Personalización en Recomendadores Basados en Contenido y su Aplicación a Repositorios de Objetos de Aprendizaje

• Almudena Ruiz Iniesta ^[1] ; Guillermo Jiménez Díaz ^[1] ; Mercedes Gómez Albarrán ^[1]
  1. [1] Universidad Complutense de Madrid

     Universidad Complutense de Madrid

     Madrid, España

     
• Localización: Revista Iberoamericana de Tecnologías del Aprendizaje: IEEE-RITA, ISSN 1932-8540, Vol. 5, Nº. 1 (Feb. 2010), 2010, págs. 31-38
• Idioma: español
• Títulos paralelos:
  • Promoting Strong Personalization in Content-based Recommendation Systems of Learning Objects
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• Resumen

  • Recommendation technologies have a clear application in e-learning: providing support for personalized access to the Learning Objects (LOs) that exist in repositories.

    In this paper we describe a novel approach that fosters a strong personalized content-based recommendation of LOs. This approach gives priority to those LOs that are most similar to the student’s short-term learning goals (the concepts that the student wants to learn in the session) and, at the same time, have a high pedagogical utility in the light of the student’s cognitive state (long-term learning goals). The paper includes the definition of a flexible metric that combines the similarity with the query and the pedagogical utility of the LO. We finally describe the application of the approach to an educational repository of Computer Programming LOs.