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A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning within a Test

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Title: A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning within a Test
Language: English
Authors: Lozano, José H. (ORCID 0000-0003-4659-5663), Revuelta, Javier (ORCID 0000-0003-4705-6282)
Source: Educational and Psychological Measurement. Aug 2023 83(4):782-807.
Availability: SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com
Peer Reviewed: Y
Page Count: 26
Publication Date: 2023
Document Type: Journal Articles
Reports - Research
Descriptors: Bayesian Statistics, Learning Processes, Test Items, Item Analysis, Accuracy, Learning Analytics, Evaluation Methods, Logical Thinking, Monte Carlo Methods, Markov Processes, Models, Cognitive Ability, Goodness of Fit
DOI: 10.1177/00131644221109796
ISSN: 0013-1644
1552-3888
Abstract: The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and incorrect responses, which allows for distinguishing different types of learning effects in the data. Model estimation and evaluation is based on a Bayesian framework. A simulation study is presented that examines the performance of the estimation and evaluation methods. The results show accuracy in parameter recovery as well as good performance in model evaluation and selection. An empirical study illustrates the applicability of the model to data from a logical ability test.
Abstractor: As Provided
Entry Date: 2023
Accession Number: EJ1381815
Database: ERIC
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  Data: A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning within a Test
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  Data: <searchLink fieldCode="AR" term="%22Lozano%2C+José+H%2E%22">Lozano, José H.</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4659-5663">0000-0003-4659-5663</externalLink>)<br /><searchLink fieldCode="AR" term="%22Revuelta%2C+Javier%22">Revuelta, Javier</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4705-6282">0000-0003-4705-6282</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Educational+and+Psychological+Measurement%22"><i>Educational and Psychological Measurement</i></searchLink>. Aug 2023 83(4):782-807.
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  Data: The present paper introduces a general multidimensional model to measure individual differences in learning within a single administration of a test. Learning is assumed to result from practicing the operations involved in solving the items. The model accounts for the possibility that the ability to learn may manifest differently for correct and incorrect responses, which allows for distinguishing different types of learning effects in the data. Model estimation and evaluation is based on a Bayesian framework. A simulation study is presented that examines the performance of the estimation and evaluation methods. The results show accuracy in parameter recovery as well as good performance in model evaluation and selection. An empirical study illustrates the applicability of the model to data from a logical ability test.
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        Value: 10.1177/00131644221109796
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        StartPage: 782
    Subjects:
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        Type: general
      – SubjectFull: Learning Processes
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      – SubjectFull: Test Items
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      – SubjectFull: Item Analysis
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      – SubjectFull: Accuracy
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      – SubjectFull: Learning Analytics
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      – SubjectFull: Evaluation Methods
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      – SubjectFull: Logical Thinking
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      – SubjectFull: Monte Carlo Methods
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      – SubjectFull: Markov Processes
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      – SubjectFull: Models
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      – SubjectFull: Cognitive Ability
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      – SubjectFull: Goodness of Fit
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      – TitleFull: A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning within a Test
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