1. The Multi-model, Metadata-driven Approach to Content and Layout Adaptation Owen.Conlan@cs.tcd.ie Knowledge and Data Engineering Group (KDEG) Trinity College, Dublin University of Dublin Trinity College

2. Overview Adaptive Hypermedia Systems and Services – Methods of Adaptivity Metadata for Representing Adaptivity Multi-Model, Metadata Driven Approach to Adaptive Hypermedia Services – Narrative, Architecture Adaptive Layout – Layout Model Multiple Adaptive Engines University of Dublin Trinity College

3. Adaptive Hypermedia Systems What are the components of a typical AHS? – A User model (may be individual or stereotypical) – A mechanism to produce personalized content Why are AHSs difficult to maintain? – The content and the rules that govern how that content is personalized are usually intertwined – This makes it difficult to – Add/Modify new content Change the structure of the content Use only a sub-section of the content University of Dublin Trinity College

4. Adaptive Hypermedia Systems User Model Repository Narrative /Content Repository AHS Engine Personalized Content University of Dublin Trinity College

5. Data about the User User Model System Adaptation Effect User modelling Adaptation Collects Processes University of Dublin Trinity College User, Device, Environment, etc. Context Modelling Context Information

6. Methods of Adaptivity Adaptive Presentation – Personalization of content delivered Adaptive Navigation – Dynamically generated navigation and paths Historical Adaptation – Time context Structural Adaptation – Spatial representations University of Dublin Trinity College

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8. Multi-model, Metadata Driven Approach Metadata to describe Adaptive Resources Multi-model Two versions of the approach – 3 Models – Content, Learner and Narrative (PLS) – N Models – At least one Narrative, the rest are metadata based (APeLS) University of Dublin Trinity College

9. Metadata for describing Adaptive Resources1 Developed as part of EASEL (IST Project 10051) – Educator Access to Services in the Electronic Landscape Appropriate Descriptive Metadata to facilitate discovery and reuse of Adaptive Electronic Learning Objects Extension of IEEE LOM and IMS LRM University of Dublin Trinity College

10. Metadata for describing Adaptive Resources2 Current specifications don’t facilitate the description of Adaptive Resources – Full Adaptive Hypermedia Systems – Reusable Adaptive Components As part of EASEL the IMS Learning Resource Metadata v1.2 was extended to facilitate the complex nature of Adaptive Learning Resources University of Dublin Trinity College

11. XML Metadata Representation Functions.Concept Funktionen.Konzept... University of Dublin Trinity College

12. Basic Schema View for Adaptivity adaptivitytype* name= ref= ? set? type=“one-or-more“|“all“|... set* candidate* langstring* University of Dublin Trinity College

13. Multi-Model, Metadata Driven Approach The Multi-model, Metadata Driven approach separates the models used in adaptation (e.g. Narrative, Learner and Content) from each other Provides a generic run-time engine for interpreting Narratives and reconciling models to produce an adaptation effect. University of Dublin Trinity College

14. Simple 3 Model Architecture Adaptive Engine Content Learner Learner Interface Learner Model Content Model Narrative Learner Models Narrative Models University of Dublin Trinity College

15. Multi-model Approach – Requirements Separate – – User Model Pertinent information that the system can use to personalize to the user’s preferences – Content Model Describes the individual pieces of content – Narrative Model Describes how the content can be structured/sequenced for different needs – Other Models Device, Environment, Layout etc. Provide appropriate alternative candidates Provide an abstraction layer and selection criteria University of Dublin Trinity College

16. Multi-model Approach – Narrative 1 The Narrative Model is – – The Embodiment of a Domain Experts Knowledge – Represented in Jess (Expert System Shell for Java) – Responsible for assembling the personalized course The Narrative can access any metadata in the repositories Narrative is described at a conceptual level, i.e. it does not refer directly to learning content. University of Dublin Trinity College

17. Multi-model Approach – Narrative 2 There may be multiple Narrative Models for a single course There is a Candidate Narrative Repository Each Narrative also has associated metadata A Narrative may be comprised of sub- narratives University of Dublin Trinity College

18. Multi-model Approach - Candidates What are candidates? – Elements that fulfil the same role… Pieces of content that cover the same material Narratives that produce courses from the same content body – …but achieve that role differently The content candidates may be textual, graphical or interactive Narrative candidates may support different approaches to learning University of Dublin Trinity College

19. Candidate Content Groups A Content Candidate is a pagelet and its associated metadata A Candidate Content Group contains Candidates that fulfil the same learning objective, but are implemented differently The Narrative can refer to Groups rather than individual pieces of content Most appropriate Candidate selected at runtime by looking at the Learner model University of Dublin Trinity College

20. Multi-model Approach – Abstraction and Selection Abstraction – Narratives are built using concept names rather than content identifiers – Enables the service to use the most appropriate candidate Selection – There criteria used to select a candidate from a group of potential candidates are based upon – The candidates metadata The learner’s metadata University of Dublin Trinity College

21. A Generic Architecture The Adaptive Hypermedia Service is designed to facilitate multiple tiers Each tier can achieve one (or more) axes of adaptivity Facilitated by metadata Supported by an extensible AI mechanisms University of Dublin Trinity College

22. Adaptive Hypermedia Service – APeLS Architecture Rules Engine Candidate Selector Learner Metadata Repository Candidate Content Groups Candidate Narrative Groups Content Metadata Repository Content Repository Narrative Repository Personalized Course Model (XML) Personalized Course Content Adaptive Engine Learner Input Learner Modeler Narrative Metadata Repository University of Dublin Trinity College Transform

23. What about Layout? Adaptive Engine Stylesheet Elements Learner Model Stylesheet Elements Context Learner Models Context Information University of Dublin Trinity College Layout Strategy Layout Tailored Layout Model

24. Adaptive Layout Rules Engine Candidate Selector Learner Metadata Repository Candidate Content Groups Candidate Narrative Groups Content Metadata Repository Content Repository Narrative Repository Personalized Course Model (XML) Personalized Course Content Adaptive Engine Learner Input Learner Modeler Narrative Metadata Repository University of Dublin Trinity College XSLT Transform Tailored Layout Model

25. University of Dublin Trinity College Adaptive Service AE Adapted Output Strategy Metadata Adaptive Service AE Adapted Output Strategy Metadata Adaptive Engine Adapted Output Strategy Adaptive Service AE Adapted Output Strategy Metadata Multiple Adaptive Services (APeLS II)

26. Summary Adaptive Hypermedia Services can deliver information personalised for the user’s needs – They can also tailor delivery towards environment and device (Context) Personalization and Adaptation may be facilitated by appropriate metadata The tiers of the multi-model, metadata approach may be used to implement different axes of adaptivity University of Dublin Trinity College

27. Thank You! University of Dublin Trinity College Owen.Conlan@cs.tcd.ie Knowledge and Data Engineering Group (KDEG) Trinity College, Dublin http://kdeg.cs.tcd.ie www.iclass.info www.m-zones.org