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E-Learning Technologies on the Semantic Web Rachid Benlamri Chair, Dept. of Software Engineering Lakehead University Canada For Contact

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Presentation on theme: "E-Learning Technologies on the Semantic Web Rachid Benlamri Chair, Dept. of Software Engineering Lakehead University Canada For Contact"— Presentation transcript:

1 e-Learning Technologies on the Semantic Web Rachid Benlamri Chair, Dept. of Software Engineering Lakehead University Canada For Contact King Abdulaziz University - Joint Supervision Program – Oct , 2009 Jeddah – KSA

2 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 4. Conclusions Demos 1. Motivation Historical view, Problems, Goals & Vision 1. Motivation Historical 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

3 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) 3

4 Slide 4 4 Part 1 Motivation Historical view Problems Goals & Vision Part 1 Motivation Historical view Problems Goals & Vision

5 Traditional Learning Multimedia-Based Learning e-Learning History

6 Early Web-Based Learning Multimedia-Based Learning e-Learning History (Contd) WWW Markup Early web: Simple web pages, Word, Excel, pdf files managed by simple databases of learning content (markup)

7 Early Web-Based Learning Web-based e-Learning Systems e-Learning History (Contd) WWW 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

8 Learning Object based Systems Web-based e-Learning Systems e-Learning History (Contd) Learning Object Systems: SCORM, ADL, IMI, OCPI, etc., (XML-based Metadata + Web Services + Standards ) XML – LOM -SWDL- SOAP Web Services Discover Share Reuse Interoperability Can access any LO Build learning paths out of LOs LO-based metadata for indexing and retrieval

9 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 Web Services Discover Share Reuse Agents OWL-SWRL Semantic Web Reasoning Adapt to Context … … … Ontologies Internet 2 Interoperability Scalable Service Oriented Systems

10 Confluence of Enabling Technologies Ontologies E-learining on Semantic 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

11 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 learners needs, background, and learning style Contextualized Learning –Adapted to learners situation (current activity, task at hand, and surronding environment)

12 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 –Users 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 learners needs and preferences [Ritrova 2005]

13 13 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 learners current context (activity, place, device)

14 14 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

15 15 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

16 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

17 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., 2001]

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

19 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

20 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

21 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

22 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 Standards such as IEEE LOM (Learning Object Metadata)

23 LO-based Learning Systems The SCORM Model Source from ADLs SCORM

24 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

25 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 Projects Lectures MidTerm Final Why not use XML to represent ontologies ?

26 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

27 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) site-owner Benlamri author

28 28 RDFs 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 URIs can refer to anything and not just digital resources ( e.g. lecturer, author, student, device, etc.) So, RDF, is extendable and doesnt require rigid meta- data structures or proprietary standards or fixed vocabularies

29 29 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

30 30 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)

31 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 PhDStudProfessor subClassOf type hasSuperVisor domain range type hasSuperVisor [Steffen Staab 2006]

32 32 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

33 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

34 34 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]

35 Ontology Ontologies enable a better communication between Humans/Machines Ontologies standardize and formalize the meaning of words through concepts Ontology in Philosophy Ontology 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 cant share knowledge if they do not speak a common language. [Davenport & Prusak, 1998]

36 Ontology is a formal Specification of a shared conceptualization of a domain of interest [Gruber 93] Formal Specification of Conceptualization … … … Concepts Domain of Interest of Reasoning + Processable Group of shared People Web agents Applications Ontology Services cooperation

37 37 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)

38 Example of Learner Ontology Standards: RDF(S); OWL (Web Ontology Language)

39 39 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

40 40 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) [G. Antoniou & F.Harmelen]

41 41 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]

42 42 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…

43 43 SW and Rules Markup Languages Rule-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

44 44 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

45 45 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 [G. Antoniou & F.Harmelen]

46 46 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

47 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

48 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)

49 Slide 49 Web 49 Part 3 How it all fits together? Case Study Context-Aware M-Learning On the Semantic Web Part 3 How it all fits together? Case Study Context-Aware M-Learning On the Semantic Web

50 Yesterday: Gadget Rules Cool toys… Too bad they cant talk to each other… [Harry Chen]

51 Today: Communication Rules Sync. Download. Done. Configuration? Too much work… [Harry Chen]

52 Tomorrow: Services Will Rule Thank God! Pervasive Computing is here. [Harry Chen]

53 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]

54 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 Abowds ]

55 Context-Awareness in e-Learning Research Challenges (1) Context Modeling & Reasoning for e- learning –How to build representations of context that can be processed, and reasoned about by the used devices (2) Knowledge Maintenance & Sharing –How to maintain consistent knowledge about the context and share that information with other learning services Possible Solution Use SW ingredients to solve (1) & (2)

56 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 cant be directly acquired from the sensors –Detect and resolve inconsistent knowledge that results from imperfect sensing –Infer new knowledge for service adaptation

57 Context Awareness Pyramid Context Acquisition (World) Context Perception Context Understanding & Usage Expressiveness Sensory Data Context Information Semantics/Understanding /Insight

58 Context Awareness Pyramid Conceptual Model 58 Raw-Context Data Context Sources Atomic Context Sensor Context Sensing Layer Context Reasoning and Service Adaptation Context Inference Context Learning Context Perception Layer Context Understanding and Usage Layer Context Computation Raw-Context Data

59 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 users interaction the users goal, tasks, work context, business processes, etc. –Retrieved from profiles of users, tasks, schedule, etc.

60 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, s, 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 ]

61 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)

62 Context Awareness Pyramid Context Acquisition (World) Context Perception Context Understanding & Usage Expressiveness Sensory Data Context Information Semantics/Understanding/Insight Data Pyramid

63 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

64 Context Awareness Pyramid Context Acquisition (World) Context Perception Context Understanding & Usage Expressiveness Sensory Data Context Information Service layer Semantics/Understanding/Insigh t Data Pyramid

65 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

66 Context Awareness in M-learning 66

67 Challenges & Contributions Challenges : Todays 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 67

68 68 System Architecture

69 69 Context Aggregation

70 Global Ontology Space 70 Learner Ontology Domain Ontology Device Ontology Activity Ontology Environment Ontology Concep t Learner HasCovered Query HasKeyword Environment HasSurrounding Environment Learning Resource Consumed Learning Resource Learning Activity ConductedLearningActivity Device UsedDevice IsMappedTo MakeQuery HasLearning Goal LocatedAt Location HasLocatiom

71 71 Domain Ontology

72 72 Learner Ontology

73 73 Environment Ontology

74 74 Device Ontology

75 75 Activity Ontology

76 76 Subject Domain Ontology: C++ Programming

77 77 Ontology Reasoning

78 > User Web Borrower HTML WAP Borrower WML Sensing Type of Network Connection & Maximum Connection Speed Sense & Update Actual Network Bandwidth Compute Maximum Resource Size Adapt Retrieved Resources to Device Features Infer Current Bandwidth Infer Media Type Global Ontology Space Device Repository Environment Repository System Centric Adaptation > 78 System-Centric Adaptations

79 Inferring Current Bandwidth Infer Current Bandwidth based on Previously sensed bandwidth Fuzzy Membership Function SWRL Rules to Compute Truth Values 79

80 Resource-Size & Media-Type Selection Resource Size & Media Type Rules Resource Size Computation 80 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

81 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-1 ActivityID(?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) 81

82 Other System-Centric Adaptations Selecting Resources based on Device Features Language Rule O/S-Compatibility Rule 82 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)

83 Learner-Centric Adaptations Objective: Generate a learning path tailored to the learners needs, background, and task at hand. 83 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 historyInfer covered resources 1 - Infer covered concepts (HasCovered) 2 – Infer consumed learning resources (HasConsumed)

84 Current Task Adaptation Inference of Similar Learning Knowledge Inference of Prerequisite Learning Knowledge 84 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)

85 Learner Needs Adaptation Inference of Core Learning Knowledge Inference of Related (non-core) Learning Knowledge 85 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)

86 Example of Learner-Centric Adaptations Assumptions: - Irenes 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) 86

87 87 System Implementation

88 88 System-Centric Adaptations

89 89 Learner-Centric Adaptations

90 90 Learner-Centric Adaptations

91 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 learners needs, background, and environment –search engines can look for resources that refer to a precise learners 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.

92 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

93 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

94 Thank you Questions?

95 Acknowledgements & References RDF Primer, OWL, Web Services, G. Antoniou & F. Harmelen, A Semantic Web Primer, 2nd Edition (Cooperative Information Systems), MIT Press, 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, Steffen Staab, Ontologies and the Semantic Web, 2006, 2006/103.htm 2006/103.htm Harry Chen, Semantic Web in a Pervasive Context-Aware Architecture,

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