A Bayesian General Model to Account for Individual Differences in Operation-Specific Learning Within a Test
Bibliographic Data
| ID | 20286157 |
|---|---|
| Authors | José H Lozano (0000-0003-4659-5663, Universidad Autónoma de Madrid, corresponding author), Javier Revuelta (0000-0003-4705-6282, Universidad Autónoma de Madrid) |
| Year | 2023 |
| Volume | 83 |
| Issue | 4 |
| Pages | 782-807 |
| Publication date | 2023-08-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Educational and Psychological Measurement (JOURNAL) |
| Journal identifiers | ISSN: 0013-1644 • E-ISSN: 1552-3888 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/00131644221109796 |
| PMID | 37398844 |
| OpenAlex | W4296784804 |
| Language | EN |
| References cited | 28 |
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
Bayesian inference · Bayesian probability · Data mining · Econometrics · Item response theory · Machine learning · Measure (data warehouse) · Model selection · Psychometrics · Selection (genetic algorithm) · Statistics · Test (biology) · Artificial Intelligence · Computer Science · Control Systems and Identification · Mathematics · Optimal Experimental Design Methods · Statistical Methods and Inference
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| Highly cited | No |
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