Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing

Palabras clave: Computerized adaptive testing, association rules, e-learning, intelligent

Resumen

Una de las etapas más importantes de las pruebas adaptativas informatizadas es la selección de ítems, en la cual se utilizan diversos métodos que presentan ciertas debilidades al momento de su aplicación. Así, en este trabajo, se propone la integración de la minería de reglas de asociación como criterio de selección de ítems en un sistema CAT. Se presenta el análisis de algoritmos de minería de reglas de asociación como Apriori, FP-Growth, PredictiveApriori y Tertius en dos conjuntos de datos con el fin de conocer las ventajas y desventajas de cada algoritmo y elegir el más adecuado. Se compararon los algoritmos teniendo en cuenta el número de reglas descubiertas, el soporte y confianza promedios y la velocidad. Según los experimentos, Apriori encontró reglas con mayor confianza y soporte en un menor tiempo.

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Biografía del autor/a

Josué Pacheco Ortiz, Tecnológico Nacional de México

Master in Administrative Engineering.

Lisbeth Rodríguez Mazahua, Tecnológico Nacional de México

PhD in Computer Science

Jezreel Mejía miranda, Centro de Investigación en Matemáticas CIMAT

PhD in Computer Science

Isaac Machorro Cano, Universidad del Papaloapan

PhD in Engineering Sciences

Giner Alor Hernández, Tecnológico Nacional de México

PhD in Electrical Engineering

Ulises Juárez Martínez, Tecnológico Nacional de México

PhD in Science in Electrical Engineering

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Publicado
2020-12-01
Cómo citar
Pacheco Ortiz, J., Rodríguez Mazahua, L., Mejía miranda, J., Machorro Cano, I., Hernández, G. A., & Juárez Martínez, U. (2020). Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing. Revista Perspectiva Empresarial, 7(2 Supl.1), 19-30. https://doi.org/10.16967/23898186.666
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