Question Selection Methods for Adaptive Testing with Bayesian Networks

0506836 - ÚTIA 2020 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Plajner, Martin - Magauina, A. - Vomlel, Jiří
Question Selection Methods for Adaptive Testing with Bayesian Networks.
Proceedings of the 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty. Ostrava: University of Ostrava, 2017 - (Novák, V.; Inuiguchi, M.; Štěpnička, M.), s. 164-175. ISBN 978-80-7464-932-5.
[The 20th Czech-Japan Seminar on Data Analysis and Decision Making under Uncertainty. Pardubice (CZ), 17.09.2017-20.09.2017]
Grant CEP: GA ČR(CZ) GA16-12010S
Grant ostatní: GA ČTU(CZ) SGS17/198/OHK4/3T/14
Institucionální podpora: RVO:67985556
Klíčová slova: Computerized Adaptive Testing * Question Selection Methods * Bayesian Networks
Obor OECD: Robotics and automatic control
http://library.utia.cas.cz/separaty/2019/MTR/plajner-0506836.pdf

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.
Trvalý link: http://hdl.handle.net/11104/0297993