RT Journal Article
T1 Merged Tree-CAT: A fast method for building precise computerized adaptive tests based on decision trees
A1 Rodriguez Cuadrado, Javier
A1 Delgado Gómez, David
A1 Laria de la Cruz, Juan Carlos
A1 Rodriguez-Cuadrado, Sara
AB Over the last few years, there has been an increasing interest in the creation of Computerized Adaptive Tests (CATs) based on Decision Trees (DTs). Among the available methods, the Tree-CAT method has been able to demonstrate a mathematical equivalence between both techniques. However, this method has the inconvenience of requiring a high performance cluster while taking a few days to perform its computations. This article presents the Merged Tree-CAT method, which extends the Tree-CAT technique, to create CATs based on DTs in just a few seconds in a personal computer. In order to do so, the Merged Tree-CAT method controls the growth of the tree by merging those branches in which both the distribution and the estimation of the latent level are similar. The performed experiments show that the proposed method obtains estimations of the latent level which are comparable to the obtained by the state-of-the-art techniques, while drastically reducing the computational time.
PB Elsevier
SN 0957-4174
YR 2020
FD 2020-04-01
LK https://hdl.handle.net/10016/32929
UL https://hdl.handle.net/10016/32929
LA eng
NO Numerical experiments were conducted in Uranus, a supercomputer cluster located at Universidad Carlos III de Madrid and jointly funded by EU-FEDER funds and by the Spanish Government via the National Projects nos. UNC313-4E-2361, ENE2009-12213- C03-03, ENE2012-33219, ENE2012-31753 and ENE2015-68265-P. This article was also funded by the Spanish National Project no. RTI2018-101857-B-I00.
DS e-Archivo
RD 15 sept. 2024