e-learning technologies on the semantic web rachid benlamri chair, dept. of software engineering...

e-Learning Technologies on the

Semantic Web

Rachid BenlamriChair, Dept. of Software Engineering

Lakehead UniversityCanada

For Contact Email: rbenlamr@lakeheadu.ca King Abdulaziz University - Joint Supervision Program – Oct. 24-29, 2009 Jeddah – KSA

3. How it all fits together?Case Study: Context-Aware

M-Learning

3. How it all fits together?Case Study: Context-Aware

M-Learning

4. Conclusions Demos

1. MotivationHistorical view,

Problems, Goals & Vision

1. MotivationHistorical view,

Problems, Goals & Vision

Outline

2. What does Semantic Web bring to e-learning?

Semantic Markup: LOM, RDF, Ontology Rule-Markup: Rule-ML, SWRL,

Web Services, Web Agents

2. What does Semantic Web bring to e-learning?

Semantic Markup: LOM, RDF, Ontology Rule-Markup: Rule-ML, SWRL,

Web Services, Web Agents

Objectives

This presentation is intended to provide you with– research motivations in Semantic Web-based

technologies– an understanding of the basic requirements of

service-oriented learning systems– an understanding of what Semantic Web can bring to

e-learning – methods for context modeling and management for

e-learning – a system prototype for context-aware mobile-learning– a system demo for collaborative/social learning

(Unite Software)

Part 1

Motivation

Historical view Problems

Goals & Vision

Part 1

Motivation

Historical view Problems

Goals & Vision

Traditional Learning

Multimedia-Based Learning

e-Learning History

Early Web-Based Learning

Multimedia-Based Learning

e-Learning History (Cont’d)

Markup

Early web: Simple web pages, Word, Excel, pdf files managed by simple databases of learning

content (markup)

Early Web-Based Learning

Web-based e-Learning Systems

Learning Management Systems: WebCT, Blackboard, Domino, etc. (markup and XML)

Mark-up + XML

Time- and location-dependent Learning aimed at mass participation

Educator-driven Linear access

Learning Object based Systems

Web-based e-Learning Systems

Learning Object Systems: SCORM, ADL, IMI, OCPI, etc., (XML-based Metadata + Web Services + Standards )

XML – LOM -SWDL- SOAP

Sequence Link

Correlation Link

LO LO LO

Mandatory Learning Object LO

Secondary Learning Object

LO LO LO LO LO LO LO

Web Services

Discover Share

Reuse Interoperability

Can access any LOBuild learning paths out of LOs

LO-based metadata for indexing and retrieval

Learning Objects based Systems

Semantic Web-based e/m/u-Learning Systems

Vision: e-Learning on the Semantic-Web

Confluence of enabling technologies: Web Agents, Ubiquitous Computing, Ontologies, Web Services, and Open Standards

WSDL-SOAP

Sequence Link

Correlation Link

LO LO LO

Mandatory Learning Object LO

Secondary Learning Object

LO LO LO LO LO LO LO

Web Services

Discover Share

Agents

OWL-SWRL

Semantic Web

Reasoning

Adapt to Context

……

Ontologies

Internet 2

Interoperability

Scalable Service Oriented Systems

Confluence of Enabling TechnologiesOntologies

E-learining onSemantic Web

Agents

Web Services

Knowledge Management

Reasoning Tools

Interoperability Service discovery Service invocation

Shared conceptualizations Explicite Knowledge spec. Semantic Annotation

Contextualization Community creation Proactivity Service composition

Semantically well-defined context-aware dynamic services

Information-Transfer

based Learning

• Information transfer– Online repositories

– Teachers produce and learners consume

• Passive learners – Learner cannot impact the learning process

• No Personalization– Uniform Learning

– All learners learn same way

• No contextualization – System is unaware of learner situation

Knowledge-Transfer

based Learning

• Knowledge transfer– Knowledge-driven framework

– Semantically annotated content– Sharing, reusing, and reasoning

• Active learners – Learner has impact on the learning

process

• Personalized Learning– Tailored to the learner’s needs,

background, and learning style

• Contextualized Learning– Adapted to learner’s situation (current

activity, task at hand, and surronding environment)

Learning Models: How we Conceive the Learning

• Contextualised learning– Understanding of concepts through direct experience of their

manifestation in realistic contexts (e.g. providing access to real world data)

• Social learning– User’s mental processes are influenced by social and cultural

contexts• Collaborative learning

– More than a simple information exchange – peers interactions, conversation tracks, knowledge reconstruction

• Personalised learning– Guarantee the learner to reach a cognitive excellence through

different learning path tailored to learner’s needs and preferences

[Ritrova 2005]

Requirements of future e-Learning Systems

Emerging web technologies provide new exciting horizons for building smart distributed e-learning environments

• Semantic markup and reasoning– Learning resources from different sources can be linked to

commonly agreed ontologies– Powerful semantic querying to build personalized learning paths– Open standards for resource sharing and reuse

• Service orientation– Learning is conceived as support services – The learning process is enabled by composing services

• Context-awareness – Ability to recognize learner’s current context (activity, place, device)

Requirements of future e-Learning Systems(2)

• Learner-centric– Dynamically optimized learning, on demand, and on a learner's

schedule and context. – Appropriate semantic annotation will still define pedagogical

sequences in the learning hierarchy (core, prerequisites, electives, etc...)

• Community and collaboration based– Is central for all kinds of formal and informal learning – Autonomous and dynamic creation of communities

• Dynamicity– the learner can influence the process– the social and context aspects influence the learner

Requirements of future e-Learning Systems(3)

• High demand of interoperability– access to resources on heterogeneous environments

• Security and trust– Confidentiality, privacy, copyright issue, etc...

• Simplify– Learning content authoring– Application Development– Metadata exchange

What Semantic Web Brings to e-Learning

Part 2

What does Semantic Web bring to e-Learning?

Learning Object Metadata (LOM)

Semantic Markup (RDF, RDF-S, OWL, OWL-S)

Rule Markup Languages (Rule-ML and SWRL)

Web Services & Web Agents

Part 2

What does Semantic Web bring to e-Learning?

Learning Object Metadata (LOM)

Semantic Markup (RDF, RDF-S, OWL, OWL-S)

Rule Markup Languages (Rule-ML and SWRL)

Web Services & Web Agents

Semantic Web - Definition

The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in co-operation.

[Berners-Lee et al.,

Semantic Web Layers(T. Berners-Lee et al.)

First Step Towards SW Learning:Augmenting Learning Objects with Metadata

Learning Object : A re-usable chunk of information used as a modular building block for e-Learning content.

Metadata (Data about data)Structural data about Learning Objects (LOs), to augment them with a meaningful classification so that they can be easily reused, transformed, accessed, etc., in order to gain more information on demand.

IEEE LTSC LOM (Learning Object Metadata)

XML-based Metadata

Role of Metadata• SW-techniques allow you to add metadata to distributed resources just like

html allows you to link to such resources.

• Metadata allows Learning Management Systems (LMS) to:– Annotate– Find– Select– Retrieve– combine– use/re-use, and – share

learning content on the Web

• Metadata is not bound to a fixed schema. You may invent a description format of your own and add personal annotation

Sample of Metadata

• The display type of a device• The topic of a of a lecture• The size of a learning resource• The author of a learning resource• The operating system to execute a

program

Metadata Standards

• Often, standards go through three stages:– Technical Specifications

such as AICC, IMS, ARIADNE– Implementation Reference Models

such as ADL SCORM (sharable content object reference model)

– Accredited Standardssuch as IEEE LOM (Learning Object Metadata)

LO-based Learning SystemsThe SCORM Model

Source from ADL’s SCORM

Is Metadata Enough for Sharing Resources on the SW?

• No metadata standard covers every aspect of life

• They just provide information about the packaged learning content

• Different communities develop different vocabularies (ontologies), but they should be able to use each others

• XML is not good at describing vocabulary • There is a need for a language with semantic

markup to describe shared vocabularies

XML• XML documents are labeled trees • Storage is done just like an n-ary tree (DOM)• Tree element = label + Attribute/Value + content• Document Type definition (DTD): Simple grammar (regular

expressions) to describe legal trees (XML-Schema )• It says what elements and attributes are required or optional.

course

Exams ProjectsLectures

MidTerm Final

</Exams> <Projects>...</Projects> </course>

Why not use XML to represent ontologies?

XML: Limitations for Semantic Markup

• XML is a universal metalanguage for defining markup• It provides a uniform framework for interchange of data and

metadata between applications• However, XML is unspecific:

- No predetermined vocabulary

- No semantics for relationships

• XML does not provide explicit semantics (meaning) of data– E.g., there is no intended meaning associated with the nesting of

tags– It is up to each application to interpret the nesting.

• XML is not for sharable Web-resources on a broad scale

• So, there is a need for other form of semantic markup

Resource Description Framework (RDF)

for Semantic Markup • RDF provides metadata about Web resources• Basic building block:

Subject -> Predicate -> Object triples– subject is the focus of the statement– predicate describes a property of the subject– property value is the object.

• So, RDF keeps meta-data external to objects• It has an XML syntax• Chained triples form a graph (semantic net)

http://flash.lakeheadu.ca/~rbenlamr

site-owner

Benlamri http://flash.lakeheadu.ca/pres.pdfauthor

RDF’s Resources

• Every resource has a URI, a Universal Resource Identifier

• A URI can be – a URL or – unique identifier

• We can think of a resource as an object, a “thing”. So, RDF URI’s can refer to anything and not just digital resources (e.g. lecturer, author, student, device, etc.)

• So, RDF, is extendable and doesn’t require rigid meta-data structures or proprietary standards or fixed vocabularies

Properties• Properties describe relations between

resources– e.g. “author”, “prerequisite”, etc.

• Properties are also identified by URIs (kind of resources)

• Advantages of using URIs:– Α worldwide, unique naming scheme– Reduces the problem of distributed data

representation

Statements

• Statements assert the properties of resources• A statement is an subject-predicate-object

– It consists of a resource, a property, and a value

• Values can be resources or literals – Literals are atomic values (strings)

• The triple (x,P,y) can be considered as a logical formula P(x,y)– Binary predicate P relates object X to object Y – RDF offers only binary predicates (properties)

What does RDF Schema add?

• Defines vocabulary for RDF• Organizes this vocabulary in a

typed hierarchy• Class, subClassOf, type• Property, subPropertyOf• domain, range

RudiYork

Person

PhDStud Professor

subClassOfsubClassOf

hasSuperVisordomain range

hasSuperVisor

[Steffen Staab 2006]

Basic Ideas of RDF Schema

• RDF is a universal language that lets users describe resources in their own vocabularies – RDF does not assume, nor does it define

semantics of any particular application domain

• The user can do so in RDF Schema using:– Classes and Properties– Class Hierarchies and Inheritance– Property Hierarchies

RDF(S) Contributions to Semantic Markup

• RDF provides a foundation for representing and processing metadata

• RDF has an XML-based syntax to support syntactic interoperability.

• Next step up from plain XML:– (small) ontological commitment to modeling

primitives– possible to define vocabulary

• So, RDF & RDFS allow incremental building of knowledge, and its sharing and reuse

• However, many desirable modelling primitives are missing – no precisely described meaning– no inference model

Limitations of the Expressive Power of RDF-S

• Example: Boolean combinations of classes– Sometimes we wish to build new classes by

combining other classes using union, intersection, and complement ( e.g. person is the disjoint union of the classes male and female)

• Therefore we need an ontology layer on top of RDF and RDF Schema to offer:– explicit, formal conceptualizations of domain models– efficient reasoning support – a formal semantics – sufficient expressive power

[G. Antoniou & F.Harmelen]

Ontology

Ontologies enable a better communication between Humans/Machines

Ontologies standardize and formalize the meaning of words through concepts

Ontology in PhilosophyOntology is a branch of philosophy that deals with the nature and the organization of reality

Ontology deals with questions such as:

What characterizes being?Eventually, what is being?

“ People can‘t share knowledge if they do not speak a common language.“

[Davenport & Prusak, 1998]

Ontology is a formal Specification of a shared conceptualization of a domain of interest [Gruber 93]

Formal Specification of

Conceptualization

Concepts

Domain of

Interest

Reasoning+

Processable

Group of

sharedPeople

Web agentsApplications

Ontology

Services

cooperation

Ontologies for e-Learning

• In e-learning we distinguish between two types of knowledge (ontologies): – Operational Knowledge

• Learner ontology• Activity ontology• Environment ontology( device + environment)

– Domain Knowledge • Content • Pedagogy• Structure (navigation)

Example of Learner Ontology

Standards: RDF(S); OWL (Web Ontology Language)

Object Property

ConductedLearningActivity

Object Property

HasSurroundingEnvironment

Data Property

HasUserName

HasCovered

Learner

Learning Activity

External

XSD: String Class

Language

Learning Resource

Environment

Concept Object Property

ConsumedLearningResource

Object Property

Data Property

HasPassword R

HasLearningGoal

Object Property

Data Property

HasLearningTime

External

XSD: Date_Time

PreferredLanguage

Object Property

Location

LocatedIn

Object Property

Loops in C++

Learning Resource InstanceOf

Property

InstanceOf

Property

English

Learning

Resource

Reasoning about Knowledge in Ontology

• Class membership – If x is an instance of a class A, and A is a subclass of

B, then we can infer that x is an instance of B

• Equivalence of classes – If class A is equivalent to class B, and class B is

equivalent to class C, then A is equivalent to C, too• Consistency

– X instance of classes A and B, but A and B are disjoint– This is an indication of an error in the ontology

Reasoning about Knowledge in Ontology (2)

• Reasoning support is important for– checking the consistency of the ontology and the knowledge– checking for unintended relationships between classes– automatically classifying instances in classes

• Checks like the preceding ones are valuable for – designing large ontologies, where multiple authors are involved– integrating and sharing ontologies from various sources– Dealing with operational knowledge and domain knowledge in

an efficient way (i.e. context-aware personalized learning)

Web Ontology Language (OWL)

• OWL is a knowledge representation language to model ontologies and reason about their embedded knowledge

• OWL is based on formal semantics• Semantics is a prerequisite for reasoning support• Semantics and reasoning support are usually provided

by – mapping an ontology language to a known logical formalism– using automated reasoners that already exist for those

formalisms• OWL is (partially) mapped on a description logic, and

makes use of reasoners • Description logics are a subset of predicate logic for

which efficient reasoning support is possible[G. Antoniou & F.Harmelen]

OWL Standard

• Three OWL versions with different expressive power and designed for different requirements:– OWL FULL (fully expressive, not fully decidable)– OWL DL (less expressive, Description logic )– OWL Lite (restricted power of expressiveness)

• OWL is a knowledge representation language with rich modeling primitives: – Classes with data & object properties– Inverse and equivalence properties– Property and cardinality restrictions– Boolean combinations– Enumerations, etc…

SW and Rules Markup LanguagesRule-ML & SWRL

• The Semantic Web approach is to express the knowledge in a machine-accessible way using one of the Web languages (RDF/S, OWL, etc.)

• Then, extend these knowledge representation languages with rule markup languages, also expressed in XML-like syntax

• Basic Idea: shared reasoning is enabled through exchange of rules across different applications

Web Services – Contribution of Semantic Web Technology

• Web Service: service based, aiming to provide interoperability among distributed loosely coupled components

• Use machine-interpretable descriptions of services to automate:• discovery, invocation, composition and monitoring of

Web services• Share web services across applications (e.g. use

of Web Service Description Language - WSDL)• Web agents can compose simple web services

into complex web services

Web Services and OWL-S

• Applications should be able to employ a set of basic classes and properties by declaring and describing services: ontology of services

• OWL-Schema can be used for developing an ontology language for Web services

• It can be viewed as a layer on top of OWL

Three Basic Kinds of Knowledge Associated with a Service

• Service profile– Description of the offerings and requirements

of a service– Important for service discovery

• Service model– Description of how a service works

• Service grounding– communication protocol and port numbers to

be used in contacting the service

Web Agents – Contribution of Semantic Web Technology

• Software agents may represent dynamic entities in a Learning Management System

• Shared ontology(es) are used for mutual understanding of terms and embedded knowledge

• LMS Facts w.r.t. learners, learning process, environment, etc., and subsequent LMS decisions are represented by statements

• Information is exchanged between Agents in a Markup language

• Agent negotiation strategies are described in a logical language

• Agents decide about next course of action through inference, based on negotiation strategy and current facts

Web Agents – Contribution of Semantic Web Technology (2)

Part 3

How it all fits together?

Case Study

Context-Aware M-LearningOn the Semantic Web

Part 3

How it all fits together?

Case Study

Context-Aware M-LearningOn the Semantic Web

Yesterday: Gadget Rules

Cool toys…

Too bad they can’t talk to each other…

[Harry Chen]

Today: Communication Rules

Sync. Download

. Done.

Configuration? Too much

work…

[Harry Chen]

Tomorrow: Services Will Rule

Thank God! Pervasive

Computing is here.

[Harry Chen]

Context-Aware Systems

• “Systems that can anticipate the needs of users and act in advance by “understanding” their context.” [Chen 2003]

• “Systems that are able to adapt their operations to the current context without explicit user intervention. Context –awareness here aims at increasing usability and effectiveness by taking environmental context into account.”

[Dustdar 2006]

Context - Definition

“Context is any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves.”

[Dey and Abowd’s ]

Ontology–based Context Models• Need for knowledge sharing

– Knowledge sharing => Global Ontology spaces to enhance cross-domain understanding

• Use OWL as a meta-language to define other languages that are used in context-aware systems– Policy languages for privacy and security– Content languages for agent communications

• Use the ontology semantics of OWL+ SWRL (SW Rule Language) to reason about context– Deduce context knowledge that can’t be directly

acquired from the sensors– Detect and resolve inconsistent knowledge that

results from imperfect sensing – Infer new knowledge for service adaptation

Context Awareness Pyramid

Context Acquisition (World)

Context Perception

Context Understandin

g & Usage

ssiven

Sensory Data

Context Information

Semantics/Understanding/Insight

Context Awareness Pyramid Conceptual Model

Raw-Context DataContext Sources

Atomic Context Sensor

Context Sensing Layer

Context Reasoning and Service Adaptation

Context Inference Context LearningContext Perception Layer

Context Understanding and Usage Layer

Context Computation

Raw-Context Data

What is Atomic Context ?

• External (Sensed)– Context that can be measured by hardware sensors

• location, light, sound, temperature, air pressure, blood pressure, heart rate, etc.

• Internal (Retrieved from UI / Profiled )– Mostly specified by the user or captured monitoring

the user’s interaction• the user’s goal, tasks, work context, business

processes, etc.– Retrieved from profiles of users, tasks, schedule,

Sensing Atomic Context• Sensors

– Physical sensors• sensor, camera, microphone, accelerometer, GPS,

thermometer, biosensors– Virtual sensors

• From software: browsing an electronic calendar, a travel booking system, emails, mouse movements, keyboard input

– Logical sensors• Combination of physical and virtual sensors with additional

information from databases: analyzing logins, mapping fixed devices to location information, etc.

• Sensory Data Capture– Drivers and APIs– Query functionality (ex: getPosition())– Etc.

[Dustdar 2006]

Modeling Atomic Context:Context Atom Attributes

– Context type (Nature of context)

– Context value (Quantized / non quantized( boolean, literal) )

– Description (Symbolic description for high level reasoning)

– Time stamp (at acquisition time)

– Source (Sensor ID)

– Confidence (Truth probability)

Context Perception

g & Usage

ssiven

Sensory Data

Context Information

Data Pyramid

Context Perception– Involves monitoring and simple recognition– Links sensors to network and network-centric

services. – Enabled through:

• Reasoning and interpreting• Computation

– Extraction and quantization operations– Aggregation or compositing of atomic context

elements• Learning techniques

– Statistical methods where training phase is required

– To deal with ambiguous context

Context Perception

g & Usage

ssiven

Sensory Data

Context Information

Service layer

Data Pyramid

Context Understanding and Usage

• Semantic Analysis of Context Involves: – Reasoning: support of axiomatic rules (e.g.

expressed in SWRL)– Interpretation– Recognition, and – Evaluation

• Goal: – Awareness of the likely evolution of a

situation, – Identify its possible/probable future states and

events– Service adaptation to deal with the new

situation

Context Awareness in M-learning

Challenges & Contributions• Challenges : Today’s Learning Technology is characterised by:

Passive components rather than active components Separate use of contextual information on users, environment, and

services Lack of techniques for modeling and reasoning with the global

context

• Contributions Proactivity: context sensing, prediction, & management Knowledge Representation: Ontological approach for context

integration and aggregation Knowledge Management: Hybrid approach that may combine

probabilistic, logic, and rule based reasoning

System Architecture

Context

Sensing

Acquisition

Ontology Reasoning

System

Centric

Adaptations

Learner

Centric

Adaptations

Ontology Management

Global Ontology Space

LO Repository 1

Learner Profile Repository

Activity Repository

Device Repository

Environment Repository

LO Repository N

……

LO Repository 2

Run Time Environment Surrounding

Environment

Nomadic

Learners

Service

Discovery

A ge n t

Activity Context Learner Context Extends

Leaner-Centric Context

Device Context Extends

System-Centric Context

Context Integration & Aggregation

Personalization

Service Adaptation

Execution Profile

Environment Context

Context Aggregation

Learner Ontology

Domain Ontology

Device Ontology

Activity Ontology

Environment Ontology

Concept

LearnerHasCovered

HasKeyword

Environment

Learning Resource

Learning Activity

Device

UsedDeviceIsMappedTo

MakeQuery

HasLearningGoal

LocatedAt

Location

HasLocatiom

Domain Ontology

R D D D

Object Property

HasKeyword

Object Property

HasCovered

Object Property

IsMappedTo

Object Property

HasPrerequisite

Object Property

HasPart

Object Property

HasNecessaryPart

Concept

Learning Resource Class

Learner

Object Property

ExpressedIn

Language

Object Property

HasType Class

Media Type D

Object Property

HasLearningGoal

External

XSD: Time Data Property

LearningTime

Object Property

Data Property

HasUserName

HasCovered

Learner

Learning Activity

External

XSD: String Class

Language

Learning Resource

Environment

Concept Object Property

Object Property

Data Property

HasPassword R

HasLearningGoal

Object Property

Data Property

HasLearningTime

External

XSD: Date_Time

PreferredLanguage

Object Property

Location

LocatedIn

Object Property

Learner Ontology

Environment Ontology

Data Property

Object Property

HasLocation

Data Property

HasBandwith

HasWirelessNetwork

External

XSD: Float

External

XSD: Date_Time

WirelessNetwork

Object Property

Learner

Environment

Location

Object Property

HasSurrroundingEnvironment

SensedAt

External

XSD: Boolean Class

WirelessNetworkT ype

Data Property

IsSecured Object Property

HasWirelessNetworkType

Object Property

LocatedAt D R

D D Object Property

HasHardwareProfile

Object Property

HasDisplayType

Data Property

HasMaxBandwidth

Object Property

HasSofwareProfile Class

Device

Learning Activity

Hardware Profile

External

XSD: Float

Display Type

Software Profile

Object Property

HasNetworkAdaptor

UsedDevice

External

XSD: Integer

Object Property

HasDeviceType

Device Type

Object Property

HasKeyboardType

Keyboard Type

Data Property

AvailableMemory

External

XSD: Integer

Network Adaptor

Data Property

HasScreenLength

Network Protocol

Object Property

HasNetworkProtocol

S/W Application

Object Property

RunApplication

Language

Object Property

SupportLanguage

Media Type

OS Type

Object Property

SupportMediaType

Data Property

HasScreenWidth

Device Ontology

Object Property

Data Property

Object Property

UsedDevice

Begin-time Data Property

HasActivityId

Object Property

MakeQuery

Learning Activity

External

XSD: Date_Time

External

XSD: Integer Class

Learner

Concept

Device

Object Property

HasKeyword

Data Property

End-time

Activity Ontology

Subject Domain Ontology:C++ Programming

1: Login

successful?

2: Formulate

3: Retrieve subject –

domain ontology

4: Infer query related

concepts

5: Infer metadata using

perceived

system-centric context

6: Discover & filter out

learning resources using

metadata generated in step

(4) and (5)

7: Infer learner’s expertise in

subject-domain and use it to

generate learning sequence

8: Generate

personalized learning

9: Update

learning expertise

10 Terminates

learning session

Yes No

<< New interaction>>

<<Logout>>

Ontology Reasoning

<<New Query>>

Web BorrowerHTML

WAP BorrowerWML

Sensing Type of Network Connection & Maximum Connection

Sense & Update Actual Network

Bandwidth

Compute Maximum

Resource Size

Adapt Retrieved Resources to

Device Features

Infer Current Bandwidth

Infer Media Type

Device Repository

Environment Repository

System Centric Adaptation

<<Login>>

System-Centric Adaptations

Inferring Current Bandwidth• Infer Current Bandwidth based on

Previously sensed bandwidth Fuzzy Membership Function SWRL Rules to Compute Truth Values

Lowband Mediumband Highband B* Maxband

μA(B)

Medium High

Fuzzifier Fuzzy Inference Engine Defuzzifer

Fuzzy Rule Base

Resource-Size & Media-Type Selection

• Resource Size & Media Type Rules

• Resource Size Computation

)()()(

)(*arg)(*)(**

BeSizeLBMediumSizeBSmallSize

eLMediumLow

Knowledge base for the fuzzy logic system:

• if bandwidth is Low then set file size to Small.• if bandwidth is Medium then set file size to Medium• if bandwidth is High then no file size restriction

SmallSize MediumSize MaxSize LargeSize

μA(Z)

Medium Large

SWRL Rules for Fuzzy Logic Reasoning

MediaTypeSelectionRule-1 ActivityID(?a)∧UsedDevice(?a,?y)∧NetworkBandwidth(?y,"Low") → HasMediaType(?y, Text)

MediaTypeSelectionRule-2 ActivityID(?a)∧UsedDevice(?a, ?y)∧NetworkBandwidth(?y, "Medium")→HasMediaType(?y, Text)∧HasMediaType(?y, Image)

MediaTypeSelectionRule-3 ActivityID(?a)∧UsedDevice(?a, ?y)∧NetworkBandwidth(?y, "High") →HasMediaType(?y,Text)∧ HasMediaType(?y,Image)∧ HasMediaType(?y,Video)

ResourceSizeRule-1ActivityID(?a)∧UsedDevice(?a, ?y)∧ProbLow(?y, ?Tl)∧ProbMedium(?y, ?Tm)∧ProbHigh(?y, ?Th)∧ MaxSize(?y, ?Maxsize) ∧swrlb:multiply(?Lowsize, 0.25, ?Maxsize)∧swrlb:multiply(?Mediumsize, 0.5, ?Maxsize)∧swrlb:multiply(?Largesize, 0.75, ?Maxsize)∧swrlb:multiply(?l, ?Lowsize, ?Tl)∧swrlb:multiply(?m, ?Mediumsize, ?Tm)∧swrlb:multiply(?h, ?Largesize,?Th)∧ swrlb:add(?z1,?l,?m, ?h)∧swrlb:add(?z2, ?Tl, ?Tm, ?Th)∧swrlb:equal(?z2, 1)∧swrlb:divide(?z, ?z1, ?z2) → FileSize(?y, ?z)

AllowedResourceSizeRule-1 ActivityID(?a)∧UsedDevice(?a, ?y)∧FileSize(?y, ?Size)∧ AvailableMemory(?y,?MemorySize)∧ swrlb:lessThan(?MemorySize,?Size) →AllowedSize(?y,?MemorySize)

AllowedResourceSizeRule-2 ActivityID(?a)∧UsedDevice(?a,?y)∧FileSize(?y,?Size)∧AvailableMemory(?y, ?MemorySize)∧ swrlb:greaterThanOrEqual(?MemorySize, ?Size)→AllowedSize(?y,?Size)

Other System-Centric Adaptations

• Selecting Resources based on Device Features Language Rule

O/S-Compatibility Rule

LanguageRule-1

ActivityID(?a)∧UsedDevice(?a,?y)∧PreferredLanguage(?a, ?z)∧ HasSupportLanguage(?y,?z) → SearchLanguages(?a, ?z)

SystemCentricRule-1

SearchLanguages(?y,?z)∧ExpressedIn(?LR,?z)∧ UsedDevice(?y, ?D)∧ HasMediaType(?D,?b) ∧HapType(?LR,?b)∧HasOS(?D,?c)∧ RunsOn(?LR,?c) → SystemCentric(?y, ?LR)

Learner-Centric Adaptations

Objective: Generate a learning path tailored to the learner’s needs, background, and task at hand.

Type of Adaptation Adaptation Process

1. Current task

1 - Get similar knowledge (Isa)

2 – Check for prerequisite knowledge (HasPrerequisite)

2. Learner needs

1- Get core knowledge (HasNecessaryPartOf)

2- If time permits then get related knowledge (HasPartOf)

3. Learning history Infer covered resources 1 - Infer covered concepts (HasCovered)

2 – Infer consumed learning resources (HasConsumed)

Current Task Adaptation• Inference of Similar Learning Knowledge

• Inference of Prerequisite Learning Knowledge

SimilarLearningResourceRule-1: ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C)IsMappedTo(?C,?LR) → SimilarLR(?a,?LR)

SimilarLearningResourceRule-2: ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C)Has(?C,?Ci) ¬Covered(?L,?Ci) IsMappedTo(?Ci,?LRi)¬Consumed(?L,?LRi) → SimilarLR(?a,?LRi)

SimilarLearningResourceRule-3: ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C) Isa(?C,?Ci)¬ Covered(?L,?Ci)IsMappedTo(?Ci,?LRi) ¬Consumed(?L,?LRi) → SimilarLR(?a,?LRi)

PrerequisiteLearningResourceRule-1:ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C) HasPrerequisite(?Q,?Ci) ¬covered(?L,?Ci) IsMappedTo(?Ci,?LRi) ¬Consumed(?L,?LRi) → PrerequisiteLR(?a,?LRi)

Learner Needs Adaptation

• Inference of Core Learning Knowledge

• Inference of Related (non-core) Learning Knowledge

CoreLearningResourceRule-1 : ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q) HasKeyword(?Q,?C) HasNecessaryPart(?Q,?Ci) ¬Covered(?L,?Ci) IsMappedTo(?Ci,?LRi) ¬ Consumed(?L,?LRi) → CoreLR(?a,?LRi)

CoreLearningResourceRule-2 : ConductedLearningActivity(?L, ?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C)IsNecessaryPartOf(?Q,?Ci) ¬Covered(?L,?Ci)IsMappedTo(?Ci,?LRi) ¬Consumed(?L, ?LRi) → CoreLR(?a,?LRi)

NonCoreRelatedLearningResourceRule-1: ConductedLearningActivity(?L,?a)MakeQuery(?a,?Q)HasKeyword(?Q,?C) HasPart(?Q,?Ci) ¬Covered(?L,?Ci) IsMappedTo(?Ci,?LRi) ¬Consumed(?L,?LRi) → NonCoreRelatedLR(?a,?LRi)

NonCoreRelatedLearningResourceRule-2: ConductedLearningActivity(?L,?a) MakeQuery(?a,?Q) HasKeyword(?Q,?C) IsPartOf(?Q,?Ci) ¬Covered(?L,?Ci) IsMappedTo(?Ci,?LRi) ¬Consumed(?L,?LRi) → NonCoreRelatedLR(?a,?LRi)

Example of Learner-Centric Adaptations

Assumptions: - Irene’s query: C++ Loops - Covered Concept by Irene: (Do-While)

Recommended Learning Sequence: - Step 1: Learning Path (Ignoring Learning History) (C++ Loops) → (While) → (Do-While) → (For) → (Looping) - Step 2: Final Learning Path (C++ Loops) → (While) → (For) → (Looping)

Similar Concept

Learning Resource

LR6a LR6c

Non Core Concept

C36 C37

LR45c LR41a

C41 C445

LR45a LR1a

3: Read Ontology

1: Send Query

12: Return

8: Invoke Reasoning

2: Invoke

1: Send Query

4: Infer

13: Display Results (WML)

Web Borrower

WAP Borrower WML

Web Server

Apache-Tomcat-6.0.14

Web Application

Java Servlet

Ecl i pse SDK 3. 3. 1

Jena-2.5.4

OWL Ontology

Protégé 3. 4 Beta

Jess 7

Ontology Reasoning

SWRL-Jess Bridge Java API

Context

Atomic Context XML

9: Aggregate

Context

10: Infer Context

13: Display Results (HTML)

User5: Update Learning

Sequence

7: Save to

Buffer

6: Retrieve

Related LRs

LR-Repository

Learning Resource

Dom4j-1.6.1 Buffer Storage

Retrieved LRs

11: Retrieve

Matched LRs

System Implementation

System-Centric Adaptations

Learner-Centric Adaptations

Conclusions• Ontologies provide a shared understanding of a domain,

hence allowing semantic interoperability

• Ontologies are useful for improving the accuracy of searches for learning resources that are tailored to the learner’s needs, background, and environment – search engines can look for resources that refer to a precise

learner’s context

• SW provides a learning infrastructure where knowledge, organized in conceptual spaces (based on its meaning) can be semantically queried, discovered, and shared across applications.

Conclusions (2)• SW allows learning applications to share services

without much overhead. Learning services across applications can be integrated by resolving differences in terminology through mappings between ontologies

• SW allows expressing knowledge in well-understood formal semantics

• Automated reasoners can deduce (infer) conclusions from the given knowledge– Logic can be used to uncover ontological knowledge that is

implicitly given – It can also help uncover unexpected relationships and

inconsistencies– Logic can also be used by intelligent agents for making

decisions and selecting courses of learning action

Conclusions (3)

• SW provides Web agents with:– Agent communication languages– Formal representation of intentions (negotiation strategies)– Logic to reason based on current facts and negotiation

strategies

• Thus, e-learning systems will use Semantic Web-based Knowledge Systems as key parts of everyday learning cycles

Thank you

Questions?

Acknowledgements & References• RDF Primer , http://www.w3.org/TR/rdf-primer/• OWL, http://www.w3.org/2004/OWL/ • Web Services, http://www.w3.org/2002/ws/ • G. Antoniou & F. Harmelen, A Semantic Web Primer, 2nd Edition

(Cooperative Information Systems) , MIT Press, 2008.• Terence Love et al., The Future of e-Learning: Inclusive learning

objects using RDF.• Andreas Hotho & York Sure, A Short Semantic Web Tutorial,

Knowledge Management Group Institute AIFB, University of Karlsruhe

• Steffen Staab, Querying OWL Ontologies, 2007, http://www.uni-koblenz.de/~staab/Teaching/Tutorials/SSMS-2007/

• Steffen Staab, Ontologies and the Semantic Web, 2006, http://www.uni-koblenz.de/~staab/Teaching/Tutorials/SMBM-2006/103.htm

• Harry Chen, Semantic Web in a Pervasive Context-Aware Architecture, http://ebiquity.umbc.edu/get/a/publication/66.ppt

e-learning technologies on the semantic web rachid benlamri chair, dept. of software engineering...

learning objects

collaborativesocial

learning models

elearning technologies

elearning methods

oriented learning systems

reasoning learning resources

different learning path

Documents

epfl trigonakis babak falsafi, rachid guerraoui, javier...

littl butter - lakehead university

maria grazia viola - lakeheadu.ca · lakehead u n iv ers it...

teaching commons: lakehead university

rachid laajaj (paris school of economics - inra)

lakehead university viewbook 2015

rachid guerraoui – adversary oriented computing

rachid i slides

rachid nouicer - indico.bnl.gov€¦ · rachid nouicer 1...

customer service standard - lakehead university

rachid mada portfolio

provost’s report to senate 9.5... · 955 oliver road,...

pete hollings lakehead university

ch3p1and2 - lakehead university

rachid kanaan who is goldorach

co operative education - lakehead u

scanned document - lakehead university

observing the atmosphere - lakehead u

rachid 07.11.2012

under h.e. mr. rachid mohamed rachid