Computerized adaptive testing item selection in computerized adaptive learning systems

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Abstract

Item selection methods traditionally developed for computerized adaptive testing (CAT) are explored for their usefulness in item-based computerized adaptive learning (CAL) systems. While in CAT Fisher information-based selection is optimal, for recovering learning populations in CAL systems item selection based on Kullback-Leibner information is an alternative
Original languageEnglish
Title of host publicationPsychometrics in practice at RCEC
EditorsT.J.H.M. Eggen, B.P. Veldkamp
Place of PublicationEnschede
PublisherRCEC
Pages14-25
Number of pages180
ISBN (Print)9789036533744
DOIs
Publication statusPublished - 2012

Publication series

Name
PublisherRCEC

Keywords

  • METIS-292528
  • IR-80211

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