Publication:
Computerized adaptive test and decision trees: A unifying approach

dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorDelgado Gómez, David
dc.contributor.authorLaria de la Cruz, Juan Carlos
dc.contributor.authorRuiz Hernandez, Diego
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.contributor.funderUniversidad Carlos III de Madrides
dc.date.accessioned2021-06-22T10:55:59Z
dc.date.available2021-06-22T10:55:59Z
dc.date.issued2019-03-01
dc.description.abstractIn the last few years, several articles have proposed decision trees (DTs) as an alternative to computerized adapted tests (CATs). These works have focused on showing the differences between the two methods with the aim of identifying the advantages of each of them and thus determining when it is preferable to use one method or another. In this article, Tree-CAT, a new technique for building CATs is presented. Unlike the existing work, Tree-CAT exploits the similarities between CATs and DTs. This technique allows the creation of CATs that minimise the mean square error in the estimation of the examinee’s ability level, and controls the item’s exposure rate. The decision tree is sequentially built by means of an innovative algorithmic procedure that selects the items associated with each of the tree branches by solving a linear program. In addition, our work presents further advantages over alternative item selection techniques with exposure control, such as instant item selection or simultaneous administration of the test to an unlimited number of participants. These advantages allow accurate on-line CATs to be implemented even when the item selection method is computationally costly.en
dc.description.sponsorshipNumerical 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 No. UNC313-4E-2361, No. ENE2009-12213- C03-03, No. ENE2012-33219, No. ENE2012-31753 and No. ENE2015-68265-P.en
dc.format.extent9
dc.identifier.bibliographicCitationDelgado-Gómez, D., Laria, J. C. & Ruiz-Hernández, D. (2019). Computerized adaptive test and decision trees: A unifying approach. Expert Systems with Applications, 117, pp. 358–366.en
dc.identifier.doihttps://doi.org/10.1016/j.eswa.2018.09.052
dc.identifier.issn0957-4174
dc.identifier.publicationfirstpage358
dc.identifier.publicationlastpage366
dc.identifier.publicationtitleExpert Systems with Applicationsen
dc.identifier.publicationvolume117
dc.identifier.urihttps://hdl.handle.net/10016/32910
dc.identifier.uxxiAR/0000022738
dc.language.isoeng
dc.publisherElsevieren
dc.relation.projectIDGobierno de España. ENE2009-12213-C03-03es
dc.relation.projectIDGobierno de España. ENE2012-33219es
dc.relation.projectIDGobierno de España. ENE2012-31753es
dc.relation.projectIDGobierno de España. UNC313-4E-2361es
dc.relation.projectIDGobierno de España. ENE2015-68265-Pes
dc.rights© 2018 Elsevier Ltd.en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaEstadísticaes
dc.subject.otherDecision treesen
dc.subject.otherLinear programmingen
dc.subject.otherComputerized adaptive testsen
dc.subject.otherItem-exposure controlen
dc.subject.otherR packageen
dc.subject.otherSelectionen
dc.subject.otherQualityen
dc.subject.otherSystemen
dc.subject.otherModelen
dc.subject.otherCATren
dc.titleComputerized adaptive test and decision trees: A unifying approachen
dc.typeresearch article*
dc.type.hasVersionAM*
dspace.entity.typePublication
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