2 nd International Workshop on Managing Ubiquitous Communications and Services

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