Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision

Detalhes bibliográficos
Autor(a) principal: Jaloto, Alexandre
Data de Publicação: 2023
Outros Autores: Primi, Ricardo
Tipo de documento: preprint
Idioma: eng
Título da fonte: SciELO Preprints
Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/6578
Resumo: The Progressive-Restricted method (PR) increases the security (in terms of item exposure) of Computerized Adaptive Testing (CAT). However, little is known about the effect of the acceleration parameter on precision and security. Therefore, we evaluated the PR with different acceleration parameters in CATs with fixed and variable-length. We combined item selection methods (Maximum Fisher Information – MFI – and PR) with stopping criteria (Fixed length, Standard error of 0.30 and Error reduction of 0.015) and simulated CATs for each Enem test. With the error reduction criterion, the precision with MFI was lower. In the other CATs, the precision was similar. Security has increased with larger acceleration parameters. At last, we compared the linear format of the Enem with the 20-item CAT. The latter had greater precision.
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spelling Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precisionEl método progresivo restringido disminuye la exposición de los ítems en una Prueba Adaptativa Computarizada sin comprometer la precisiónO método progressivo restrito reduz a exposição dos itens em um Teste Adaptativo Computadorizado sem comprometer a precisãopsychometricslarge-scale educational assessmentsimulationitem exposure controlcomputerized adaptive testThe Progressive-Restricted method (PR) increases the security (in terms of item exposure) of Computerized Adaptive Testing (CAT). However, little is known about the effect of the acceleration parameter on precision and security. Therefore, we evaluated the PR with different acceleration parameters in CATs with fixed and variable-length. We combined item selection methods (Maximum Fisher Information – MFI – and PR) with stopping criteria (Fixed length, Standard error of 0.30 and Error reduction of 0.015) and simulated CATs for each Enem test. With the error reduction criterion, the precision with MFI was lower. In the other CATs, the precision was similar. Security has increased with larger acceleration parameters. At last, we compared the linear format of the Enem with the 20-item CAT. The latter had greater precision.SciELO PreprintsSciELO PreprintsSciELO Preprints2023-12-11info:eu-repo/semantics/preprintinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://preprints.scielo.org/index.php/scielo/preprint/view/657810.1590/SciELOPreprints.6578enghttps://preprints.scielo.org/index.php/scielo/article/view/6578/14393Copyright (c) 2023 Alexandre Jaloto, Ricardo Primihttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessJaloto, AlexandrePrimi, Ricardoreponame:SciELO Preprintsinstname:Scientific Electronic Library Online (SCIELO)instacron:SCI2023-08-07T22:04:55Zoai:ops.preprints.scielo.org:preprint/6578Servidor de preprintshttps://preprints.scielo.org/index.php/scieloONGhttps://preprints.scielo.org/index.php/scielo/oaiscielo.submission@scielo.orgopendoar:2023-08-07T22:04:55SciELO Preprints - Scientific Electronic Library Online (SCIELO)false
dc.title.none.fl_str_mv Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
El método progresivo restringido disminuye la exposición de los ítems en una Prueba Adaptativa Computarizada sin comprometer la precisión
O método progressivo restrito reduz a exposição dos itens em um Teste Adaptativo Computadorizado sem comprometer a precisão
title Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
spellingShingle Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
Jaloto, Alexandre
psychometrics
large-scale educational assessment
simulation
item exposure control
computerized adaptive test
title_short Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
title_full Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
title_fullStr Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
title_full_unstemmed Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
title_sort Progressive-restricted method decreases item exposure of a Computerized Adaptive Testing without compromising precision
author Jaloto, Alexandre
author_facet Jaloto, Alexandre
Primi, Ricardo
author_role author
author2 Primi, Ricardo
author2_role author
dc.contributor.author.fl_str_mv Jaloto, Alexandre
Primi, Ricardo
dc.subject.por.fl_str_mv psychometrics
large-scale educational assessment
simulation
item exposure control
computerized adaptive test
topic psychometrics
large-scale educational assessment
simulation
item exposure control
computerized adaptive test
description The Progressive-Restricted method (PR) increases the security (in terms of item exposure) of Computerized Adaptive Testing (CAT). However, little is known about the effect of the acceleration parameter on precision and security. Therefore, we evaluated the PR with different acceleration parameters in CATs with fixed and variable-length. We combined item selection methods (Maximum Fisher Information – MFI – and PR) with stopping criteria (Fixed length, Standard error of 0.30 and Error reduction of 0.015) and simulated CATs for each Enem test. With the error reduction criterion, the precision with MFI was lower. In the other CATs, the precision was similar. Security has increased with larger acceleration parameters. At last, we compared the linear format of the Enem with the 20-item CAT. The latter had greater precision.
publishDate 2023
dc.date.none.fl_str_mv 2023-12-11
dc.type.driver.fl_str_mv info:eu-repo/semantics/preprint
info:eu-repo/semantics/publishedVersion
format preprint
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://preprints.scielo.org/index.php/scielo/preprint/view/6578
10.1590/SciELOPreprints.6578
url https://preprints.scielo.org/index.php/scielo/preprint/view/6578
identifier_str_mv 10.1590/SciELOPreprints.6578
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://preprints.scielo.org/index.php/scielo/article/view/6578/14393
dc.rights.driver.fl_str_mv Copyright (c) 2023 Alexandre Jaloto, Ricardo Primi
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2023 Alexandre Jaloto, Ricardo Primi
https://creativecommons.org/licenses/by/4.0
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
publisher.none.fl_str_mv SciELO Preprints
SciELO Preprints
SciELO Preprints
dc.source.none.fl_str_mv reponame:SciELO Preprints
instname:Scientific Electronic Library Online (SCIELO)
instacron:SCI
instname_str Scientific Electronic Library Online (SCIELO)
instacron_str SCI
institution SCI
reponame_str SciELO Preprints
collection SciELO Preprints
repository.name.fl_str_mv SciELO Preprints - Scientific Electronic Library Online (SCIELO)
repository.mail.fl_str_mv scielo.submission@scielo.org
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