The Semantic Web in e-Learning

E-learning is not simply about learning happening anywhere, anytime, but it also includes "the personal definition of learning goals, and the synchronous and asynchronous communication, and collaboration between learners and between learners and instructors" (Ghaleb et al, 2006).When talking about discovering new information on the web, one would immediately think about searching for data by writing you keywords in a search engine on the World Wide Web. Sometimes the terms, World Wide Web, Web 2.0 and the Semantic Web are used interchangeably, which shouldn't be the case. Web 2.0 is the second generation of the original World Wide Web which extends it by means of its collaborative elements. In Web 2.0 the main focus is people communicating and sharing information which is why we refer to it as being social. The World Wide Web, or the Web as we better know it is rather contains lots of static HTML pages where as Web 2.0 is build upon dynamic web based communities. The latter make use of many standards, like AJAX, Ruby, XHTML and SOAP. It focuses on giving the best product to people to facilitate their communication without involving them in technical issues.
The Semantic Web focuses on machines instead of people. It is the "emerging landscape of new web technologies aiming at web-based information and services that would be understandable and reusable by both humans and machines" (Sampson, Lytras, Wagner & Diaz, 2004). Presently, the web requires human input in order to fetch data else it would be useless. In the Semantic Web, web pages are machine readable and thus human input isn't needed for the web to search and retrieve information. We say that the semantic web is an extension of the web which adds new data and meta data to the present web documents and transforming them into data. It is only in this way that we can do without humans and machines will be able to read the data by themselves. According to Hendler (2001), the semantic web will be based on ontologies or better "a great number of small ontological components consisting largely of pointers to each other".
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Tim Berners-Lee, inventor of the WWW,URIs, HTTP and HTML supports the idea of the Semantic Web and tries to push it forward. Moreover, the W3C have already developed technologies for web friendly data descriptions and AI researchers have also invented tools and applications for the Semantic web. (Ghaleb et al, 2006)
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A particular project focuses on the implementation of the semantic web on e-learning practices. Qatar University students have made use of this system. The application allows students to:
  • Create e-learning content
  • Annotate
  • Share
  • Discuss
  • Supplies resources like lecture notes, links and exercises

The Semantic Web is made up of semantic web languages, ontologies, semantic markup of web pages and semantic web services (Ghaleb et al., 2006). There exist several semantic web languages, all of which consist of XML, XML Schemas, RDF and RDF Schemas.
Defining XML
An XML document includes:
  1. XML declaration which specifies the version and encoding of XML being used
  2. XML Schema or DTD which contains structure of XML instances
  3. XML instance which is a tagged document including a hierarchy of elements

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Defining RDF
RDF is short for Resource Description Framework and is "framework to represent data about data (metadata), and a model for representing data about "things on the Web" " (Ghaleb et al., 2006). It includes information about the information on the web site. Being a standard, it provides interoperability between applications that share machine-understandable information on the Web. Since it encodes knowledge on the web it makes the web accessible by agents. It was developed under the W3C and was designed to "allow developers to build search engines that rely on the metadata and to allow Internet users to share Web site information more readily" (Webopedia, 2010). RDF relied on XML as an interchange syntax. An RDF schema describes the vocabulary of an RDF model.
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How an RDF service works
What are Ontologies?
The Oxford English Dictionary defines an ontology as being "the science or study of being". In the field of Artificial Intelligence, an ontology is defined as being the "specialization of a conceptualization … usually in some formal and preferably machine-readable manner" (Hendler, 2001). Some also refer to it as an explicit formal specification of the terms in a domain and the relations among them. The purpose behind an ontology is basically to define a common vocabulary for researchers in a domain who need to share information. A lot of disciplines have standard ontologies which are used by domain experts to share and annotate information. In the field of medicine, there is the SNOMED (Systemized Nomenclature of Medicine) and the Unified Medicine Language System. Ontologies are based on machine-interpretable definitions. According to Ghaleb et al., an ontology is a "text-based piece of reference-knowledge, put somewhere on the Web for agents to consult it when necessary" (2006).
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Basic Ontology

An ontology language that extends RDF is OWL. It stands for Web Ontology Language and has more expressive constructs than RDF as well as facilitates web agent's interaction.

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Ontologies are useful for various reasons. These include:
  1. Allow us or software agents to share common understanding of the structure of information
  2. Enable reuse of domain knowledge
  3. Make domain assumptions explicit
  4. Separate domain knowledge from the operational knowledge
  5. Analyze domain knowledge

In order for ontologies to be created, we need to make us of RDF.

Re-use and understanding of knowledge

E-learning can be implemented in the semantic web and results in being more effective and efficient. This is possible because e-learning is based on the use of learning objects. In the view, an ontology becomes "a network of semantically related learning objects for a specific learning or instructional domain" (Wang, 2008). Since learning objects are complex in their structure, it is possible to represent them as an ontology. Thorough examination of the domain must happen for an ontology to be produced. Hence the production of ontologies required an extensive understanding of knowledge in the domain. Since the ontologies created are based on standardised learning objects, this makes it possible for them to be re-used. Re-usability is also encouraged through the semantic web's principle where all the content is both machine and human understandable.
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General structure of an ontology of learning objects

Ontological categories of learning objects for e-learning
"Applications of ontology to model related components of learning objects repository would contribute to effective reused of learning objects resources" (Wang, 2008). Below is a list of fundamental learning objects involved in an e-learning system.

  1. Learning subject
  2. Learning objective
  3. Instructional method
  4. Delivery instrument
  5. Assessment instrument
  6. Assessment outcome

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More information
Just In Time Learning (JIT)
Competency based Just In Time Learning makes use of competency ontologies and semantic web services. The goal of JIT learning is "to increase efficiency by identifying precisely the training that an employee needs to do their job and provide that training"(Woelk, 2010).
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Why do we need AI supplied by machines in e-learning context?
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In the semantic web, as has been already stated, both humans and machines can read web content. Shared ontologies help to exchange data and meaning between web-based services. However, the question is, how can machines read content? In order to do this intelligent agents come into play. These intelligent agents would "find possible ways to meet user needs and offer the user choices for their achievement" (Hendler, 2001).

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Depiction of mapping between agents and ontologies they use
References
Sampson, D. G., Lytras, M. D., Wagner, G., & Diaz, P. (2004). Ontologies and the Semantic Web for E-learning. Educational Technology & Society, 7 (4), 26-28

Woelk, D. e-learning, Semantic Web Services and Competency Ontologies. Retrieved October 31, 2013, from https://www.um.edu.mt/vle/1314/pluginfile.php/23820/mod_resource/content/1/Literature/SemanticWebandOntologies_1_Copy.pdf

Ghaleb, F., Daoud, S., Hasna, A., Jaam, J., El-Seoud, S. A., & El-Sofany, H. (2006). E-Learning Model Based on Semantic Web Technology. International Journal of Computing & Information Sciences, 4(2), 63-71.

Hendler, J. (2001, March). Agents and the Semantic Web. Intelligent Systems, IEEE, 16(2), 30-37.

Wang, S. (2008). Ontology of learning objects repository for pedagogical knowledge sharing. Interdisciplinary Journal of e-learning and learning objects, 4, 1-12.