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 |
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| Language: | English |
| Authors: | Lozano, José H. (ORCID |
| 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 |
| 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. |
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| ISSN: | 0013-1644 1552-3888 |
| DOI: | 10.1177/00131644221109796 |
