IRT-based classification analysis of an english language reading proficiency subtest
buir.contributor.author | Kaya, Elif | |
buir.contributor.author | O’Grady, Stefan | |
buir.contributor.author | Kalender, İlker | |
buir.contributor.orcid | Kaya, Elif | 0000-0002-0482-1338 | |
buir.contributor.orcid | O’Grady, Stefan| 0000-0003-3810-713X | |
buir.contributor.orcid | Kalender| İlker 0000-0003-1282-4149 | |
dc.citation.epage | 566 | en_US |
dc.citation.issueNumber | 4 | en_US |
dc.citation.spage | 541 | en_US |
dc.citation.volumeNumber | 39 | en_US |
dc.contributor.author | Kaya, Elif | |
dc.contributor.author | O’Grady, Stefan | |
dc.contributor.author | Kalender, İlker | |
dc.date.accessioned | 2023-02-15T12:30:37Z | |
dc.date.available | 2023-02-15T12:30:37Z | |
dc.date.issued | 2022 | |
dc.department | English Language Preparatory Program | en_US |
dc.description.abstract | Language proficiency testing serves an important function of classifying examinees into different categories of ability. However, misclassification is to some extent inevitable and may have important consequences for stakeholders. Recent research suggests that classification efficacy may be enhanced substantially using computerized adaptive testing (CAT). Using real data simulations, the current study investigated the classification performance of CAT on the reading section of an English language proficiency test and made comparisons with the paper based version of the same test. Classification analysis was carried out to estimate classification accuracy (CA) and classification consistency (CC) by applying different locations and numbers of cutoff points. The results showed that classification was suitable when a single cutoff score was used, particularly for high- and low-ability test takers. Classification performance declined significantly when multiple cutoff points were simultaneously employed. Content analysis also raised important questions about construct coverage in CAT. The results highlight the potential for CAT to serve classification purposes and outline avenues for further research. | en_US |
dc.description.provenance | Submitted by Mandana Moftakhari (mandana.mir@bilkent.edu.tr) on 2023-02-15T12:30:37Z No. of bitstreams: 1 IRT-based_classification_analysis_of_an_English_language_reading_proficiency_subtest.pdf: 591737 bytes, checksum: 4a7b338ea4c48b9c06eedf36a4d654b5 (MD5) | en |
dc.description.provenance | Made available in DSpace on 2023-02-15T12:30:37Z (GMT). No. of bitstreams: 1 IRT-based_classification_analysis_of_an_English_language_reading_proficiency_subtest.pdf: 591737 bytes, checksum: 4a7b338ea4c48b9c06eedf36a4d654b5 (MD5) Previous issue date: 2022 | en |
dc.identifier.doi | 10.1177/02655322211068847 | en_US |
dc.identifier.eissn | 1477-0946 | |
dc.identifier.issn | 0265-5322 | |
dc.identifier.uri | http://hdl.handle.net/11693/111356 | |
dc.language.iso | English | en_US |
dc.publisher | SAGE | en_US |
dc.relation.isversionof | https://dx.doi.org/10.1177/02655322211068847 | en_US |
dc.source.title | Language Testing | en_US |
dc.subject | classification accuracy | en_US |
dc.subject | classification consistency | en_US |
dc.subject | computerized adaptive testing | en_US |
dc.subject | language proficiency | en_US |
dc.subject | Rudner approach | en_US |
dc.title | IRT-based classification analysis of an english language reading proficiency subtest | en_US |
dc.type | Article | en_US |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- IRT-based_classification_analysis_of_an_English_language_reading_proficiency_subtest.pdf
- Size:
- 577.87 KB
- Format:
- Adobe Portable Document Format
- Description:
License bundle
1 - 1 of 1
No Thumbnail Available
- Name:
- license.txt
- Size:
- 1.69 KB
- Format:
- Item-specific license agreed upon to submission
- Description: