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Bayesian Estimation and Testing of a Linear Logistic Test Model for Learning during the Test

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Bibliographic Details
Title: Bayesian Estimation and Testing of a Linear Logistic Test Model for Learning during the Test
Language: English
Authors: Lozano, José H., Revuelta, Javier (ORCID 0000-0003-4705-6282)
Source: Applied Measurement in Education. 2021 34(3):223-235.
Availability: Routledge. Available from: Taylor & Francis, Ltd. 530 Walnut Street Suite 850, Philadelphia, PA 19106. Tel: 800-354-1420; Tel: 215-625-8900; Fax: 215-207-0050; Web site: http://www.tandf.co.uk/journals
Peer Reviewed: Y
Page Count: 13
Publication Date: 2021
Document Type: Journal Articles
Reports - Research
Descriptors: Bayesian Statistics, Computation, Learning, Testing, Statistical Analysis, Models, Test Items, Difficulty Level, Item Response Theory, Logical Thinking
DOI: 10.1080/08957347.2021.1933982
ISSN: 0895-7347
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.
Abstractor: As Provided
Entry Date: 2021
Accession Number: EJ1312427
Database: ERIC
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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.
ISSN:0895-7347
DOI:10.1080/08957347.2021.1933982