Feasibility of Computerized Adaptive Testing Evaluated by Monte-Carlo and Post-hoc Simulations

Authors: L. Štěpánek, Patrícia Martinková
Publication year: 2020
Publication type: Konferenční příspěvek (mezinárodní konf.)
Periodical: Proceedings of the 2020 Federated Conference on Computer Science and Information Systems
Bibliographic citation: ŠTĚPÁNEK, L., MARTINKOVÁ, Patrícia. Feasibility of Computerized Adaptive Testing Evaluated by Monte-Carlo and Post-hoc Simulations. In: GANZHA, M., MACIASZEK, L., PAPRZYCKI, M., eds. Proceedings of the 2020 Federated Conference on Computer Science and Information Systems. Piscataway: IEEE, 2020, S. 359-367. Annals of Computer Science and Information Systems, 21. ISBN 978-83-955416-7-4. ISSN 2300-5963
Dostupné z: doi:10.15439/2020F197.
DOI: 10.15439/2020F197
ID: 533596

Abstract

Computerized adaptive testing (CAT) is a modern alternative to classical paper and pencil testing. CAT is based on an automated selection of optimal item corresponding to current estimate of test-taker's ability, which is in contrast to fixed predefined items assigned in linear test. Advantages of CAT include lowered test anxiety and shortened test length, increased precision of estimates of test-takers' abilities, and lowered level of item exposure thus better security. Challenges are high technical demands on the whole test work-flow and need of large item banks.