semantic web for e-learning setareh momayez. semantic web for e-learning2 outline introduction...

Semantic Web for e-Learning

Setareh Momayez

Semantic web for e-learning 2

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


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, 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).


Differences between training and e-Learning (Drucker 2000)


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 & 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.


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.


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.


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.


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.


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.


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


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.


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.


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.


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.


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/imsmdv1p2/imsmd_infov1p2.html)


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.


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


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.


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.


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


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.


Ontology & e-Learning

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


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 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.


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.


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.


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.


A formal specification for Maria’s request


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.


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’.


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.


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.


Extending e-Learning Platforms to Incorporate Semantic WebOntologies 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.


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?


Step 2 - Establishing Object Relationships in e-Learning System. These relationships enable answering the

competency questions formulated in the previous step.


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.


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


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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.


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)


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)


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semantic web for e-learning setareh momayez. semantic web for e-learning2 outline introduction...

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