0506836 20240111141021.620190725235959.9 000418391500019 12 s. P cav_un_epca*0480157978-80-7464-932-5164-175OstravaUniversity of Ostrava2017NovákV.InuiguchiM.ŠtěpničkaM. Computerized Adaptive Testing Question Selection Methods Bayesian Networks cav_un_auth*0329423 Department of Decision Making Theory Plajner Martin UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR CZ Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0377505 Magauina A. KZ cav_un_auth*0101228 Department of Decision Making Theory Vomlel Jiří UTIA-B Matematická teorie rozhodování Department of Decision Making Theory MTR MTR Ústav teorie informace a automatizace AV ČR, v. v. i. soubor PDF http://library.utia.cas.cz/separaty/2019/MTR/plajner-0506836.pdf 8 MB cav_un_auth*0332303 GA16-12010S GA ČR CZ cav_un_auth*0361640 SGS17/198/OHK4/3T/14 GA ČTU CZ The performance of Computerized Adaptive Testing systems, which are used for testing of human knowledge, relies heavily on methods selecting correct questions for tested students. In this article we propose three different methods selecting questions with Bayesian networks as students’ models. We present the motivation to use these methods and their mathematical description. Two empirical datasets, paper tests of specific topics in mathematics and Czech language for foreigners, were collected for the purpose of methods’ testing. All three methods were tested using simulated testing procedure and results are compared for individual methods. The comparison is done also with the sequential selection of questions to provide a relation to the classical way of testing. The proposed methods are behaving much better than the sequential selection which verifies the need to use a better selection method. Individually, our methods behave differently, i.e., select different questions but the success rate of model’s predictions is very similar for all of them. This motivates further research in this topic to find an ordering between methods and to find the best method which would provide the best possible selections in computerized adaptive tests. cav_un_auth*0377506 The 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty 20170917 20170920 Pardubice CZ JD 20000 20200 20204 2020 3 PR RVO:67985556 http://hdl.handle.net/11104/0297993 S 3+4 Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied 3+4 Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied 3+4 Proceedings Paper Computer Science Artificial Intelligence|Mathematics Applied 2017 SCOPUS PUBMED 000418391500019 WOS soubor PDF 8 MB cav_un_epca*0480157 Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty University of Ostrava 2017 Ostrava 164 175 978-80-7464-932-5 340 Novák V. 340 Inuiguchi M. 340 Štěpnička M.