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Methodological aspects of the highly adaptive testing design for PISA.

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Τίτλος: Methodological aspects of the highly adaptive testing design for PISA.
Συγγραφείς: Fink, Aron, König, Christoph, Frey, Andreas
Πηγή: Frontiers in Psychology; 10/1/2024, p1-7, 7p
Θεματικοί όροι: Adaptive testing, Item response theory, Computer adaptive testing, Algorithms, Hat design & hat making, Test design
Εταιρία/Οντότητα: Programme for International Student Assessment
Περίληψη: 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. [ABSTRACT FROM AUTHOR]
Copyright of Frontiers in Psychology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Βάση Δεδομένων: Complementary Index
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  Data: Methodological aspects of the highly adaptive testing design for PISA.
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  Data: <searchLink fieldCode="DE" term="%22Adaptive+testing%22">Adaptive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Item+response+theory%22">Item response theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+adaptive+testing%22">Computer adaptive testing</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Hat+design+%26+hat+making%22">Hat design & hat making</searchLink><br /><searchLink fieldCode="DE" term="%22Test+design%22">Test design</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Frontiers in Psychology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3389/fpsyg.2024.1446799
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      – Code: eng
        Text: English
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              Text: 10/1/2024
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