Methodological aspects of the highly adaptive testing design for PISA

This methods paper describes the methodological and statistical underpinnings of the highly adaptive testing design (HAT), which was developed for the Programme for International Student Assessment (PISA). The aim of HAT is to allow for a maximum of adaptivity in selecting items while taking the constraints of PISA into account with appropriate computer algorithms. HAT combines established methods from the area of computerized adaptive testing (a) to improve item selection when items are nested in units, (b) to make use of the correlation between the dimensions measured, (c) to efficiently accomplish constraint management, (d) to control for item position effects, and (e) to foster students' test-taking experience. The algorithm is implemented using the programming language R and readers are provided with the necessary code. This should facilitate future implementations of the HAT design and inspire other adaptive testing designs that aim to maximize adaptivity while meeting constraints.

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Publication Details

Journal
Frontiers in Psychology
Published
2024-09-17
DOI
https://doi.org/10.3389/fpsyg.2024.1446799
Primary Topic
Psychometric Methodologies and Testing
Type
article
Field-Weighted Citation Impact
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article

Methodological aspects of the highly adaptive testing design for PISA

Aron Fink, Christoph König, Andreas Frey
Frontiers in Psychology
Psychometric Methodologies and Testing
article

Methodological aspects of the highly adaptive testing design for PISA

Aron Fink, Christoph König, Andreas Frey
article en

Abstract

This methods paper describes the methodological and statistical underpinnings of the highly adaptive testing design (HAT), which was developed for the Programme for International Student Assessment (PISA). The aim of HAT is to allow for a maximum of adaptivity in selecting items while taking the constraints of PISA into account with appropriate computer algorithms. HAT combines established methods from the area of computerized adaptive testing (a) to improve item selection when items are nested in units, (b) to make use of the correlation between the dimensions measured, (c) to efficiently accomplish constraint management, (d) to control for item position effects, and (e) to foster students' test-taking experience. The algorithm is implemented using the programming language R and readers are provided with the necessary code. This should facilitate future implementations of the HAT design and inspire other adaptive testing designs that aim to maximize adaptivity while meeting constraints.

Frontiers in PsychologyVol. 15
Goethe University Frankfurt (DE)
Openalex Percentile: Top 81%
Psychometric Methodologies and Testing
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