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dc.contributor.authorLoe, BSen
dc.contributor.authorSun, Luningen
dc.contributor.authorSimonfy, Fen
dc.contributor.authorDoebler, Pen
dc.date.accessioned2018-07-31T13:43:01Z
dc.date.available2018-07-31T13:43:01Z
dc.date.issued2018-04en
dc.identifier.issn2079-3200
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/278582
dc.description.abstractThis study investigates the item properties of a newly developed Automatic Number Series Item Generator (ANSIG). The foundation of the ANSIG is based on five hypothesised cognitive operators. Thirteen item models were developed using the numGen R package and eleven were evaluated in this study. The 16-item ICAR (International Cognitive Ability Resource1) short form ability test was used to evaluate construct validity. The Rasch Model and two Linear Logistic Test Model(s) (LLTM) were employed to estimate and predict the item parameters. Results indicate that a single factor determines the performance on tests composed of items generated by the ANSIG. Under the LLTM approach, all the cognitive operators were significant predictors of item difficulty. Moderate to high correlations were evident between the number series items and the ICAR test scores, with high correlation found for the ICAR Letter-Numeric-Series type items, suggesting adequate nomothetic span. Extended cognitive research is, nevertheless, essential for the automatic generation of an item pool with predictable psychometric properties
dc.publisherInderscience
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.titleEvaluating an automated number series item generator using linear logistic test modelsen
dc.typeArticle
prism.issueIdentifier2en
prism.number20en
prism.publicationDate2018en
prism.publicationNameJournal of Intelligenceen
prism.volume6en
dc.identifier.doi10.17863/CAM.25917
dcterms.dateAccepted2018-03-26en
rioxxterms.versionofrecord10.3390/jintelligence6020020en
rioxxterms.versionVoR*
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2018-04en
dc.contributor.orcidSun, Luning [0000-0002-2470-4278]
dc.identifier.eissn2079-3200
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idESRC (ES/L016591/1)
cam.issuedOnline2018-04-02en
dc.identifier.urlhttp://www.mdpi.com/2079-3200/6/2/20en


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International