Detecting atypical student behaviour on a e-learning system

• Félix Castro ^[1] ; Alfredo Vellido ^[2] ; Ángela Nebot ^[2] ; Julia Minguillón ^[3]
  1. [1] Universidad Autónoma del Estado de Hidalgo

     Universidad Autónoma del Estado de Hidalgo

     México

     
  2. [2] Universitat Politècnica de Catalunya

     Universitat Politècnica de Catalunya

     Barcelona, España

     
  3. [3] Universitat Oberta de Catalunya

     Universitat Oberta de Catalunya

     Barcelona, España

     

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• Localización: Actas del I Simposio Nacional de Tecnologías de la Información y de las Comunicaciones en la Educación / Manuel Ortega Cantero (ed. lit.) , Asociación para el Desarrollo de la Informática Educativa (aut.), 2005, ISBN 84-9732-437-4, págs. 153-160
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • E-learning systems such as virtual campus environments have established themselves as a strong alternative to traditional distance university education. In this scenario, the Internet allows the gathering of information on many aspects of students’ online behavior in nearly real time. The knowledge extracted from this information can be used to define personalization strategies tailored to the students’ needs and requirements. In this brief study we introduce a model to detect atypical behavior on the grouping structure of the users of a real virtual campus (Open University of Catalonia). Experiments carried out on these data indicate that atypical students’ behavior can be identified and interpreted with a novel model that, simultaneously, neutralizes the negative impact of outliers on the data clustering process.