The OECD’s programme for international student assessment (pisa) study: A review of its basic psychometric concepts

Ali Ünlü, Daniel Kasper, Matthias Trendtel, Michael Schurig

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

The Programme for International Student Assessment (PISA; e.g., OECD, Sample tasks from the PISA 2000 assessment, 2002a; OECD, Learning for tomorrow’s world: first results from PISA 2003, 2004; OECD, PISA 2006: Science competencies for tomorrow’s world, 2007; OECD, PISA 2009 Technical Report, 2012) is an international large scale assessment study that aims to assess the skills and knowledge of 15-year-old students, and based on the results, to compare education systems across the participating (about 70) countries (with a minimum number of approx. 4,500 tested students per country). Initiator of this Programme is the Organisation for Economic Co-operation and Development (OECD; www.​pisa.​oecd.​org). We review the main methodological techniques of the PISA study. Primarily, we focus on the psychometric procedure applied for scaling items and persons. PISA proficiency scale construction and proficiency levels derived based on discretization of the continua are discussed. For a balanced reflection of the PISA methodology, questions and suggestions on the reproduction of international item parameters, as well as on scoring, classifying and reporting, are raised. We hope that along these lines the PISA analyses can be better understood and evaluated, and if necessary, possibly be improved.

Original languageEnglish
Title of host publicationData Analysis, Machine Learning and Knowledge Discovery
EditorsLars Schmidt-Thieme, Ruth Janning, Myra Spiliopoulou
PublisherKluwer Academic Publishers
Pages417-425
Number of pages9
ISBN (Print)9783319015941
DOIs
StatePublished - 2014
Event36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery, GfKl 2012 - Hildesheim, Germany
Duration: 1 Aug 20123 Aug 2012

Publication series

NameStudies in Classification, Data Analysis, and Knowledge Organization
Volume47
ISSN (Print)1431-8814

Conference

Conference36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery, GfKl 2012
Country/TerritoryGermany
CityHildesheim
Period1/08/123/08/12

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