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Multi-model, Metadata Driven Approach to Adaptive Hypermedia Services for Personalized eLearning

  • Owen Conlan
  • Vincent Wade
  • Catherine Bruen
  • Mark Gargan
Conference paper
  • 1k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2347)

Abstract

One of the major obstacles in developing quality eLearning content is the substantial development costs involved and development time required [12]. Educational providers, such as those in the university sector and corporate learning, are under increasing pressure to enhance the pedagogical quality and technical richness of their course offerings while at the same time achieving improved return on investment. One means of enhancing the educational impact of eLearning courses, while still optimizing the return on investment, is to facilitate the personalization and repurposing of learning objects across multiple related courses. However, eLearning courses typically differ strongly in ethos, learning goals and pedagogical approach whilst learners, even within the same course, may have different personal learning goals, motivations, prior knowledge and learning style preferences. This paper proposes an innovative multi-model approach to the dynamic composition and delivery of personalized learning utilizing reusable learning objects. The paper describes an adaptive metadata driven engine that composes, at runtime, tailored educational experiences across a single content base. This paper presents the theoretical models, design and implementation of the adaptive hypermedia educational service. This service is currently being successfully used for the delivery of undergraduate degree courses in Trinity College, Dublin as well as being used as part of a major EU research trial.

Keywords

Learning Object Instructional Designer Learner Model Content Model Intelligent Tutor System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Owen Conlan
    • 1
  • Vincent Wade
    • 1
    • 2
  • Catherine Bruen
    • 2
  • Mark Gargan
    • 1
  1. 1.Knowledge and Data Engineering GroupTrinity CollegeDublin
  2. 2.Centre for Learning TechnologyTrinity CollegeDublin

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