1. 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

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 Introduction to Adaptive Hypermedia APeLS APeLS Context for Adaptive Hypermedia Context for Adaptive Hypermedia Mechanisms for Context-Informed Adaptive Hypermedia Mechanisms for Context-Informed Adaptive Hypermedia Analysis Analysis Conclusions Conclusions

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

5. APeLS[2] Adaptive Personalised eLearning Service Adaptive Personalised eLearning Service Multi-Model Metadata Driven Adaptive Hypermedia System Multi-Model Metadata Driven Adaptive Hypermedia System Uses Jess to build an XML document of the course 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 Candidate Groups

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

7. Context for Adaptive Hypermedia Context in Adaptive Hypermedia is composed of a variable set of axes with the following properties: 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-Informed Adaptive Hypermedia has ‘deep’ and ‘shallow’ models Context for one system might not be context for another Context for one system might not be context for another

8. Why Add Context? AH systems already have methods for modeling relevant data. AH systems already have methods for modeling relevant data. –These methods are tailored and effective –But, the models tend to be complex ‘Deep Models’ ‘Deep Models’ Need some way to handle extra concerns easily 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’ ‘Shallow models’

9. Context-Informed AH Context supports additional concerns for the 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 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 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. Adaptive Service Context-Informed APeLS Content Learner Model Content Model Narrative Learner Models Learner Portal Narrative Models Context Interpreter

11. Mechanisms Complete Model Enrichment Complete Model Enrichment –Pass the contents of a model to the Adaptive engine, which alters it –User Model Update Selected Model Enrichment 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 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. 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 The integrity of AH core ‘deep’ models is maintained –While the CI is able to employ ‘shallower’ techniques

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

14. References 1. 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 2. 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