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Semantic web for e-Learning.ppt

VIEWS: 203 PAGES: 59

									Semantic Web for
e-Learning


    Setareh Momayez
Outline
   Introduction
   Differences between training and e-Learning
   Semantic Web & e-Learning
   Benefits of using Semantic Web as a technology for
    e-Learning
   Metadata & e-Learning
   Ontology & e-Learning
   Scenario
   Extending e-Learning Platforms to Incorporate
    Semantic Web Ontologies
   Conclusion
   Innovative idea

                       Semantic web for e-learning       2
Introduction
 It is clear that new styles of learning are some
  of the next challenges for every industry.
 Incredible velocity and volatility of today's
  markets require just-in-time methods for
  supporting the need-to-know of employees,
  partners and distribution paths.
 e-Learning aims at replacing old-fashioned
  time/place/content predetermined learning
  with a just-in-time/at-work-place/customized/
  on-demand process of learning. (Maurer&Sapper 2001)

                     Semantic web for e-learning    3
Semantic Web,
a promising technology
 One of its primary characteristics, viz. shared
  understanding based on the ontology
  backbone.
 Ontology enables the organization of learning
  materials around small pieces of semantically
  annotated (enriched) learning objects (Neidl
  2001).




                    Semantic web for e-learning     4
Differences between
training and e-Learning (Drucker 2000)




                  Semantic web for e-learning   5
Differences between
training and e-Learning (Drucker 2000)




                  Semantic web for e-learning   6
Semantic Web architecture
                             the XML layer, which
                              represents data
                             the RDF layer, which
                              represents the meaning of
                              data
                             the Ontology layer, which
                              represents the formal
                              common agreement about
                              meaning of data
                             the Logic layer, which
                              enables intelligent reasoning
                              with meaningful data


             Semantic web for e-learning                  7
Semantic Web & e-Learning
 Learning material is semantically annotated
  and for a new learning demand it may be
  easily combined in a new learning course.
 The process is based on semantic querying
  and navigation through learning materials,
  enabled by the ontological background.


                   Semantic web for e-learning   8
Benefits of using Semantic Web as a
technology for e-Learning (Delivery)
 E-Learning: Pull – Student determines agenda



 Semantic Web: Knowledge items (learning
  materials) are distributed on the web, but they are
  linked to commonly agreed ontologie(s). This enables
  construction of a user-specific course, by semantic
  querying for topics of interest.

                       Semantic web for e-learning      9
Benefits of using Semantic Web as a
technology for e-Learning (Responsiveness)
 E-Learning: Reactionary – Responds to problem
  at hand



 Semantic Web: Software agents on the
  Semantic Web may use commonly agreed service
  language, which enables co-ordination between
  agents and proactive delivery of learning materials in
  the context of actual problems. The vision is that
  each user has his own personalized agent that
  communicates with other agents.

                      Semantic web for e-learning      10
Benefits of using Semantic Web as a
technology for e-Learning (Access)
 E-Learning: Non-linear – Allows direct access to
  knowledge in whatever sequence makes sense to the
  situation at hand



 Semantic Web: User can describe situation at
  hand (goal of learning, previous knowledge,...) and
  perform semantic querying for the suitable learning
  material. The user profile is also accounted for.
  Access to knowledge can be expanded by
  semantically defined navigation.

                      Semantic web for e-learning       11
Benefits of using Semantic Web as a
technology for e-Learning (Symmetry)
 E-Learning: Symmetric – Learning occurs as an
  integrated activity




 Semantic Web: The Semantic Web (semantic
  intranet) offers the potential to become an integration
  platform for all business processes in an
  organization, including learning activities.

                        Semantic web for e-learning     12
Benefits of using Semantic Web as a
technology for e-Learning (Modality)
 E-Learning: Continuous – Learning runs in
  parallel and never stops



 Semantic Web: Active delivery of information
  (based on personalized agents) creates a dynamic
  learning environment.



                      Semantic web for e-learning    13
Benefits of using Semantic Web as a
technology for e-Learning (Authority)
 E-Learning: Distributed – Content comes from the
  interaction of the participants and the educators


 Semantic Web: The Semantic Web will be as
  decentralized as possible. This enables an effective
  co-operative content management




                      Semantic web for e-learning        14
Benefits of using Semantic Web as a
technology for e-Learning (Personalization)
 E-Learning: Personalized – Content is
  determined by the individual user‟s needs and aims
  to satisfy the needs of every user




 Semantic Web: A user (using personalized
  agent) searches for learning material customized for
  her/his needs. The ontology is the link between user
  needs and characteristics of the learning material.

                     Semantic web for e-learning         15
Benefits of using Semantic Web as a
technology for e-Learning (Adaptivity)

 E-Learning: Dynamic – Content changes
  constantly through user input, experiences, new
  practices, business rules and heuristics


 Semantic Web: The Semantic Web enables the
  use of knowledge provided in various forms, by
  semantical annotation of content. Distributed nature
  of the Semantic Web enables continuous
  improvement of learning materials.
                      Semantic web for e-learning        16
Metadata & e-Learning
 Compared to traditional learning, the learning
  scenario in e-Learning is completely different
 learners have a possibility to combine
  learning material in courses on their own. So
  the content of learning material must stand on
  its own.
 content is useless unless it can be searched
  and indexed easily. This is especially true as
  the volume and types of learning content
  increase.

                   Semantic web for e-learning   17
Metadata & e-Learning
(Cont.)
 The accepted definition of meta-data is "data

  about data“. (T. Berners-Lee)

 However, it still seems that most people use

  the word in different and incompatible
  meanings, causing many misunderstandings.



                   Semantic web for e-learning    18
Metadata Standards
 IEEE LOM
  (http://ltsc.ieee.org/doc/wg12/LOM3.6.html)
 ARIADNE
  (http://ariadne.unil.ch/Metadata/)
 IMS
  (http://www.imsproject.org/metadata/imsmdv1
  p2/imsmd_infov1p2.html)



                  Semantic web for e-learning   19
Ontology-based metadata
 From the student point of
  view the most important
  things for searching learning
  materials are: what the
  learning material is about
  (content) and in which form
  this topic is presented
  (context). However, while
  learning material does not
  appear in isolation, another
   dimension (structure) is
  needed to encompass a set
  of learning materials in a
  learning course.
                      Semantic web for e-learning   20
Metadata for describing content of
learning materials
 In an e-Learning environment there is a high
  risk that two authors express the same topic
  in different ways.
 The problem could be solved using domain
  (content) ontologies in which mappings from
  domain vocabulary(s) in the commonly-agree
  terms are defined extensionally

                       Semantic web for e-learning   21
Metadata for describing context of
learning materials
 Learning material could be presented in the
  various learning contexts or in the various
  presentation contexts.
 The context description enables context
  relevant searching for learning material
  according to the preferences of the user.
 In order to achieve shared-understanding
  about meaning of the context vocabulary a
  context-ontology is used.

                   Semantic web for e-learning   22
Metadata for describing structure of
learning materials
 Because e-Learning is often a self-paced
  environment, training needs to be broken
  down into small bits of information that can be
  tailored to meet individual skills gaps and
  delivered as needed.
 These chunks of knowledge should be
  connected in order to create the whole
  course.


                   Semantic web for e-learning   23
Metadata for describing structure of
learning materials (Cont.)
 The structure isn‟t a static one, because it
  depends on user type, users‟ knowledge
  level, users‟ preferences and prerequisite
  materials.
 Several kinds of structuring relations between
  elementary learning materials may be
  identified. Some of them are: Prev, Next,
  IsPartOf, HasPart, References,
  IsReferencedBy, IsBasedOn, IsBasisFor,
  Requires, IsRequiredBy
                    Semantic web for e-learning   24
Metadata for describing structure of
learning materials (Cont.)
 There are semantic connections between
  some of these relations defined by axioms:
  for example, IsPartOf and HasPart are
  mutually inverse relations. This
  corresponding axiom may help in searching
  for information.



                     Semantic web for e-learning   25
Ontology & e-Learning




 Ontology provides a common vocabulary, and an
  explication of what has been often left implicit. (
  Mizoguchi ,1995)



                       Semantic web for e-learning      26
Ontology as an informal conceptual
system




 we admit the presence of an (unspecified) conceptual
  system, which we may assume to underlie a
  particular knowledge base. This is the common
  hypothesis in e-learning implementations. Without
  systematic analysis of the relevant key issues we
  confront an e-learning system as a knowledge carrier
  that utilizes a hidden conceptual system which links
  and integrates several actors, variables and
                        Semantic web for e-learning    27

  relationships.
Ontology as a formal semantic
account




 we have analyzed the phenomenon of e-learning and
  we have concluded several semantic elements that
  formulate a value layer capable of exploit in
  knowledge sources semantically. The major problem
  concerning this interpretation of ontology is the
  complexity of e-learning.
                    Semantic web for e-learning       28
Ontology & e-Learning (Cont.)
 Indeed, ontologies are a means of specifying the
  concepts and their relationships in a particular
  domain of interest.
 Web Ontology languages, like OWL, are specially
  designed to facilitate the sharing of knowledge
  between actors in a distributed environment.
 From the modeling point of view, ontology languages
  are not only able to integrate LOM and Dublin Core
  metadata, but also allow for the extension of the
  description of the learning objects with non standard
  metadata, thus giving users and groups of users
  more flexibility when sharing resources.

                      Semantic web for e-learning     29
Ontology & e-Learning (Cont.)
 Ontologies can be used in e-learning as a
  formal means to describe the organization of
  universities and courses and to define
  services. An e-learning ontology should
  include descriptions of educational
  organizations (course providers), courses and
  people involved in the teaching and learning
  process.



                  Semantic web for e-learning   30
Semantic web for e-learning   31
Scenario
 Maria wants to enroll in an English course in
  a University in Britain in summer 2006. A
  smart search service could analyze Maria‟s
  current location, locate English courses run
  by British Universities and book a ticket for
  Maria to reach her destination from start
  location. This is a simple scenario which the
  broker can split into several simple semantic
  services such as enroll-in-a-course, payment,
  accommodation, arrange-transport and so on.
                  Semantic web for e-learning   32
A formal specification for Maria’s
request




               Semantic web for e-learning   33
Usage Scenario(1)
 Prof. Meyer now wants to find new material.
  For this, he considers two approaches: either
  search for it in the world wide web or in
  distinct decentralized repositories that provide
  more structured semantic metadata about
  learning material. Both tasks can again be
  supported by using an ontology.
 In order to find new relevant material in the
  P2P network, Professor Meyer first needs to
  define a query.
                    Semantic web for e-learning   34
Usage Scenario(2)
 Professor Meyer searches for lectures on the
  topics “Algorithmic” or “Knowledge
  Discovery”.
 Hence he defines the following query: Return
  every „Lecture‟ which „hasTopic‟ „Algorithmics‟
  or which „hasTopic‟ „Knowledge Discovery‟
  and for each match retrieve also the values of
  the properties „dc:title‟ and „dc:author‟.


                   Semantic web for e-learning   35
Usage Scenario(3)
 Professor Meyer knows some web sites that
  are relevant for his task. He is quite certain
  that some interesting material (or at least
  pointers to it) would be accessible there, had
  he only time to browse the sites and follow
  the hyperlinks.
 An obvious solution would be to apply a
  crawler that follows the links starting from
  these pages, and to collect the resources
  showing up.
                   Semantic web for e-learning     36
Usage Scenario(4)
 He selects a set of concepts from the
  ontology, which specifies the kind of pages
  he wants to retrieve.
 The crawler then scores each page and each
  hyperlink according to the frequency of these
  concepts on the whole page and around the
  hyperlink.
 Concepts that Meyer did not type in explicitly,
  but which are semantically related to these
  concepts within the ontology, also add to the
  score.
                   Semantic web for e-learning   37
Extending e-Learning Platforms to
Incorporate Semantic Web
Ontologies
 The main goal in building up ontology for
  e-Learning systems is to represent the
  semantics of the educational materials
 so that they can be reused, shared,
  structured, and so that users of this platform
  (teachers, students, administrators) can
  perform queries wisely.



                    Semantic web for e-learning    38
Step 1 - Establishing Competency
Questions for Learning Materials.
 The ontology must answer, competency questions like:
 What are the subjects taught by the teachers?
 What subjects and modules exist?
 Who is responsible for creating modules?
 Which learning materials compose the platform?
 What are the requirements for some learning materials?
 Are there similar learning materials in the platform?
 What are the types of learning objects that compose the
  learning materials?
 What is the format of the learning objects that compose the
  learning materials?
 What are the characteristics of the learning objects that
  compose the learning materials?

                          Semantic web for e-learning           39
Semantic web for e-learning   40
Step 2 - Establishing Object
Relationships in e-Learning System.
 These relationships enable answering the
  competency questions formulated in the
  previous step.




                  Semantic web for e-learning   41
Step 3 - Establishing the Ontological
Knowledge Base.
 The ontological knowledge base is the model
  core. Therefore, it contains one or more
  ontologies, over which the inference
  mechanisms will act.




                   Semantic web for e-learning   42
Semantic web for e-learning   43
Step 4 - Carrying out Learning
Materials Annotation.
 Annotation must be in accordance with the
  metadata describing the learning materials
  defined in the ontology domain.
 The annotation process is usually slow and
  some points need to be observed before
  initiating annotation work.
 Annotation can be carried through by specific
  tools. For example, the OntoAnnotate tool
  generates the annotation in RDF

                   Semantic web for e-learning   44
Step 5 – Developing the Search
Machine for Ontological Knowledge
Base.
 To carry out the search in ontological
  knowledge base, a search machine is
  needed. This machine will verify the
  relationships and ontological instances
  codified in the ontological language.




                   Semantic web for e-learning   45
Conclusion
 “Making content machine-understandable” is
  a popular paraphrase of the fundamental
  prerequisite for the Semantic Web. In spite of
  its potential philosophical ramifications this
  phrase must be taken very pragmatically:
  content (of whatever type of media) is
  'machine-understandable' if it is bound
  (attached, pointing, etc.) to some formal
  description of itself.

                   Semantic web for e-learning   46
Conclusion (Cont.)
 This vision requires development of new
  technologies for web-friendly data
  description. The Resource Description
  Framework (RDF) metadata standard is a
  core technology used along with other web
  technologies like XML.
 Ontologies are (meta)data schemas,
  providing a controlled vocabulary of concepts,
  each with an explicitly defined and machine
  processable semantics.
                   Semantic web for e-learning   47
                Innovative idea
 Using intelligent-based agents within a CMS (course
  management system ) addresses several limitations
  that currently exist in the areas of user isolation, lack
  of constructivist pedagogy, and lack of quality
  communication with peers and faculty.
 The agent will continuously provide individualized
  feedback to students, remind of the assignment
  deadlines, connect students with peers, and inform
  faculty on student and group progress.



                        Semantic web for e-learning           48
 The agent provides positive feedback for exemplar
  performance or reports gaps in understanding if you
  are falling behind.
 Imagine if you were the faculty member or TA of the
  course, where the agent will automatically report
  assignment status and students who are possible of
  falling below a predetermined level of achievement at
  some points in the semester.




                      Semantic web for e-learning     49
 The Course Agent access to the CMS database and
  get massive information about a course, student
  performance in the course, average class
  performance, course learning objectives,
  assignments, deadlines, etc.
 Access to the student database is needed to learn
  about history, background and experiences.




                     Semantic web for e-learning      50
Scenario
 John Smith sign in the system and he notices
  a concerned face and some text messages.
  The Course Agent uses various algorithms to
  dynamically determine which expression and
  which messages should display on the page.
  John is getting the concerned image because
  the Course Agent has understand that he has
  again missed an assignment, has forgot to
  participate in a required class discussion
  board or has not signed in for the last five
  days.
                  Semantic web for e-learning   51
Scenario (Cont.)
 the Course Agent suggesting John to review
  two additional reading assignments to assist
  him in accomplishing the third learning
  objective of the course. The Agent notes that
  John did poorly on the recent quiz which
  assessed this learning objective, and John‟s
  professor has identified to the Agent various
  reading materials and additional quizzes to
  suggest to those who fall below a minimum
  threshold level.
                   Semantic web for e-learning    52
Scenario (Cont.)
 Course Agent is aware that the next learning
  objective requires a strong math background and
  also recognizes that John does not have a sufficient
  math background, it automatically searches the
  Student database and identifies the top five
  classmates with strong math backgrounds. The
  Agent considers past emails, discussion board, and
  chat activities among these students to note John‟s
  collaborative friends in the class. This last message
  from the Course Agent to John simply informs him
  that the forthcoming learning objective requires a
  strong math background and suggests that the
  following classmates might be able to help him.
                      Semantic web for e-learning         53
Scenario (Cont.)
 The Course Agent then displays a popup
  message informing John that one of the
  student with strong math backgrounds has
  just signed on and he may want to chat with
  him to set up a study time for the forthcoming
  learning objective.




                   Semantic web for e-learning   54
Scenario (Cont.)
 On Wednesday evening, because the Agent
 anticipates that John typically will not sign in
 during the weekend, and because there is an
 assignment deadline on Saturday which John
 has not yet seen because he has not yet
 clicked on the link, when John tries to sign off
 from the course, the Course Agent reminds
 him with a popup message about the
 assignment.

                   Semantic web for e-learning   55
Scenario (Cont.)
 John has configured his Course Agent to
  inform him via a cell phone SMS message
  when two situations occur: the message is
  from the professor or the TA, the importance
  level is high.
 The method of the communication can be
  intelligently reasoned and verified by the
  Agent based on the algorithm built by the
  owner of the Agent along with various other
  internal and external factors.
                   Semantic web for e-learning   56
References
 E-Learning based on the Semantic Web , Ljiljana
  Stojanovic, Steffen Staab, Rudi Studer(2002)
 Ontologies and the Semantic Web for
  E-learning, Demetrios G Sampson , Miltiadis D. Lytras
  (2004)
 Semantic Web Meta-data for E-Learning , Mikael
  Nilsson, Matthias Palmér (2002)
 Design of a Semantic Web-based Brokerage
  Architecture for the E-learning Domain , Juan M.
  Santos, Luis Anido (2005)
 An Ontology-Oriented approach on E-learning ,
  Miltiadis D. Lytras, Athanasia Pouloudi(2005)

                         Semantic web for e-learning      57
References (Cont.)
 E-LEARNING BASED ON CONTEXT ORIENTED
  SEMANTIC WEB, MUNA S. HATEM, HAIDER A.
  RAMADAN (2005)
 Semantic Resource Management for the Web: An
  E-Learning Application, Julien Tane, Christoph
  Schmitz (2004)




                   Semantic web for e-learning   58
     Thank you
for your attention!!!


       Semantic web for e-learning   59

								
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