the multi-model, metadata-driven approach to content and layout adaptation [email protected]...

The Multi-model, Metadata-driven Approach to Content and Layout

Adaptation

[email protected] and Data Engineering Group

(KDEG)

Trinity College, Dublin

University of DublinTrinity College

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


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


Adaptive Hypermedia Systems

UserModel

Repository

Narrative /Content

Repository

AHS Engine

PersonalizedContent


Data about the User

User ModelSystem

Adaptation Effect

User modelling

Adaptation

Colle

cts

Processes

Processes


User, Device, Environment, etc.

Context Modelling

Context Information

Methods of Adaptivity

Adaptive Presentation– Personalization of content delivered

Adaptive Navigation– Dynamically generated navigation and paths

Historical Adaptation– Time context

Structural Adaptation– Spatial representations


Adaptive Techniques

Adaptive Presentation Adaptive Navigation

Multimedia

Text

Direct Guidance

Link Sorting

Link Hiding

Link Annotation

Map Adaptation

Natural Language

Canned Text

Inserting/Removing

Altering

Stretchtext

Sorting

Hiding

Disabling

Removal


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)


Metadata for describing Adaptive Resources 1

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


Metadata for describing Adaptive Resources 2

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


XML Metadata Representation<adaptivity>

<adaptivitytype name=“competencies.required” ref=“…”>

<set type=“all”>

<candidate>

<langstring lang=“en”>Functions.Concept</langstring>

<langstring lang=“de”>Funktionen.Konzept</langstring>

</candidate>

<candidate> ... </candidate>

</set>

</adaptivitytype>

</adaptivity>


Basic Schema View for Adaptivity

adaptivitytype*name=<langstring>ref=<URI>?

set?type=“one-or-more“|“all“|...

set*candidate*

langstring*


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.


Simple 3 Model Architecture

Adaptive Engine

Content

LearnerLe

arn

er

Inte

rface

LearnerModel

ContentModel

Narrative LearnerModels

NarrativeModels


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


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.


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


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


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


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


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


Adaptive Hypermedia Service – APeLS Architecture

Rules Engine

CandidateSelector

Learner Metadata

Repository


Candidate Narrative Groups

Content Metadata

Repository

Content Repository

NarrativeRepository

Personalized Course Model

(XML)

Personalized Course Content

Adaptive

Engine

Learner InputLearner Modeler

Narrative Metadata

Repository


Transform

What about Layout?

Adaptive Engine

StylesheetElements

LearnerModel

StylesheetElements

ContextLearnerModels

Context Information


LayoutStrategy

LayoutTailored Layout Model

Adaptive Layout

Rules Engine

CandidateSelector

Learner Metadata

Repository


Candidate Narrative Groups

Content Metadata

Repository

Content Repository

NarrativeRepository

Personalized Course Model

(XML)

Personalized Course Content

Adaptive

Engine

Learner InputLearner Modeler

Narrative Metadata

Repository


XS

LTT

rans

form

Tailored LayoutModel


Ad

ap

tive

Serv

ice

AEAdaptedOutput

Strategy

Metadata

Ad

ap

tive

Serv

ice

AEAdaptedOutput

Strategy

Metadata

AdaptiveEngine

AdaptedOutput

Strategy

Ad

ap

tive

Serv

ice

AEAdaptedOutput

Strategy

Metadata

Multiple Adaptive Services(APeLS II)

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


Thank You!


[email protected] and Data Engineering

Group (KDEG)

Trinity College, Dublin

http://kdeg.cs.tcd.ie

www.iclass.info

www.m-zones.org

the multi-model, metadata-driven approach to content and layout adaptation [email protected]...

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