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ContextInformed Adaptive Hypermedia

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Title: ContextInformed Adaptive Hypermedia


1
Context-Informed Adaptive Hypermedia
  • Alexander OConnor
  • Owen Conlan
  • Vincent Wade
  • oconnoat, Owen.Conlan, Vincent.Wade_at_cs.tcd.ie
  • Knowledge Data Engineering Group
  • Trinity College, Dublin

2
Adaptive Hypermedia for eLearning
  • Developed from Intelligent Tutoring Systems (ITS)
    and Hypertext
  • Adaptive Hypermedia1 systems compose content
    based on rules and course design with reference
    to model of learner
  • Models are generally highly detailed.

3
APeLS2
  • Adaptive Personalised eLearning Service
  • Multi-Model Metadata Driven AH System
  • Uses Jess to build an XML document of the course
  • Narrative compares attributes of the Content and
    the Learner Model
  • Content is referred to indirectly
  • Candidate Groups

4
APeLS Architecture
Narrative Models
Content
Learner Models
5
Context for Adaptive Hypermedia
  • Context in Adaptive Hypermedia is composed of a
    variable set of axes with the following
    properties
  • Not core model components
  • Potentially interesting to the system
  • Context has deep and shallow models
  • Context for one system might not be context for
    another

6
Why Add Context?
  • AH systems already have methods for modeling
    relevant data.
  • These methods are tailored and effective
  • But, the models tend to be complex
  • Need some way to handle extra concerns easily
  • Define Context as data that would be useful, but
    is not core to the AH

7
Context-Informed AH
  • Context supports additional concerns for the AH
  • Factors not specified when the course was created
  • A Context Interpreter translates these extra
    factors into a known vocabulary
  • This is done by providing mechanisms to pass
    information about the state of the narrative and
    models to the CI, which can make changes
  • Decisions on a list of concepts/entities passed

8
Context-Informed APeLS
Narrative Models
Content
Learner Models
9
Mechanisms
  • Complete Model Enrichment
  • Pass the contents of a model to the Adaptive
    engine, which alters it
  • User Model Update
  • Selected Model Enrichment
  • Context decides on the membership or order of a
    portion of the model from a list provided
  • Candidate Group Manipulation
  • Collaborative Dialogue
  • Define Decision points which are answered by
    Context
  • Narrative Choice

Different Mechanisms impose different
requirements for shared knowledge
10
Advantages
  • This method permits Adaptive Hypermedia to make
    use of a wider knowledge set.
  • Without having to model it directly
  • Increases Adaptivity
  • Provides Interoperability framework
  • The integrity of AH core deep models is
    maintained
  • While the CI is able to employ shallower
    techniques

11
Conclusions
  • Separated Architecture
  • Core concerns are modeled deeply by Adaptive
    Hypermedia
  • Context handles extra inputs separately
  • Translated to terms known to the Adaptive Engine
  • Applications to other systems

12
References
  • Brusilovsky, P. Methods and techniques of
    adaptive hypermedia. In P. Brusilovsky and J.
    Vassileva (eds.), Spec. Iss. On Adaptive
    Hypertext and Hypermedia, User Modeling and
    User-Adapted Interaction 6 (2-3), 87-129
  • Conlan, O. Wade, V. Bruen, C. Gargan, M.
    Multi-Model, Metadata Driven Approach to Adaptive
    Hypermedia Services for Personalized eLearning.
    Second International Conference on Adaptive
    Hypermedia and Adaptive Web-Based Systems,
    Malaga, Spain, May 2002
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