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Determining the scoring validity of a co-constructed Cefr-based rating scale

Bibliographic Data

ID19381027
AuthorsBart Deygers (0000-0002-3198-0449, KU Leuven), Koen Van Gorp (0000-0002-2033-3852, KU Leuven)
Year2015
Volume32
Issue4
Pages521-541
Publication date2015-10-01
Open AccessYes
TypeARTICLE
VenueLanguage Testing (JOURNAL)
Journal identifiersISSN: 0265-5322 • E-ISSN: 1477-0946
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/0265532215575626
OpenAlexW2327696282
LanguageEN
Citations received7
References cited31

Considering scoring validity as encompassing both reliable rating scale use and valid descriptor interpretation, this study reports on the validation of a CEFR-based scale that was co-constructed and used by novice raters. The research questions this paper wishes to answer are (a) whether it is possible to construct a CEFR-based rating scale with novice raters that yields reliable ratings and (b) allows for a uniform interpretation of the descriptors. Additionally, this study focuses on the question whether co-constructing a rating scale with novice raters helps to stimulate a shared interpretation of the descriptors over time. For this study, six novice raters employed a CEFR-based scale that had been co-constructed by themselves and 14 peers to rate 200 spoken and written performances in a missing data design. The quantitative data were analysed using item response theory, classical test theory and principal component analysis. The focus group data, collected after the rating process, were transcribed and coded using both a priori and inductive coding. The results indicate that novice raters can reliably use the CEFR-based rating scale, but that the interpretations of the descriptors, in spite of training and co-construction, are not as homogeneous as the inter-rater reliability would suggest

Construct validity · Developmental psychology · Natural language processing · Psychometrics · Rating scale · Computer Science · Educational and Psychological Assessments · Psychology · Psychometric Methodologies and Testing · Student Assessment and Feedback · Artificial Intelligence

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Citations per year0.88
Citation span2018 - 2026 (9)
Highly citedNo

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