Mostafa Saleh
Department of Information Systems, Faculty of Computing & Information Technology
King Abdulaziz University, Jeddah, Saudi Arabia msherbini@kau.edu.sa
ABSTRACT . Researchers have used ontologies entirely to describe the whole learning object (LO). Although this approach is useful to look for the whole LO in respective repositories, but it does not provide us with features neither to reuse components of LO, nor to incorporate an explicit specification of semantics into LO content. This paper proposes an approach to describe LO contents based on ontology. Content's domain ontology is used to describe what the learning material is about, context ontology is used to describe in which form the LO is presented, and structure ontology is used to describe in which style the learning material will appear to the user. This notion supports the workers in e-learning to assemble their learning objects from other learning object pieces and enables them to query LO according to domain, context, and structure ontologies.
Keywords: Learning objects, Ontology, Metadata, Learning object contents, Interoperability, Reusability, SCORM.
1.
Introduction
According to the IEEE Learning Technology Standards Committee [1], e-learning is defined as the use of computers and network technology to create, deliver, manage, and support learning at anytime, anywhere.
Moreover, dynamically changing business environment requires e-learning systems to be flexible and adaptive, and to be able to support non-linear and personalized learning processes.
But, the production of e-learning material is time-consuming and labor-intensive, and learning material created by different providers is usually not in easy to share and interoperable format. This hinders people from taking one of the greatest advantages of e-learning, i.e., exchanging learning resources [2]. For example, learning content offered by different education providers may be generated in different formats and described using various metadata, as well as using different terms for the same concepts. As a result, systematic compilation of an online course from distributed learning content remains as a great challenge. Therefore, an infrastructure is highly desired for creating machine-understandable, sharable, and reusable learning contents. Also, to improve e-learning effectiveness while reducing the cost, e-learning systems should be designed and implemented to support random, on demand, content access to the learning materials [3,4].
Learning objects (LOs) have received a great attention in e-learning community in recent years. Although, it is very expensive and time-consuming to develop the content for an e-learning course based on these learning objects, but reusing those LOs created by others reduces the time and cost to develop learning materials [5]. As the author explored in [6], learning objects are like software components, so, reusing published learning objects may be of a higher quality than developing them from scratch, and also, speeding up the total course material construction time.
Different learning communities have established their own standards for representing metadata vocabularies to fulfill their specific needs. But, most of those metadata standards lake the semantic representation. These standards can’t enable interoperability across domains due to the lack of a shared understanding of vocabularies across these domains. This problem may be solved by utilizing ontologies as a conceptual backbone in an eLearning scenario
[7].
SCORM is a commonly used e-learning standard supported by more than 90% of the learning management and learning content management. The major goals of SCORM are to promote reusability, interoperability, accessibility and durability [8]. Learning Management systems are specialized software applications that are custom made for managing and delivering different types of educational trainings [9],[10]. SCORM is sponsored and maintained by the Advanced Distributed Learning Initiative (ADL), as a reference model derived from work done by various industry and technology organizations, including the IMS Global Learning Consortium (IMC), the Aviation Industry CBT Committee (AICC) and the Electrical and Electronics Engineers Learning Technology
Standards Committee (IEEE LTSC). SCORM provides a framework that applies to eLearning content in an LMS in order to define its encapsulation, launching and data exchange [11].
Ontologies allow for a representation of knowledge that enables sharable understanding of concepts within the same domain. Representing knowledge in the form of a conceptualization (i.e. ontologies) is crucial for the
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automatic processing of the information on the Web. However, ontologies can also enhance the management, distribution and retrieval of the learning material within a Learning Management System (LMS) and can thus play a relevant role in eLearning. Using ontologies in representing knowledge and educational contents in educational systems reveals several advantages. Ontologies give learners the ability to visualize and also grasp the relationships between the different concepts within the domain of study. Ontology also, support learners with advanced browsing and searching support. Finally, ontology provides those learners with intelligent and personalized support [12], [13], [14].
The concept of content in an educational context is very different from the one in other fields such as publishing or electronic newspapers. Teachers have main concern when using e-learning environments which is the availability of the appropriate structures and tools for organizing such learning resources and making them accessible to learners [15],[16]. Artificial Intelligence systems was the first application of ontology in computer systems. Later, ontology has been used in diverse computer systems, and are the basis of diverse knowledge management systems, such as business fields, in the intelligent management of news or for wider uses such as the definition of conceptual models, among others. Štuikys, [17] introduced and discussed a taxonomy-based framework to understand the Computer Science LO domain. He explores the importance of LO domain in elearning in general, Computer Science specially.
The size and scope of the pieces of learning content that are combined to form the content packages is a key consideration. For example, if a content package is comprised of only a few pieces of large grained learning content then re-sequencing them to form a new package that supports a different pedagogical approach may not be possible, as the new narrative flow may not make sense in the context of the learner’s learning. This issue is vital in the reuse of any learning object [18].
To facilitate reuse, learning objects should be fine-grained, paragraph or diagram size, for example. Content packages should be used where larger grained learning resources are required as they may be constructed using smaller learning resources and/or other content packages. With smaller grained learning objects the course author has greater flexibility in the creation of additional content packages. For example, if learning objects are available at the paragraph level then the course author can add/remove content at this level to produce tailored courses [18].
Beside to reusability, LOs should be described in a form that enables LOs researches to find their needs. The
Semantic Web and ontology are used best to fulfill this goal [19]. Ontology is used to describe the LOs in agreedupon concepts, consequently it enables Semantic Web agents to perform an advanced search within LO repositories [20].
Recently, researches have directed their effort towards contents reusability. For example, Mohan and Brooks [21] analyzed relations of LOs and the Semantic Web, especially emphasizing importance of ontologies. Accordingly, they identify several kinds of ontologies related to LOs: ontologies covering domain concepts, ontologies for elearning, ontologies about teaching and learning strategies, and ontologies about physical structuring of learning objects.
Gaševiü et, al, [22] proposed to enhance learning object (LO) content using ontologies and Semantic Web languages. They suggested creating LOs that have content marked up in accordance with domain ontologies.
Accordingly, LOs can be used not only as learning materials, but they can also be used in real-world applications.
Gašević, et.al, [23],[24] proposed a framework for building learning object (LO) content using ontologies. They proposed the use of two kinds of ontologies as a solution to this problem: content structure ontologies and domain ontologies. Further, they explored the necessary tools for such an approach, like Semantic Web annotation tools and specific domain authoring tools, as well as domain XML formats and transformation techniques. Additionally, they give a conceptual overview of a course authoring tool that fully supports the proposed approach. They proposed ALOCoM ontology [25] to cover structural aspects of different types of LOs, so that LO components can be reused as well.
Brase, et, al, [26] gave an example of an ontology developed in accordance with the ACM Computer Classification
System (ACM CCS). This ontology is represented in RDF, and used in the Edutella system. However, these solutions do not provide us with a possibility to reuse just certain components of a LO or to use the same LO in different ways (e.g. presentational adaptivity).
This paper explores in some details the notion of reusing components of LO by associating ontologies with the
LO: contents (domain); context (filed of uses); and structure. A prototype is implemented and the generated
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ontological files are in Resource Description Framework (RDF) which is a widely accepted standard proposed by the World Wide Web Consortium (W3C) for representing metadata.
The organization of the rest of this paper is as follows. Section 2 discusses the concept of ontologies in learning objects. Section 3 is directed to discuss content domain ontology. Section 4 is directed to context ontology, and section 5 for structure ontology. Finally, section 6 is devoted to the conclusion.
2.
Learning Object Ontology
The role of ontology is to formally describe shared meaning of used vocabulary (set of symbols). In fact, ontology constrains the set of possible mapping between symbols and their meanings. But the shared understanding problem in e-learning occurs on several orthogonal levels, which are related to several aspects of document usage, as we described in Figure 1.
Fig. 1.
Learning objects ontology.
Metadata is defined as information about information [27]. We need to use metadata with a common format to describe LOs; consequently this description enables LOs to be readable and understandable by the machine. One of the most common metadata schemes on the web today is the”Dublin Core” (DC) Schema by the Dublin Core
Metadata Initiative (DCMI) [28]. Each Dublin Core element is defined using a set of 15 attributes from the
ISO/IEC 11179 standard for the description of data elements, including for example: Title, Identifier, Language, and Comment. Whereas ”Simple Dublin Core” uses only the elements from the Dublin Core metadata set as attribute-value-pairs, ”Qualified Dublin Core” employs additional qualifiers to further refine the meaning of a resource. The DCMI recommends a set of qualifiers called ”Dublin Core Qualifiers” (DCQ), which include for example Name, Label, Definition or Comment as alternative qualifiers to refine the Title element. For a complete description, please refer to [28].
Since Dublin Core is designed for metadata for any kind of (digital) resources, it pays no attention to the specific needs encountered in describing learning resources. The" Learning Objects Metadata Standard” (LOM) [29] by the Learning Technology Standards Committee (LTSC) of the IEEE was therefore established as an extension of
Dublin Core. LOM is the base model for learning community. Based on LOM, other standards are established. For example, IMS (Instructional Management System) is based on IEEE LOM [30]. ADL SCORM [31] is based on
IMS.
Inspecting the required metadata for representing the LOM, we found that they comprise more than 70 items, which are considered as a source for hard coding the metadata. We suggested using a minimum number of important metadata items to be mapped in the implementation [6].
To enable global operation with learning objects, we developed a Global Learning Object Identifier (GLOID). The
GLOID is suggested to support the uniqueness of the learning object ID through global as well as local manipulation. Also, it will be used as an attribute for the 1.1 Identifier elements in the General category, and in the 7.2 Resource. Figure 2 shows a sample for GUID for the Computer Science field [6].
A Global Universal Identifier (GLUID)
+Dewey Decimal Classification System (Super-topic)
+ Any universally accepted classification for the files (Sub-topic)
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GUID- DDCS – CSS
GUID
Globally Unique Identifier, a unique 128-bit
Dewey Decimal Classification System (Super-topic)
000 Generalities
004 Data processing, Computer science
ACM Computer Classification Systems (Sub-topic)
IS6
IS Intelligent systems
IS/Agents
GLOID = C1226DB6-5BF2-46cf-9CD9-123467116C70 + 004 + IS-Agents
Fig. 2. A sample GLOID for a computer science field.
The reusability at this level is only on the learning object level as a whole. Annotations of LOs with the standardcompliant metadata sets (e.g., IEEE Learning Object Metadata [29] and Dublin Core [28] aim at enabling search and retrieval of existing LOs stored in LO repositories. Accordingly, metadata is seen as the primary mean for developing LOs reusability. However, very often a content author needs to reuse just some specific parts of a LO, rather than the entire LO, for example, just a couple of paragraphs, or an image or a table out of a text document.
Such a need lets the content author typically turns to search-read-copy-paste approach. To support further levels of reusability, it is required to give some details about the learning object contents. Next sections discuss domain ontology; context ontology; and structure ontology to support more reusability levels.
Content's domain ontology is used to describe what the learning material is about, context ontology is used to describe in which form the LO is presented, and structure ontology is used to describe in which style the learning material will appear to the user. ). The proposed approach assumes attaching (inheriting) metadata to each component of a LO, thus making individual components searchable and reusable.
The rationale behind using ontologies in the proposed approach is to enable Semantic Web agents to perform an advanced search of LO repositories. The advancement reflects in ability to search for a content of a certain type
“introduction”, dealing with a certain topic “Semantic Web”) and being at a certain level of granularity "slide”.
Besides the benefit of having a more convenient search mechanism that better reflects the searchers’ needs, another important benefit lies in an ability to (semi-)automatically compose the retrieved content units into a new LO compliant to the specific instructional approach of a content author.
Each content unit should be annotated in order to be more easily searchable and thus reusable. Annotations of content units in the proposed approach are based on the Reduced IEEE LOM [6]. Web aimed for both human and machine consumption, consequently all the data it deals with needed to be presented in a machine comprehensible format. This means that not only content units but also their metadata must be expressed in a Semantic Web language. Accordingly, our starting point was the official proposal for the IEEE LOM RDF Binding specification
[2]. However, not all LOM elements are used, but a subset necessary to support the intended functionalities of the system [6].
3.
Content Domain Ontology
In order to enable effective learning object contents reusability we have to further enhance their semantic. Content's domain ontology is used to describe what the learning material is about. A LO created using this principle gets a new dimension of reusability: it can be used in different courses and different learning strategies. For example, in the ACM Curricula [32], the “AL3. Fundamental computing algorithms” topics (Simple numerical algorithms; sequential and binary search algorithms; quadratic and O(N log N) sorting algorithms; hashing; binary search trees) are taught as topics within (CS111. Introduction to Programming, CS112. Data Abstraction, and CS210.
Algorithm Design and Analysis). Also, teaching a course in “Introduction to Computer” to Literature students will be different from those students in Medicine faculties from the teaching strategy and applications fields.
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One important question arises here, as there is domain ontology within the learning object metadata, whey it is recommended to use domain ontology to describe the learning object contents itself? The answer is simple, as the domain ontology for the learning object as a whole describes the learning object as a concrete unit and can’t give details about the contents. Also, there are no restrictions on learning object granularity, so, learning object developers can prepare their material by including many variations for the same learning object in the same package. For example, the developer can package the whole information about a whole lecture in one learning object unit.
Now, a great number of specific domain ontologies is already available at the WWW. Actually, we can deal with libraries of ontologies created through contributions of the Semantic Web community’s members. For example,
DAML Ontology Library ( http://www.daml.org/ontologies/ ) holds nearly 300 ontologies written in the DAML ontology language. Authors of a LO can search one of those libraries in order to find an ontology that best describes content of the LO they are creating. Also, authors might be provided with means to create their own ontology during a LO construction.
For example, the ACM Computing Curricula 2013 [32] has been used by the Association for Computer Machinery since several decades to classify scientific publications in the field of computer science. The classification has up to four levels containing unordered keywords, thus including about 1600 entries on all four levels. To reach to a course about “Agents” we can navigate to IS category (IS Intelligent Systems) and then inside the IS we can go to
“IS- Agents” as shown in Figure 3.
Fig. 3. Domain ontology for Computer Science from [32], and the details of Intelligent systems.
The shared-understanding for the same concept in the domain is required in e-learning when one tries to define the content of a learning material to his audience, and also when he tries to look for particular learning material.
In e-learning environment there is a high risk that two authors express the same topic in different ways. This means that semantically identical concepts (i.e. topics of e-Learning content) may be expressed by different keywords. For example, one may use the following semantically equivalent terms for “Agent”: agent, actor, contributor, creator, player, worker, and performer. The problem could be solved using domain ontologies where we can map pings from user vocabularies into the commonly-agreed-upon terms in the domain ontology. For example, the keywords such as agent, actor, contributor, creator, player, doer, worker, and performer are mapped to the concept "Agent" in the domain ontology [23]. This research suggested using concepts in Table 1 to enrich
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the domain ontology description. The namespace "locVoc" is used to denote the learning object content vocabularies. Figure 4 gives a sample code with RDF to describe the added concepts for domain ontology about
"Agent".
Table 1: Added ontological metadata items to the domain ontology.
Metadata element locVoc:Keyword
Description
A related synonyms keyword for searching. locVoc:App-Field locVoc:CourseOnt-TYPE
The field of application for that domain.
Course ontology within the sub topic classification identifier. locVoc: CourseOnt-VAL The value assigned to that content according to the chosen course ontology.
<rdf:Description rdf:ID="agent">
<locVoc:App-Field>Computer Engineering</locVoc:App-Field>
<locVoc:Keyword>
<rdf:Bag>
<rdf:li>actor</rdf:li>
<rdf:li>contributor</rdf:li>
<rdf:li>creator</rdf:li>
<rdf:li>player</rdf:li>
<rdf:li>worker</rdf:li>
</rdf:Bag>
</locVoc:Keyword>
<locVoc:CourseOnt-TYPE>IS6</locVoc:CourseOnt-TYPE>
1
<locVoc: CourseOnt-VAL>
< !-- Definition of agents -->
</locVoc: CourseOnt-VAL>
</rdf:Description>
Fig. 4. Added domain ontology concepts about "Agent".
The majority of the domain ontology concepts can be inherited from the whole learning object metadata, so, the items used in the learning object content metadata are minimized and directed to those items related to synonyms keywords, and filed of application. For example for teaching statistics to medicine students, we should choose the application filed of medicine to be near to students as possible.
4.
Context Ontology
Learning material could be presented in one of the various learning contexts: as introduction, as analysis, as discussion, as conclusion, as example; or in one or more of the various presentation contexts: as text, as audio, as video, as figure; or in one of user contexts: for learner, for instructor, for developer. The context description enables context relevant searching for learning material according to the preferences of the user. For example, if the user needs a more detailed explanation of the topic, it is reasonable to find learning material which describes an example of the given topic. In order to achieve shared understanding about meaning of the context vocabulary (e.g. intro or introduction) a context ontology is used [23].
Table 2: Added ontological in context ontology.
Context Keyword locVoc:lern-Context
Description
The learning context. locVoc:pres-Context locVoc:user-Context locVoc:type locVoc:Keyword
The presentation context.
The user context.
The type for that learning object component’s context.
A related synonyms keyword for searching.
For example, in computer science courses, like Agents, the “ Definition of agents” can be presented for the instructor in a brief definition within a Power Point presentation, while for student it is better to be a detailed definition with animation. Also, for the same user it may be in different presentation contexts. For example, in solving mathematical equations, it may be just plain steps to show the solution, or in the form of animation to show the solution, or in interactive form to be attractive for the learner.
5.
Content Structure Ontology
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Learning material is usually more complex in its structure than continuous prose, so it requires greater care in its design and appearance. Much of it will not be read continuously. The structure isn’t a static one, because it depends on user type, users’ knowledge level, users’ preferences and prerequisite materials. But, again shared understanding about used terms is also needed for describing the structure of a learning course. 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, IsRequiredB y. 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.
Without the definition of the inverse relation, searching for information would depend on the strategy of providing meta data information. If one defines that some learning material named “X” “IsBasedOn” some other learning material named “Y”, there is no possibility (without programming or explicit specification) to find all learning materials the learning material “Y” “IsBasisFor”. The reader may note that these three dimensions of metadata also appear in the conventional meta data model (content = classification metadata, context
= educational/pedagogical metadata, structure = relation al metadata).
However, our metadata are ontology based metadata and describe the whole domain (including axioms), not only data. Consequences are, as mentioned previously, better (in the mean of the semantic) describing of learning materials and better searching for useful materials according to user preferences. Our ontology based approach could be very easily ex tended to the situation that all of the conventional metadata levels (e.g., general, technical) are used (in ontology based manner) in annotation of learning materials. [31]
Table 3: Added ontological in structure ontology.
Concept locVoc:Structure
Inverse Concept Comment
The structural ontology section dcterms:Prev dcterms:IsPartOf dcterms:References dcterms:IsBasedOn dcterms:Next dcterms:HasPart dcterms:IsReferencedBy dcterms:IsBasisFor
The sequence of presentation.
The super/sub class relation.
The link between contents.
The prerequisite relation.
According to ACM [30], the course in Agents can be presented as in Figure 5. Figure 6 represents the RDF code for the structure of a learning object about “ Agent architectures” with respect to the course “Agents” and also with respect to other modules, and other lectures. This is a simple representation for the shake of simplicity, and clarity.
Agent architectures
Course
Module
Simple reactive agents Layered architectures
Lecture
Reactive planners
< !--complete the namespace declaration -->
<rdf:Description rdf:ID= "Agent architectures" >
<locVoc:Structure>
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<dcterms:IsPartOf rdf:resource=”#Agents”/>
<dcterms:HasPart rdf:resource=”#Simple reactive agents”/>
<dcterms:HasPart rdf:resource=”#Reactive planners”/>
<dcterms:HasPart rdf:resource=”#Layered architectures”/>
<dcterms:Prev rdf:resource=”#Agent theory”/>
</locVoc:Structure>
</rdf:Description>
</rdf:RDF>
Fig. 6. The RDF code for the object structure about "Agent Architecture".
In this research, we have implemented a tool to edit the LO content ontology using C# and generate the metadata in the form of RDF. The process of annotating LOs’ components is mostly based on a top-down approach, meaning that metadata for describing components of an LO are derived from the metadata assigned to their parent LO.
Goals of this top-down approach can be summarized as follows: (1) The values of some metadata elements are inherited from a LO to its components such as dc:creator , and dc:language metadata elements, refereeing to the author(s), and language(s) of a content unit, respectively. (2) Some metadata elements of RLOM RDF Binding profile are meaningful only in the context of an LO as a whole. Therefore, they are not supposed to be assigned to the components smaller than LOs. Those metadata elements are: lom-edu:difficulty and lomcls:accessibilityRestrictions.
6.
Conclusion e-Learning is a trend in education. Learners can benefit because the technology has no restriction in time and distance. However, building an e-learning environment means not only building a system but also generating highquality teaching materials. Many prefer to design teaching material by combining several learning objects. Authors spend a lot of time designing similar teaching materials repeatedly. How to give the provider and consumer a convenient environment for producing and selecting teaching material is important. This paper has presented an approach based on using ontologies for authoring LO contents to highly increase learning object reusability.
Content's domain ontology is used to describe what the learning material is about, context ontology is used to describe in which form the LO is presented, and structure ontology is used to describe in which style the learning material will appear to the user. A prototype for LO ontology tool is being implemented. This work supports the workers in e-learning to assemble their learning objects from other learning object pieces and enables them to query LO according to domain, context, and structure ontologies.
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