0468589 20240103213321.820170109235959.9 12 s. E cav_un_epca*0462433Proceedings of the Eighth International Conference on Probabilistic Graphical Models403-414BrooklineMicrotome Publishing2016AntonucciA.CoraniG.Polpo de CamposC. Bayesian networks computerized adaptive testing item response theory generalised linear models cav_un_auth*0329423 Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 50 Plajner Martin UTIA-B CZ K Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0101228 Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Department of Decision Making Theory 50 Vomlel Jiří UTIA-B Ústav teorie informace a automatizace AV ČR, v. v. i. http://library.utia.cas.cz/separaty/2016/MTR/plajner-0468589.pdf cav_un_auth*0332303 GA16-12010S GA ČR CZ cav_un_auth*0340587 SGS16/175/OHK3/2T/14 Studentské grantová soutěž ČVUT CZ This paper provides a common framework, a generic model, for Computerized Adaptive Testing (CAT) for different model types. We present question selection methods for CAT for this generic model. We use three different types of models, Item Response Theory, Bayesian Networks, and Neural Networks, that instantiate the generic model. We illustrate the usefulness of a special model condition – the monotonicity – and discuss its inclusion in these model types. With Bayesian networks we use specific type of learning using generalized linear models to ensure the monotonicity. We conducted simulated CAT tests on empirical data. Behavior of individual models was assessed based on these tests. The best performing model was the BN model constructed by a domain expert; its parameters were learned from data under the monotonicity condition. cav_un_auth*0343879 International Conference on Probabilistic Graphical Models 2016 /8./ 20160906 20160909 Lugano CH JD 2017 2 PR RVO:67985556 http://hdl.handle.net/11104/0269442 S 2016 SCOPUS PUBMED WOS cav_un_epca*0462433 Proceedings of the Eighth International Conference on Probabilistic Graphical Models Microtome Publishing 2016 Brookline 403 414 JMLR: Workshop and Conference Proceedings vol. 52 1938-7228 340 Antonucci A. 340 Corani G. 340 Polpo de Campos C.