1 / 14

2 nd International Workshop on Managing Ubiquitous Communications and Services

2 nd International Workshop on Managing Ubiquitous Communications and Services Trinity College, Dublin December 13 th & 14 th , 2004 http://www.MUCS2004.org MUCS2004@cs.tcd.ie Submission Deadline: 27 th September 2004 Context-Informed Adaptive Hypermedia Alexander O’Connor Owen Conlan

paul
Download Presentation

2 nd International Workshop on Managing Ubiquitous Communications and Services

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. 2nd International Workshop on Managing Ubiquitous Communications and Services Trinity College, Dublin December 13th & 14th, 2004 http://www.MUCS2004.org MUCS2004@cs.tcd.ie Submission Deadline: 27th September 2004

  2. Context-Informed Adaptive Hypermedia Alexander O’Connor Owen Conlan Vincent Wade {oconnoat, Owen.Conlan, Vincent.Wade}@cs.tcd.ie Knowledge & Data Engineering Group Trinity College, Dublin

  3. Overview • Introduction to Adaptive Hypermedia • APeLS • Context for Adaptive Hypermedia • Mechanisms for Context-Informed Adaptive Hypermedia • Analysis • Conclusions

  4. Adaptive Hypermedia for eLearning • Developed from Intelligent Tutoring Systems (ITS) and Hypertext • Adaptive Hypermedia[1] systems compose content based on rules and course design with reference to model of learner • Models are generally highly detailed.

  5. APeLS[2] • Adaptive Personalised eLearning Service • Multi-Model Metadata Driven Adaptive Hypermedia 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

  6. Learner Model Content Model Learner Portal Learner Narrative Adaptive Service APeLS Architecture Narrative Models Content Learner Models

  7. 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-Informed Adaptive Hypermedia has ‘deep’ and ‘shallow’ models • Context for one system might not be context for another

  8. 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 • ‘Deep Models’ • Need some way to handle extra concerns easily • Define Context as data that would be useful, but is not core to the AH • ‘Shallow models’

  9. 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

  10. Context Interpreter Learner Model Content Model Learner Portal Learner Narrative Adaptive Service Context-Informed APeLS Narrative Models Content Learner Models

  11. 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

  12. 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

  13. Conclusions • Separated Architecture • Core concerns are modeled deeply by Adaptive Hypermedia • Context handles extra inputs separately • Translated to terms known to the Adaptive Engine • Use of shared vocabulary • Applications to other systems

  14. 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

More Related