Publication:

Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments

Date

2023
Journal article
https://doi.org/10.3758/s13428-022-01910-8
Simple item page Full metadata Statistics
 
dc.contributor.authorPark, Jung Yeon
dc.contributor.authorDedja, Klest
dc.contributor.authorPliakos, Konstantinos
dc.contributor.authorKim, Jinho
dc.contributor.authorJoo, Sean
dc.contributor.authorCornillie, Frederik
dc.contributor.authorVens, Celine
dc.contributor.authorVan den Noortgate, Wim
dc.contributor.imecauthorVan den Noortgate, Wim
dc.contributor.imecauthorDedja, Klest
dc.contributor.imecauthorCornillie, Frederik
dc.contributor.imecauthorVens, Celine
dc.contributor.orcidimecVan den Noortgate, Wim::0000-0003-4011-219X
dc.contributor.orcidimecDedja, Klest::0000-0001-5280-6717
dc.contributor.orcidimecCornillie, Frederik::0000-0002-4820-7970
dc.contributor.orcidimecVens, Celine::0000-0003-0983-256X
dc.date.accessioned2023-11-23T16:27:56Z
dc.date.available2022-07-22T02:26:53Z
dc.date.available2023-11-23T16:27:56Z
dc.date.embargo2022-12-16
dc.date.issued2023
dc.description.wosFundingTextThis work was carried out within imec's Smart Education research programme, with support from the Flemish government. This research received funding from the Flemish AI Research Program. Also, this work was supported by the 2021 Research Fund of the University of Seoul for Jinho Kim.
dc.identifier.doi10.3758/s13428-022-01910-8
dc.identifier.issn1554-351X
dc.identifier.pmidMEDLINE:35819719
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/40161
dc.publisherSPRINGER
dc.source.beginpage2109
dc.source.endpage2124
dc.source.issue14
dc.source.journalBEHAVIOR RESEARCH METHODS
dc.source.numberofpages16
dc.source.volume55
dc.subject.keywordsCLASSIFIERS
dc.title

Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments

dc.typeJournal article
dspace.entity.typePublication
Files

Original bundle

Name:
Main_manuscript_2nd resubmission and accepted ver.pdf
Size:
801.35 KB
Format:
Adobe Portable Document Format
Description:
Accepted version
Download
Publication available in collections:
Articles