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1. Making the education system smart and reflective;
2. Delivering explicit knowledge;
3. Standardizing vocabulary;
4. Ease of communication, and;
5. Making knowledge reusable.
Breuker proposed the use of ontology in education for indexing and specifying various relationships and structure within the knowledge domain.
Table1 briefly compares four different knowledge representation ways. Textbook is the traditional way for knowledge sharing and representation. However, it cannot fulfill the requirements of user interaction and dynamic personal adaption. Google covers a lot of resources. However, since it is not specific for education purpose, knowledge is not organized very systematically. Wikipedia is good at knowledge representation. Even though it does not support graphically organized knowledge skeleton, it does supply a list of links to related concept definitions. Ontology system has several advantages over them:
1. It supports graphically organized knowledge skeleton. This will help students have a overall idea about "What this topic about?", "What subtopics are included in this topic?", and "What is the core?";
2. It is organized and ready for sharing;
3. It is more accurate since it is built up by domain experts;
4. Personal adaptive, and;
5. Data analysis and report generation.
Textbook
|
Google Search | Wikipedia | Ontology System | |
|---|---|---|---|---|
| Outline | Yes |
No
|
Yes
|
Yes
|
| Tutorial | No |
No
|
No
|
Can have
|
| Accurate Infomation | Yes |
Maybe
|
Maybe
|
Yes
|
| Environment | Hard copy
|
Internet
|
Internet
|
Computer
|
Organized information (information organization)
|
Yes |
Partial
|
Yes
|
Yes
|
Graphical ontology (representation)
|
Yes |
Yes
|
Yes
|
Yes
|
Query / navigation |
keyword search, indexing
|
Yes
|
Yes
|
Yes
|
Question generation |
specified
|
only the specific question can be generated, can generate question for nearmiss, syntax not semantic
|
No
|
several kinds of question
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1. Using Ontology to Index Educaiton Materials: Murray proposed a framework called Model for Distributed Curriculum (MDC) to allow one web-based tutorial to include a specification for another tutorial. By using ontology to organize tutorials, when a topic is requested, not only the corresponding tutorial, but also the related ones will be invoked. Shabajee and the colleagues proposed to use ontology for categorizing learning topics to improve the education in Singapore. Bouzeghoub and the colleagues uses ontology to develop portals for digital resources for the generation of adaptive courses. Kasai and the colleagues also uses ontology to organize the education goal for IT education. And, ontology is used to structure the leaning objects (LO) from different tutors. Ontology is used to facilitate LOs discovering and mergence. In an authoring tool to support the development of intelligent tutoring systems to teach literacy, Calvalho, Pain, and Cox proposed to use ontology to categorize the teaching strategic rules.
2. Specifying user profiles: Another common use is to use ontology languages or tools such as RDF and OWL to specify user profiles so that the information can be exchanged with another source. For example, a framework for user profile management proposed by Palmisano and the colleagues uses RDF to specify user profiles. Another example is using ontology for web-based learning resource repository proposed by Song and the colleagues.
3. Describing applications and their interfaces: Ontology is used to specify applications and their interfaces including axioms, reasoning mechanisms, and interaction. In this case, XML is often used . Ontology can also be used to describe a system environment and user context. One example is described in a paper about design environment published by Hayashi and the colleagues.
Knowledge representation and assessment generation:
Here, an ontology system is used to store knowledge (not just indexes) and the ontology infrastructure is used to aid learning.
1. Knowledge representation: Motta and the colleagues categorized the ontology into three types: topic, community-oriented knowledge structure, and learning communities. In the community-oriented knowledge structure, the processes of most important or controversial knowledge are represented. Learning communities are communities with hobbyist interest which is more like profile ontology.
2. Question generation: Holohan and others proposed a system OntAWare that provides a semi-automatic question generation. The system, based on a relational database management system, takes an ontology system specified in OWL, and generate questions related to class-subclass and class-instance relationship. The proposed ontology system can generate questions based on all the relationships including those in concepts and in processes. Hirashima additionally proposed that the differences between them should be tracked when a user makes a new problem by changing the basic problem.
3. Adaptive learning in a context: An ontology system is used to support learning in an adaptive manner. Berlanga and Garcia proposed a framework where the learning styles and test are categorized. With adaptive rules, the learning style will be switched accordingly. Huang and his colleagues believe that with semantic web techniques being added, the e-learning will be improved. It is used in a semantic context model, intelligent personal agents, and conceptual learning theories. In the new e-learning framework, the individual’s personality and interests are taken into account when the learning services are delivered.
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