Sistema recomendador colaborativo usando minería de datos distribuida para la mejora continua de cursos e-learning

• Enrique García Salcines ^[1] ; Cristóbal Romero Morales ^[1] ; Sebastián Ventura Soto ^[1] ; Carlos de Castro Lozano ^[1]
  1. [1] Universidad de Córdoba

     Universidad de Córdoba

     Cordoba, España

     
• Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 3, Nº. 1, 2008, págs. 19-30
• Idioma: español
• Títulos paralelos:
  • Collaborative recommender system using distributed rule mining for improving web-based adaptive courses.
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• Dialnet Métricas: 2 Citas
• Resumen

  • Nowadays, the application of data mining techniques in e-learning and web based adaptive educational systems is increasing exponentially. The discovered useful information can be used directly by the teacher or the author of the course to improve the instructional/learning performance. This can be an arduous task and therefore educational recommender systems are used in order to help the teacher in this task. In this paper we describe a recommender system oriented to suggest the most appropriate modifications to the teacher in order to improve the effectiveness of the course. We propose to use a cyclical methodology to develop and carry out the maintenance of web-based courses in which we have added a specific data mining step. We have developed a distributed rule mining system in order to discover information in the form of IFTHEN recommendation rules about the web courses. We have used an iterative and interactive association rule algorithm without parameters and with a weight-based evaluation measure of the rule interest. And we have used a collaborative recommender system to share and score the obtained recommendation rules in one specific course between teachers of other similar courses and some experts in education. Finally, we have carried out several experiments with real students in order to determine the effectiveness of the proposed system and the utility of the recommended rules.

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