Bayesian Estimation and Testing of a Linear Logistic Test Model for Learning during the Test.
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| Title: | Bayesian Estimation and Testing of a Linear Logistic Test Model for Learning during the Test. |
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| Authors: | Lozano, José H. (AUTHOR), Revuelta, Javier (AUTHOR) |
| Source: | Applied Measurement in Education. Jul-Sep 2021, Vol. 34 Issue 3, p223-235. 13p. |
| Subjects: | Ability testing, Prediction models |
| Abstract: | The present study proposes a Bayesian approach for estimating and testing the operation-specific learning model, a variant of the linear logistic test model that allows for the measurement of the learning that occurs during a test as a result of the repeated use of the operations involved in the items. The advantages of using a Bayesian framework compared to the traditional frequentist approach are discussed. The application of the model is illustrated with real data from a logical ability test. The results show how the incorporation of previous practice into the linear logistic model improves the fit of the model as well as the prediction of the Rasch item difficulty estimates. The model provides evidence of learning associated with two of the logic operations involved in the items, which supports the hypothesis of practice effects in deductive reasoning tasks. [ABSTRACT FROM AUTHOR] |
| Copyright of Applied Measurement in Education is the property of Taylor & Francis Ltd 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.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: pbh DbLabel: Psychology and Behavioral Sciences Collection An: 152947607 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Bayesian Estimation and Testing of a Linear Logistic Test Model for Learning during the Test. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lozano%2C+José+H%2E%22">Lozano, José H.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Revuelta%2C+Javier%22">Revuelta, Javier</searchLink> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Applied+Measurement+in+Education%22">Applied Measurement in Education</searchLink>. Jul-Sep 2021, Vol. 34 Issue 3, p223-235. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Ability+testing%22">Ability testing</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The present study proposes a Bayesian approach for estimating and testing the operation-specific learning model, a variant of the linear logistic test model that allows for the measurement of the learning that occurs during a test as a result of the repeated use of the operations involved in the items. The advantages of using a Bayesian framework compared to the traditional frequentist approach are discussed. The application of the model is illustrated with real data from a logical ability test. The results show how the incorporation of previous practice into the linear logistic model improves the fit of the model as well as the prediction of the Rasch item difficulty estimates. The model provides evidence of learning associated with two of the logic operations involved in the items, which supports the hypothesis of practice effects in deductive reasoning tasks. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Applied Measurement in Education is the property of Taylor & Francis Ltd 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=152947607 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1080/08957347.2021.1933982 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 223 Subjects: – SubjectFull: Ability testing Type: general – SubjectFull: Prediction models Type: general Titles: – TitleFull: Bayesian Estimation and Testing of a Linear Logistic Test Model for Learning during the Test. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lozano, José H. – PersonEntity: Name: NameFull: Revuelta, Javier IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul-Sep 2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 08957347 Numbering: – Type: volume Value: 34 – Type: issue Value: 3 Titles: – TitleFull: Applied Measurement in Education Type: main |
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