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2 Annals of Computer Science and Information Systems

2 2020

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Feasibility of computerized adaptive testing evaluated by Monte-Carlo and post-hoc simulations

100%

Štěpánek L. , Martinková P.

tom Vol. 21

359--367

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. In this study, we analyze feasibility and advantages of computerized adaptive testing using a Monte-Carlo simulation and posthoc analysis based on a real linear admission test administrated at a medical college. We compare various settings of the adaptive test in terms of precision of ability estimates and test length. We find out that with adaptive item selection, the test length can be reduced to 40 out of 100 items while keeping the precision of ability estimates within the prescribed range and obtaining ability estimates highly correlated to estimates based on complete linear test (Pearson’s ρ = 0.96). We also demonstrate positive effect of content balancing and item exposure rate control on item composition.

A Two-Stage Monte Carlo Approach for Optimization of Bimetallic Nanostructures

84%

Mikhov R. , Myasnichenko V. , Kirilov L. , Sdobnyakov N. , Matrenin P. , Sokolov D. , Fidanova S.

tom Vol. 21

285--288

In this paper we propose a two-stage lattice Monte Carlo approach for optimization of bimetallic nanoalloys: simulated annealing on a larger lattice, followed by simulated diffusion. Both algorithms are fairly similar in structure, but their combination was found to give significantly better solutions than simulated annealing alone. We also discuss how to tune the parameters of the algorithms so that they work together optimally.