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e-Learning Technologies on the Semantic Web

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1 e-Learning Technologies on the Semantic Web
31-Mar-17 [Title of the course] 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 , Jeddah – KSA Copyright © NameOfTheOrganization. All rights reserved.

2 2. What does Semantic Web bring 3. How it all fits together?
31-Mar-17 [Title of the course] Outline 1. Motivation Historical view, Problems, Goals & Vision 2. What does Semantic Web bring to e-learning? Semantic Markup: LOM, RDF, Ontology Rule-Markup: Rule-ML, SWRL, Web Services, Web Agents 3. How it all fits together? Case Study: Context-Aware M-Learning Use the Course summary slide to recap the most important points of the course. Keep the points short and easy to remember. Summarize the content in short sentences or phrases. If a summary item is more than a few lines long, left align its text. Compare your Course summary slide to your Course objectives slide. The summary should not just restate the objectives, but should contain the key points necessary to accomplish those objectives. If you have more than five or six important points, consider breaking the course into multiple courses or reducing the coverage of the course. 4. Conclusions Demos Copyright © NameOfTheOrganization. All rights reserved.

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 3

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

5 e-Learning History Traditional Learning Multimedia-Based Learning

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

7 Early Web-Based Learning
31-Mar-17 [Title of the course] e-Learning History (Cont’d) Learning Management Systems: WebCT, Blackboard, Domino, etc. (markup and XML) Educator-driven Linear access Time- and location-dependent Learning aimed at mass participation Mark-up + XML WWW Early Web-Based Learning Web-based e-Learning Systems Copyright © NameOfTheOrganization. All rights reserved.

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

9 Learning Objects based Systems
Vision: e-Learning on the Semantic-Web Confluence of enabling technologies: Web Agents, Ubiquitous Computing, Ontologies, Web Services, and Open Standards Scalable Service Oriented Systems Share Reasoning Discover WSDL-SOAP Web Services Agents Ontologies OWL-SWRL Semantic Web Internet 2 Interoperability Reuse Learning Objects based Systems Adapt to Context Semantic Web-based e/m/u-Learning Systems

10 Confluence of Enabling Technologies
Ontologies E-learining on Semantic Web Agents Web Services Shared conceptualizations Explicite Knowledge spec. Semantic Annotation Contextualization Community creation Proactivity Service composition Semantically well-defined context-aware dynamic services Interoperability Service discovery Service invocation Knowledge Management Reasoning Tools

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 learner’s needs, background, and learning style Contextualized Learning Adapted to learner’s 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 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 These are enabling approaches to learning [Ritrova 2005]

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 learner’s current context (activity, place, device)

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 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 What Semantic Web Brings to e-Learning

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 learning content on the Web
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 ADL’s 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 Why not use XML to represent ontologies?
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 <course Name=“...”> <Lectures>...</Lectures> <Exams> <MidTerm>...</MidTerm> <Final>...</Final> </Exams> <Projects>...</Projects> </course> Projects Lectures Exams This material is not covered in the book. 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 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

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 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 Person PhDStud Professor subClassOf type hasSuperVisor domain range hasSuperVisor York Rudi [Steffen Staab 2006]

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 RDFS provides some KR techniques, but is still not sufficient for applying real KR to semi-structured data on the web. how to apply KR - ontologies - to semi-structured data on the internet? only natural language spec. Only subsumption inference possible, no: transitivity, inverse, etc.

34 Limitations of the Expressive Power of RDF-S
31-Mar-17 Limitations of the Expressive Power of RDF-S [Title of the course] 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] Copyright © NameOfTheOrganization. All rights reserved.

35 Ontology Ontology in Philosophy Eventually, what is being?
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 can‘t share knowledge if they do not speak a common language.“ [Davenport & Prusak, 1998] Ontologies enable a better communication between Humans/Machines Ontologies standardize and formalize the meaning of words through concepts

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

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 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 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 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 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 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 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 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 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 Web Agents – Contribution of Semantic Web Technology (2)
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

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

50 Yesterday: Gadget Rules
Too bad they can’t talk to each other… Cool toys… [Harry Chen]

51 Today: Communication Rules
Configuration? Too much work… Sync. Download. Done. [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 [Dey and Abowd’s ]
“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 ] 54

55 Context-Awareness in e-Learning Research Challenges
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 can’t 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 Understanding & Usage Semantics/Understanding/Insight Expressiveness Context Information Context Perception Context Acquisition (World) Sensory Data 57

58 Context Awareness Pyramid Conceptual Model
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 58

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 user’s interaction the user’s 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
Data Pyramid Context Awareness Pyramid Context Understanding & Usage Semantics/Understanding/Insight Expressiveness Context Information Context Perception Context Acquisition (World) Sensory Data 62

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

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 66

67 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 67 67

68 System Architecture 68 68

69 Context Aggregation 69 69

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

71 Domain Ontology 71 71

72 Learner Ontology 72 72

73 HasWirelessNetworkType
Environment Ontology R Data Property D Object HasLocation HasBandwith HasWirelessNetwork Exte rnal XSD: Float External XSD: Date_Time Class WirelessNetwork Learner Environment Location Prope rty HasSur rround ingEnvironment SensedAt XS D: Boolean WirelessNetworkT y pe IsSecured HasWirelessNetworkType LocatedAt 73 73

74 Device Ontology 74 74

75 Activity Ontology 75 75

76 Subject Domain Ontology:
C++ Programming 76

77 Ontology Reasoning 77

78 System-Centric Adaptations
<<New Query>> 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 <<Login>> 78 78

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

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

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 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) 82

83 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) 83 83

84 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) 84

85 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) 85

86 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) 86

87 System Implementation
87 87

88 System-Centric Adaptations

89 Learner-Centric Adaptations

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 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.

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, 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, Steffen Staab, Ontologies and the Semantic Web, 2006, Harry Chen, Semantic Web in a Pervasive Context-Aware Architecture,

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