Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing
One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criter...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2020 |
| País: | México |
| Institución: | Universidad del Papaloapan |
| Repositorio: | Redalyc-UNPA |
| OAI Identifier: | oai:redalyc.org:672271538003 |
| Acceso en línea: | https://www.redalyc.org/articulo.oa?id=672271538003 |
| Access Level: | acceso abierto |
| Palabra clave: | Economía y Finanzas learning association rules intelligent systems Computerized adaptive testing |
| Sumario: | One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criterion in a CAT system. We present the analysis of association rule mining algorithms such as Apriori, FP-Growth, PredictiveApriori and Tertius into two data set with the purpose of knowing the advantages and disadvantages of each algorithm and choose the most suitable. We compare the algorithms considering number of rules discovered, average support and confidence, and velocity. According to the experiments, Apriori found rules with greater confidence, support, in less time. |
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