol.8, issue.6, pp.34-59, 2016.
DOI : 10.1080/08957347.2012.635507

V. J. Shute, M. Ventura, and Y. Kim, Assessment and Learning of Qualitative Physics in Newton's Playground, The Journal of Educational Research, vol.73, issue.6, pp.423-430, 2013.
DOI : 10.1177/1046878108321866

Y. Su, K. M. Choi, W. C. Lee, T. Choi, and M. Mcaninch, Hierarchical cognitive diagnostic analysis for TIMSS, Centre for Advanced Studies in Measurement and Assessment 35, pp.1-71, 2003.

. Thai-nghe, L. Nguyen, T. Drumond, L. Horváth, and . Schmidt-thieme, Multi-relational factorization models for predicting student performance, Proc. of the KDD Workshop on Knowledge Discovery in Educational Data. Citeseer (cit, pp.27-40, 2011.

A. Toscher and M. Jahrer, Collaborative filtering applied to educational data mining, In: KDD cup, 2010.

M. Ueno and P. Songmuang, Computerized Adaptive Testing Based on Decision Tree, 2010 10th IEEE International Conference on Advanced Learning Technologies, pp.191-193, 2010.
DOI : 10.1109/ICALT.2010.58

. Van-den-oord, S. Aaron, B. Dieleman, and . Schrauwen, Deep content-based music recommendation, Advances in Neural Information Processing Systems, pp.2643-2651, 2013.

K. Verbert, H. Drachsler, N. Manouselis, M. Wolpers, R. Vuorikari et al., Dataset-driven research for improving recommender systems for learning, Proceedings of the 1st International Conference on Learning Analytics and Knowledge, LAK '11, pp.44-53, 2011.
DOI : 10.1145/2090116.2090122

N. D. Verhelst, Profile Analysis: A Closer Look at the PISA 2000 Reading Data, Scandinavian Journal of Educational Research, vol.9, issue.33, pp.315-332, 2012.
DOI : 10.1017/CBO9780511499531.004

J. Vie, F. Popineau, Y. Bourda, and É. Bruillard, A review of recent advances in adaptive assessment Learning analytics: Fundaments, applications, and trends: A view of the current state of the art, 2016.

J. Vie, F. Popineau, J. Grill, É. Bruillard, and Y. Bourda, Predicting Performance over Dichotomous Questions: Comparing Models for Large-Scale Adaptive Testing, 8th International Conference on Educational Data Mining, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01275280

L. Vygotsky and . Semenovich, Mind in society: The development of higher psychological processes, 1980.

S. Wang, G. Fellouris, and H. Chang, Sequential Design for Computerized Adaptive Testing that Allows for Response Revision, 2015.
DOI : 10.5705/ss.202015.0304

S. Wang, H. Lin, H. Chang, and J. Douglas, Hybrid Computerized Adaptive Testing: From Group Sequential Design to Fully Sequential Design, Journal of Educational Measurement, vol.39, issue.350, pp.45-62, 2016.
DOI : 10.1111/jedm.12100

T. Winters, C. Shelton, T. Payne, and G. Mei, Topic extraction from item-level grades, American Association for Artificial Intelligence 2005 Workshop on Educational Datamining, pp.3-37, 2005.

X. Xu, H. Chang, and J. Douglas, A simulation study to compare CAT strategies for cognitive diagnosis, annual meeting of the American Educational Research Association, 2003.

D. Yan, A. A. Von-davier, and C. Lewis, Computerized Multistage Testing, pp.55-76, 2014.

A. Zeileis, N. Umlauf, and F. Leisch, Flexible Generation of E-Learning Exams in R: Moodle Quizzes, OLAT Assessments, and Beyond . Department of Economics (Inst. für Wirtschaftstheorie und Wirtschaftsgeschichte ) (cit, p.15, 2012.

K. Zernike, Obama Administration Calls for Limits on Testing in Schools In: The New York Times Accédé le 2 avril 2016, 2015.

H. Zou, T. Hastie, and R. Tibshirani, Sparse Principal Component Analysis, Journal of computational and graphical statistics 15.2, pp.265-286, 2006.
DOI : 10.1198/106186006X113430