Master s Thesis Conceptualization of Teaching Material

Transcription

1 OTTO-VON-GUERICKE-UNIVERSITÄT MAGDEBURG OTTO-VON-GUERICKE-UNIVERSITÄT MAGDEBURG FAKULTÄT FÜR INFORMATIK Institut für Wissens- und Sprachverarbeitung Master s Thesis Conceptualization of Teaching Material Bhavani Veeramachaneni October 31, 2005 Supervisors Prof. Dr. Dietmar Rösner Dipl.-Inf. Manuela Kunze Otto-von-Guericke-Universität Magdeburg Fakultät für Informatik Universitätsplatz Magdeburg

2 Abstract The Internet and World Wide Web are being used as support aids to facilitate the delivery of teaching and learning materials. The content of the related courses taught at different universities and organizations tend to be strikingly similar. The gains resulted by sharing the teaching material are high. The problem here is that most systems use different formats, languages and vocabularies to represent and to store these resources. Hence there is no way for two different applications to interoperate even if their teaching contents belong to the same domain and so the knowledge exposed by one cannot be used by another. A possible solution to the problem of sharing and reuse of learning resources is to have a shared vocabulary. Ontologies provide a shared and common understanding of a domain that can be communicated between people and heterogeneous application systems. An important aspect of interoperability of learning objects is a common format for describing content. In this thesis, ontologies for the teaching material is developed. Ontologies for the content and metadata of teaching material is developed. Metadata helps people organize, find, and use resources effectively. The IEEE Learning Object Metadata (LOM) was developed to provide structured metadata descriptions of learning resources called Learning Objects in order to enable semantic interoperability among applications on the e-learning domain. The metadata properties adequate for this application are used from IEEE LOM. If the applications share the common ontology of teaching material then the teaching material of one application can be used by another, it also provides intelligent integration such as sharing, searching and reusing information among applications.

3 Acknowledgements On this page, I would like to express my gratitude to all those who gave me the possibility to complete this thesis. Firstly, thanks to my supervisor Dipl.-Inf. Manuela Kunze who gave me this topic to work on it. All the inspiration and motivation behind this work is due to her. She proved to be an ideal supervisor during the thesis. Her deep blue remarks on the first draft helped me a lot to learn and improve my thesis report. The words are simply not enough to express my regards for her. I would like to profusely thank my supervisor Prof. Dr. Dietmar Rösner for giving me an opportunity to work under his esteemed guidance.

4 Declaration I herewith declare that I have completed this work by myself and only with the help of the stated references. Bhavani Veeramachaneni Matrikelnummer : Magdeburg, August 17,

5 Contents 1 Introduction Motivation Scope and Goal of Thesis Principal Results Organization of Thesis Learning Objects and Metadata for Annotation of Learning Objects Learning Objects Reusability of Learning Objects Factors Effecting Reusability of Learning Objects Metadata for Annotation of Learning Objects Learning Object Metadata Standard (LOM) Bloom s Taxonomy Summary Ontologies and Representation What are Ontologies? Ontology Components Design Criteria and Reasons for Developing Ontology Application Areas for Ontologies Ontologies and Semantic Web Topic Maps RDF Schema

6 3.2.3 OWL Comparing OWL and Topic Maps Ontology for Teaching Material and Modelling with Protégé Model of Lecture Material Model of Exercises Modelling with Protégé Conclusion 61 6

7 1 Introduction Technology extends our abilities to change the world. Internet has brought about drastic changes in the way people work, communicate and entertain themselves. It is also poised to bring about paradigm shift in the way people learn. There are a number of advantages of using the Internet and the Web as a teaching tool. Increasingly, the Internet and World Wide Web are being used as support aids to facilitate the delivery of teaching and learning materials [4]. The Web is becoming the world virtual library, where information on any subject is available. This is more efficient and cost effective compared to traditional classroom environment since students can access learning materials at any time and even students whose geographical reach have prevented them can now access the learning material. Educational domain is often engaged in massive and senseless duplication for re-creating the existing teaching materials. The content of the related courses taught at different universities and organizations tend to be strikingly similar. The gains resulted by sharing the teaching material are high. 1.1 Motivation Anyone who has had to create learning materials from scratch knows just how labor intensive and time consuming the process can be, even with the existence of a detailed course descriptions and lesson plans. This creative process can be made easier by the reuse of existing teaching and learning materials [8]. If course content is either partially or completely delivered using learning objects, there is a great potential for reusing these resources within one organization, and more importantly, between organizations [12]. A learning object is a digital learning resource that facilitates a single learning objective and which may be reused in a different context. The term digital learning resource can be defined as a digital resource that has a specified educational purpose or context [40]. 7

8 A large number of digital learning resources currently reside on the Web, located at Web sites in educational institutions and different organizations. Many of these resources like presentations, course outlines, etc. are created to support class room instruction. Most of them do not conform to the definition of learning objects and it is difficult to reuse content from one learning system to another. It is possible to convert these learning resources into learning objects by breaking them up into smaller chunks of content [30]. The problem here is that most systems use different formats, languages and vocabularies to represent and to store these resources. Hence there is no way for two different applications to interoperate even if their teaching contents belong to the same domain and so the knowledge exposed by one cannot be used by another. In other words, the ontological support in applications today is poor, because they have not been designed with automatic knowledge sharing and reuse in mind. Industries have attained today s high productivity due to standardization of basic components, say, nuts and bolts. Using standardized basic components, one can easily design their own model by configuring them. Standardization mainly provides us with a common vocabulary for understanding what have been done to date with less ambiguity [28]. Humans can communicate with each other because we have a common platform to rely on.we can express our ideas using concepts in the common platform. A possible solution to the problem of sharing and reuse of learning objects is to have a shared vocabulary. Specification of functional components should be described in terms of shared vocabulary. Ontology provides a shared vocabulary, which can be used to model a domain, that is, the type of objects and/or concepts that exist, and their properties and relations [19]. In the context of knowledge sharing, ontology means a specification of a conceptualization [18]. That is, ontology is a description (like a formal specification of a program) of the concepts and relationships that can exist for an agent or a community of agents. This definition is consistent with the usage of ontology as set-of-concept-definitions, but more general. And it is certainly a different sense of the word than its use in philosophy. Ontology should capture domain knowledge and provide a commonly agreed upon and shared understanding of the domain. It should make explicit which are objects of the domain, that we can talk about, what are the relations linking them together and which are the axioms governing their behavior. These shareable ontologies merely serve to standardize and provide interpretations for the contents and the Web-based teaching material [9]. 8

9 In order to make contents of any application machine-understandable and machine-interpretable, it is necessary to annotate the applications content appropriately. This means that content of learning object must be semantically marked-up i.e., if computers have to understand and interpret the teaching materials, the application pages need to have semantic tags established on the defined terms for one or more ontologies. These notes enable structured search to be performed though the materials formed by learning objects. Traditional Web technologies are based on syntactic markup; implementation of services like semantic markup is promising on Semantic Web [5]. The Semantic Web is an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. The Semantic Web is a current project under the direction of Tim Berners-Lee of the World Wide Web Consortium to extend the ability of the World Wide Web by developing standards and tools that allow meaning to be added to the content of web pages. The goal of the Semantic Web is to create a universal medium for the exchange of data by allowing meaning to be given, using tools and tags, to the content within web pages. The main task of Semantic Web is "Expressing Meaning". In order to achieve this there are several layers in Semantic Web [24]. The following layers are the basic ones: Figure 1.1: Layers in Semantic Web XML layer - for representing data. RDF layer - for representing the meaning of data. Ontology layer - for representing the formal common agreement about the meaning of data. Logic layer - enables intelligent reasoning with meaningful data. The Semantic Web vision is based on two main ideas: addition of semantic markup to information resources on the Web and creation of intelligent services (agents) capable to understanding and operating with such resources at the semantic level. The Semantic Web introduces a 9

10 better semantic interoperability of web resources. Using the Semantic Web, we can easily find the existing learning material, understand their descriptions, locate related materials, etc [14]. In that way, Semantic Web improves learning object s reusability. Building the shared ontology using the Semantic Web tools (XML, RDF, and OWL etc) provides automation for the web services interoperation [10]. The discovery, management, and exchange of learning objects can be considerably simplified by providing standardized information on each learning object [29]. This information is called metadata. Metadata is data about data here about learning object, it facilitates the search, evaluation, acquisition, and use of learning objects by learners, instructors, or automated systems. If we have ontologically annotated learning objects metadata, it helps in finding relevant learning objects. Learning objects can further enhanced by providing ontology-based knowledge for their content. Using ontologies as shared vocabulary and semantically marking up the content of the learning objects, interoperability and reusability can be improved. So there can be two different kinds of ontologies for learning objects: Ontologies that describe learning objects metadata [7]. Ontologies that describe learning objects content and metadata. The Learning Object Metadata (LOM) standard provides a set of metadata elements for describing learning objects: this facilitates finding relevant learning objects [22]. If we want to use a part of learning object rather than whole, current approach is to copy and paste in order to reuse specifically those parts of the documents that are relevant. But this approach is tedious and time consuming. But it s advantageous if authors were released from the task of reusing the learning objects manually by automating the process as much as possible [14]. Therefore, we need a learning object content format that includes explicit definition of the structure of the learning object. So it s useful to develop an ontology that describes learning objects content. 1.2 Scope and Goal of Thesis 10 Teaching material can be anything that teachers can use to help learners learn. The teaching materials which are dealt here are lecture material and exercises which are designed for traditional classroom instruction and are available online. In this thesis, ontologies for the content and structure of the lecture material and exercises are developed. The aim of this thesis is to conceptualize teaching material.

11 Conceptualization: It is impossible to represent the real world, or even some part of it, with all details. To represent some phenomenon or part of the world, that we call domain, it is necessary to focus on a limited number of concepts that are sufficient and relevant to create an abstraction of the phenomenon in hand. Thus, a central aspect of any modelling activity consists of developing a conceptualization: a set of informal rules that constrain the structure of a piece of reality, which an agent uses to isolate and organize relevant objects and relations. A body of formally represented knowledge is based on a conceptualization: the objects, concepts, and other entities that are assumed to exist in some area of interest and the relationships that hold among them. A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose [16]. It contains the relevant concepts of that domain, relation between the concepts, and the axioms about these concepts and relations. The process of conceptualizing is crucial in design of computer support systems. Conceiving problems and forming ideas, and abstracting ideas from particular instances is the heart of the matter in both producing and communicating about designs. The main aim of building the model is not only to capture design information, but serves more importantly as a means to communicate and come to understand a design as it evolves. Conceptualization of teaching material is to create a model or a design for the teaching material, such simplification allows computer and human alike to communicate. Lecture material and exercises are learning objects in this model. Lecture materials which are considered here are materials which are used to explain about a topic in the class room, they are generally PowerPoint slides. Exercise sheet is a set of exercises and are intended to be done by students in order test and increase skill. The lecture material and exercises available online are discussed here. Ontologies for the content and structure of lecture material and exercises are to be developed;so that they can be used as shared vocabulary. The ontologies are developed using Semantic Web tools. The learning resources are annotated based on ontology. 1.3 Principal Results In this thesis, ontologies for the teaching material is developed. The ontologies are developed for the lecture material and exercises. There are several Semantic Web tools available to develop the ontologies. Some of them are compared and OWL is chosen for representing the ontologies [23]. The teaching materials are semantically marked up using it. The ontologies are developed using the Protégé-OWL libraries [27]. 11

12 If the applications share the common ontology of teaching material then the teaching material of one application can be used by another, it also provides intelligent integration such as sharing, searching and reusing information among applications. The ontologies of the teaching material can be used by authoring tool developers. Developing ontologies is an important aspect of the Semantic Web [36]. To be useful ontologies must be shared so that there is a common understanding among the learning object producers about what the terms mean. This increases the reusability and interoperability of the learning objects. Since it is likely that different group of people will use different ontologies for learning objects, mappings between these ontologies is also an important requirement 1.4 Organization of Thesis The thesis is organized as follows: Chapter 2 Learning objects and Metadata for Annotation of Learning Objects We begin with a general chapter on introductory chapter on learning objects and existing standards. In the first part of the chapter, learning objects are introduced and we mainly focus on reusability of the learning objects. In the second part of the chapter, existing technologies are explained briefly. We discuss about IEEE Learning Object Metadata (LOM) Draft Standard specification which was developed to provide structured metadata descriptions of Learning Objects in order to enable semantic interoperability among applications on the e-learning domain. This standard is also briefly discussed in this chapter. Some properties from this standard are used in our ontologies. Bloom s taxonomy for learning levels is used to classify the questions in our exercise sheets is also briefly explained in this chapter. Chapter 3 Ontologies and Representation Here ontologies are explained, and several other topics like components of ontology, different types of ontologies, about the design criteria of the ontologies and various applications of ontologies are discussed. And then several Semantic Web tools available for representing ontologies are introduced and they are compared. The comparison is mainly between OWL and Topic Maps. Chapter 4 Ontology for Teaching Material and Modelling with Protégé In this chapter the ontologies developed for the teaching material are dicussed. The ontologies of lecture material and exercises are explained. 12

13 Some properties from IEEE LOM which are adequate for the model and Bloom s taxonomy are also integrated in our model. They are also explained briefly here. Protégé an open source Java tool is used for developing the ontologies. A brief description about that software is also given in this chapter. Chapter 5 Conclusion In this chapter the results are summarized and the future work is presented. 13

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15 2 Learning Objects and Metadata for Annotation of Learning Objects Learning objects enable and facilitate the use of educational content online. Internationally accepted specifications and standards make them interoperable and reusable by different applications and in diverse learning environments. The metadata that describes learning objects facilitates searching and renders them accessible. In this chapter, the basics about learning objects and metadata for annotation of learning objects are covered. In the first part of the chapter, learning objects are introduced and we mainly focus on reusability of the learning objects. In the second part of the chapter, existing standards and metadata for annotation of learning objects are briefly explained. After this, the relevance and usage of the standards for this concrete application is discussed. 2.1 Learning Objects The term learning object is defined by Wiley [40] "A learning object is a digital learning resource that facilitates a single learning objective and which may be reused in a different context". Learning objects are a new way of thinking about learning content. Instead of providing all of the material for an entire course or lecture, a learning object provides material for a discrete lesson or sub-lesson within a larger course. Examples of learning objects include based on an electronic text, a simulation, a web site, a.gif graphic image, a Quick- Time movie, a Java applet or any other resource that can be used in learning. In general, learning objects: are self-contained each learning object can be consumed inde- 15

16 pendently, are reusable a single learning object may potentially be used in multiple contexts for multiple purposes on multiple campuses, can be aggregated learning objects can be grouped into larger collections, allowing for their inclusion within a traditional course structure, are tagged with metadata every learning object has descriptive information allowing it to be easily found by a search, which facilitates the object being used by others in the department in particular and the discipline in general. Learning objects allow for learning that is: Just enough if you need only part of a course, you can use the learning objects you need. Just in time learning objects are searchable; you can instantly find and take the content you need. Just for you learning objects allow for easy customization of courses for a whole organization or even for each individual. In this thesis, we are dealing with the conceptualization of teaching material and our main aim is to reuse the existing material rather than developing it again. Hence the reusability of the learning objects plays an important role, so we discuss about it in the following sections Reusability of Learning Objects All learning objects have certain qualities. It is the difference in the degree to which (or manner in which) they exhibit these qualities that makes one type of learning object different from another. The reusability of learning object also vary based on these qualities. A 5-layer taxonomy of learning objects proposed by Wiley is given below [40]: Fundamental learning objects are individual digital resources uncombined with any other. Combined-closed learning objects are simple combination of fundamental or combined closed objects. They typically mix different resources. The resulting structure could be complex but should not aggregate information to the content. Generative-presentation learning objects provide logic and structure for combining or generating and combining fundamental and combined-closed objects. 16

17 Generative-instructional learning objects provide logic and structure for combining fundamental, combined-closed and generativepresentation objects. Those objects support student interaction evaluation instructional strategy instantiation. A generative-instructional object should result in an instructional simulation capable of generating instruction, problems, and evaluating answers. Combined-open learning objects combine any types of object. They provide rich transitions between their components but do not modify them. Qualifying this taxonomy in terms of granularity, fundamental and combined-closed learning objects are fine-grained material while other learning objects are generally coarse-grained material. The reusability of learning object deals with the potential of the object to be reused. Educational material reusability is tied with its granularity. Next, we describe the reusability of each element of the taxonomy above. Generative-presentation and generative-instructional learning objects can be reused in different contexts - the variety of those contexts is proportional to components adaptability. However, those objects are as adaptable as they are hard and costly to build [40]. Combined-open learning objects have a limited scope of reuse since they are instructionally specific composition of material. Nevertheless, in a very similar context, this type of learning material can directly be reused. Consequently, the reuse of such learning objects could be automatic [41]. Combined-closed and fundamental learning objects have a good potential for reuse since their internal structure should not be domain specific. The paradox is that such fine-grained objects are still hard to reuse [42]. There is no standard for the size (or granularity) of a learning object. Larger learning objects are typically harder to reuse, and smaller learner objects save less work for those who are reuse them. Per the literature of pedagogy, the best medium has been estimated as between five and fifteen minutes of learning material Factors Effecting Reusability of Learning Objects There are several arguments for designing and developing material to be reused as learning objects, including the following: Flexibility. If material is designed to be used in multiple contexts, it can be reused much more easily than material that has 17

18 to be rewritten for each new context. It s much harder to uncouple an object from the context of its parent course and then recontextualize it than it is to contextualize as part of design and development. Ease of updates, searches, and content management. Metadata tags facilitate rapid updating, searching, and management of content by filtering and selecting only the relevant content for a given purpose. Customization. When individual or organizational needs require customization of content, the learning object approach facilitates a just-in-time approach to customization. Modular learning objects maximize the potential of software that personalizes content by permitting the delivery and recombination of material at the level of granularity desired. Interoperability. The object approach allows organizations to set specifications regarding the design, development, and presentation of learning objects based on organizational needs, while retaining interoperability with other learning systems and contexts. Facilitation of competency-based learning. Competency-based approaches to learning focus on the intersection of skills, knowledge, and attitudes within the rubric of core competency models rather than the course model. While this approach has gained a great deal of interest among employers and educators, a perennial challenge in implementing competency-based learning is the lack of appropriate content that is sufficiently modular to be truly adaptive. The tagging of granular learning objects allow for an adaptive competency-based approach by matching object metadata with individual competency gaps. Increased value of content. From a business standpoint, the value of content is increased every time it is reused. This is reflected not only in the costs saved by avoiding new design and development time, but also in the possibility of selling content objects or providing them to partners in more than one context. The ideal reusable learning object content is modular, free-standing, and transportable among applications and environments, nonsequential, able to satisfy a single learning objective, accessible to broad audiences (such that it can be adapted to audiences beyond the original target audience), 18

19 coherent and unitary within a predetermined schema so that a limited number of meta tags can capture the main idea or essence of the content and not embedded within formatting so that it can be repurposed within a different visual schema without losing the essential value or meaning of the text, data, or images. In recent years, the concept of a learning object has received considerable attention in e-learning. Because it can be very expensive and time-consuming to develop the content for an e-learning course. Being able to reuse learning objects created by others reduces the time and cost to develop learning materials. 2.2 Metadata for Annotation of Learning Objects Metadata helps people to organize, find, and use resources effectively. Adopting standard practices for metadata is part of a good information management policy. Using standard way of representing metadata then the metadata will be correctly understood and interpreted by others. The existing technologies IEEE LOM, Bloom s Taxonomy are briefly explained in this section. After this, the relevance and usage of these standards for this concrete application is discussed Learning Object Metadata Standard (LOM) Designers of online materials have a number of software tools to create learning resources. They are very useful in allowing learning resources creation that might otherwise require extensive programming skills. Nevertheless, common agreement upon standards is needed in order to design instructional material that can share common mechanisms to find and use it. The IEEE Learning Object Metadata (LOM) Draft Standard specification, approved on June 12, 2002 [22], was developed to provide structured metadata descriptions of learning resources called Learning Objects in order to enable semantic interoperability among applications on the e-learning domain. According to the LOM specification, a learning object is any entity, digital or nondigital, that may be used for learning purposes. Examples of learning objects are multimedia content, instructional content, learning objectives, instructional software and software tools, persons, organizations and events referenced during technology supported learning. This specification defines a conceptual model of the metadata structure in- 19

20 cluding a set of elements to be used in learning objects metadata descriptions, such as the element name, author, owner and prerequisites, but does not include information on how to represent these metadata in a machine-readable format. Rather, it is intended to be referenced by other standards that define such implementations. The purpose of the standard is to facilitate search, evaluation, acquisition, and use of learning objects, for example, for learners, instructors or automated software processes. Likewise, it is intended to facilitate the sharing and exchange of learning objects by enabling the development of catalogs and which the learning objects and their metadata are reused. Following is the detailed list of purposes of LOM [22]: to enable learners or instructors to search, evaluate, acquire, and utilize learning objects, to enable the sharing and exchange of learning objects across any technology supported learning systems, to enable the development of learning objects into units that can be combined and decomposed in meaningful ways, to enable computer agents to automatically and dynamically compose personalized lessons for an individual learner, to compliment the direct work on standards that are focused on enabling multiple learning objects to work together within an open distributed learning environment, to compliment the direct work on standards that are focused on enabling multiple learning objects to work together within an open distributed learning environment, to enable a strong and growing economy for learning objects that supports and sustains all forms of distribution; non-profit, not-forprofit and for profit, to enable education, training and learning organizations, governments, public and private, to express educational content and performance standards in a standardized format that is independent of the content itself, to provide researchers with standards that support the collection and sharing of comparable data concerning the applicability and effectiveness of learning objects, to define a standard that is simple yet extensible to multiple domains and jurisdictions so as to be most easily and broadly adopted and applied, to support necessary security and authentication for the distribution and use of the learning objects. 20

21 LOM Data Model The base schema of LOM was envisaged to be extended and represented in different syntax forms by different communities of users. By specifying a common conceptual data schema, the LOM Standard specification ensures that different representations of Learning Object Metadata compliant with the standard will have a high degree of semantic interoperability. The conceptual data schema of LOM groups metadata elements into nine categories intended to contain different kinds of metadata, named General, Life Cycle, Meta-Metadata, Technical, Educational, Rights, Relation, Annotation and Classification, whose purpose is [22]: The General category is intended to group the general information that describes the learning object as a whole; The Life Cycle groups the features related to the history and current state of the learning object; The Meta-Metadata groups information about the metadata instance used to describe the learning object; The Technical groups the technical requirements and technical characteristics of the learning object; The Educational groups the educational and pedagogic characteristics of the learning object; The Rights groups the intellectual property rights and conditions to use the learning object; The Relation groups features that define the relationship between the learning object and other learning objects; The Annotation category provides comments on the educational use of the learning object and provides information on when and by whom the comments were created; The Classification category describes the learning object in relation to a particular classification system. The following metadata items were also defined for each metadata element: name: the name by which the data element is referenced; explanation: the definition of the data element; size: the number of values allowed; order: whether the order of the values is significant; 21

22 example: an illustrative example. For leaf nodes on each hierarchy, the LOMv1.0 Base Schema also defines: value space: the set of allowed values for the data element, typically in the form of a vocabulary formed of a list of values or a reference to another standard in which the list is defined; datatype: indicates whether the values are a strings of characters (LangString), specifications of a point in time (DateTime), or the specification of an interval in time (Duration); vocabulary: indicates the structure of a vocabulary item Bloom s Taxonomy A taxonomy of learning levels was proposed by Benjamin Bloom. This taxonomy can be used to classify the questions. This is used in our model to classify the exercises. Each exercise is classified based on the level of difficulty. Such an classification helps when we want to search based on the level of difficulty. The following is a brief description of the taxonomy of learning levels, popularly known as Bloom s Taxonomy [6]. This taxonomy contains the following 6 learning levels: Knowledge: This is the ability to recall knowledge and information presented during an instruction. Being able to define domain testing-related terms such as equivalence class analysis, boundary value analysis and all 46 pairs combination is an example of this ability. This is not an intellectual ability. The next five learning levels require intellectual skills. Comprehension: This is the ability to understand and grasp the instructional material. The ability to understand what is meant by boundary values of a variable is an example of this learning level. Application: This is the ability to use the knowledge and skills learned during the instruction by putting it to practice in real scenarios or situations. Being able to identify variables of a real program and apply equivalence class analysis to the variables to come up with equivalence classes for the variable is an example of this sort of learning level. Analysis: This is the ability to see patterns, correlate different information and identify components of a problem. Being able to realize when just applying boundary value analysis is a good idea and when finding additional test cases based on special value testing is a better idea is an example of this kind of ability. Synthesis: This is the ability to use different pieces of information and put them together to draw inferences and possibly create 22

23 new knowledge and concepts. Being able to put all different concepts in domain testing together and correctly apply them to any given program or software is an example of this ability. Evaluation: This is the ability to make judgements about the knowledge acquired and concepts learned through an instruction. This is also the ability to compare the learned concepts with other similar concepts and make informed decisions about their value, perhaps even being able to determine to what extent the instructional material addresses the higher-level objectives of the instruction. Being able to evaluate the effectiveness of the domain testing method relative to other testing techniques or being able to judge when one method is more applicable than others to a situation is an example of the highest level in Bloom s taxonomy Summary Metadata plays an essential role, when the goal is to discover, exchange and reuse web-based learning material. Metadata is used in so many different ways and is so important for effectively searching out, organizing, and using learning resources that many different approaches have been developed. The use of standards for metadata representation for the educative domain to describe the background and meaning of learning objects is necessary to achieve semantic interoperability on the Web. In our model, the taxonomy of Bloom is integrated. The LOM metadata describes various properties of learning materials. According to the usage of the model, we must be decide which of the metadata data are relevant for the application in future. If all the metadata of the LOM standard are used for annotation of learning objects, the author, who makes the annotation must fill out a lot of data slots. For an effective annotation, only relevant metadata (according to the usage of learning object) should be described. In this thesis, LOM educational properties that were found to be adequate to represent elements of the teaching material ontology are used. These properties from IEEE LOM that are used in our model are described in chapter 4. 23

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25 3 Ontologies and Representation The World Wide Web is like a virtual library at one s fingertips; Web is also becoming an important medium for distribution of learning material. A large number of learning materials are available online. Learning materials of the courses offered at university are placed also available online. The contents of the courses offered at different universities are strikingly similar. Benefits from sharing and reusing of learning materials between applications are high. But we have a great problem to: find information about learning materials. easily reuse the existing material without producing new material all the time. A possible solution to this problem is the development of educational platforms where the annotation of learning material should be ontology based. Basics of ontologies like components of ontology, different types of ontologies, about the design criteria of the ontologies and also the various applications of ontologies are discussed in the first section. In the second part of the chapter Semantic Web tools for representation of ontologies are discussed. 3.1 What are Ontologies? The word "ontology" has a long history in philosophy, in which it refers to the subject of existence. Since Aristotle s time there has been an interest to represent the existing knowledge of the world with a methodology that identifies classes of objects with common properties in a hierarchical structure where some classes are specializations of others. This way to represent knowledge was called Ontology. 25

26 Research on ontology is becoming widely spread in computer science community. Ontology research has begun in early 90 s in the knowledge base community; the research activity has been accelerated by the Semantic Web movement in last few years. There are many definitions of an ontology in computer science. One of the most widely accepted definitions is the following by Gruber [18]: "An ontology is a formal, explicit specification of a shared conceptualization. The term is borrowed from philosophy, where an Ontology is a systematic account of Existence. For AI systems, what "exists" is that which can be represented. When the knowledge of a domain is represented in a declarative formalism, the set of objects that can be represented is called the universe of discourse. This set of objects, and the describable relationships among them, are reflected in the representational vocabulary with which a knowledge-based program represents knowledge. Thus, in the context of AI, we can describe the ontology of a program by defining a set of representational terms. In such an ontology, definitions associate the names of entities in the universe of discourse (e.g., classes, relations, functions, or other objects) with human-readable text describing what the names mean, and formal axioms that constrain the interpretation and well formed use of these terms. Formally, an ontology is the statement of a logical theory. A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose." Ontologies in Computer Science evolved from semantic networks [37] and were proven to be quite useful in representing and facilitating the sharing of the knowledge about a domain by human and automatic agents. Ontologies have been used in Configuration Systems, Software Engineering, Information Retrieval, Conceptual Modeling, Interoperability, Enterprise Modeling, Electronic Commerce, and many other fields in the research and production areas. Ontologies can be of different types depending on factors such as the domain intended to be modelled or the use for which they are constructed or the complexity they need to have. There are several classifications of Computer Science s ontologies, based on different parameters. These classification s help in deciding type of ontologies needed to be designed for any particular application. There are a lot of approaches for classification of ontologies.van Heijst, Schereiber and Wieringa classify them according to the the amount and type of structure of the conceptualization [21], Gomez-Perez, Fernáandez-Lopez and Corcho classify ontologies based on the level of specification of relationships among the terms gathered on the ontology [17]. Guarino classifies them by their level of generality as [20]: 26

27 Top-level ontologies, which describe domain-independent concepts such as space, time, etc., and which are independent of specific problems; Domain and task ontologies, which describe, respectively, the vocabulary related to a generic domain and a generic task; and, finally, application ontologies, which describe concepts depending on a particular domain and task. Fensel [13] take a slightly different approach at distinguishing types of ontologies. They make a distinction between static knowledge and problem-solving knowledge. The levels of generality distinguished for static knowledge ontologies correspond roughly to the levels distinguished by Guarino. In this thesis, the domain ontologies for lecture material and exercises are developed Ontology Components Despite the representation language being used, ontologies share a common set of characteristics in order to make knowledge representation and inference tasks possible. The main components of an ontology are concepts, relations, instances, axioms and ontology operations. Concepts: A concept (also called class or frame) is the description of the common features that a set of individuals have. A concept can be anything of which anything can be stated that could be relevant to the intended purpose of the ontology. It can be a physical or a digital object. An object can be a procedure description, a functionality, action or strategy, among others. The idea behind concepts may be viewed as similar to the idea behind classes in the object-oriented modelling paradigm. Each concept has an associated term as its name, a description in natural language, and a set of properties (also called slots or roles) that characterize it. Concepts can be defined by extension, i.e., enumerating their elements, or by intension, i.e., giving restrictions that their elements must maintain. Relations: Relations describe the interactions between concepts or a concept s properties. They are the basis for the hierarchical structure of the ontology. Relations also fall into two broad kinds: 1. Taxonomies that organize concepts into sub-super-concept tree structures. The most common forms of these are: 27

28 Specialization relationships commonly known as the is a kind of relationship. Partitive relationships describe concepts that are part of other concepts. 2. Associative relationships that relate concepts across tree structures. Commonly found examples include the following: Nominative relationships describe the names of concepts; Locative relationships describe the location of one concept with respect to another; Associative relationships that represent, for example, the functions, processes a concept has or is involved in, and other properties of the concept; Many other types of relationships exist, such as causative relationships. Such as Component-integral object composition, Material-object composition, Portion-object composition, Place-area composition, Member-bunch composition, Member-partnership composition, etc. Relations also have properties that capture further knowledge about the relationships between concepts. These properties can be used to express universitality, optionality, cardinality, transitivity, etc. of the relations between concepts. Axioms: Axioms contribute to specify the definition of the ontology elements constraining their interpretation. They state facts that must always hold which are useful to verify correctness on creation time or deducing new information on query time. Instances: Instances are the things represented by a concept. Strictly speaking, an ontology should not contain any instances, because it is supposed to be a conceptualization of the domain. The combination of an ontology with associated instances is what is known as a knowledge base. However, deciding whether something is a concept of an instance is difficult, and often depends on the application. Ontology Operations: Ontological representation languages enable the execution of a certain basic set of operations to cover updating and querying tasks on ontologies. The simplest queries an ontology can answer, despite the representation language used and the purpose it was constructed for, are: What are the individuals of a given concept? Given an individual, what are the concepts to which it pertains? Which individuals have a given value in a given property? 28

29 Which individuals are related to a given individual by a given property? Similarly, new concepts can be defined, properties related to concepts and values changed or added during the entire life of the ontology. At editing time, the consistency of the ontology can be automatically checked, for example, to reject a value that was intended to fill a property for a given concept if it is not in concordance with the restrictions defined on the property values for this concept. At query time, inference can be made by using explicitly stated facts and the ontology axioms to infer implicit new facts Design Criteria and Reasons for Developing Ontology A set of design criteria to help in the ontology design task is presented in [19]. They are: Clarity: An ontology should effectively communicate intended meaning, should be without any ambiguity by giving appropriate necessary and sufficient conditions. Coherence: An ontology should maintain internal consistency. At the least axiom definitions should maintain logical consistency. As axioms determine the competency of an ontology. Extendibility: Ontology should give a scope to extend the existing terms in such a way that it does not require much revision of existing definitions. Encoding bias: An encoding bias results when representation choi ces are made purely for the convenience of notation or implementation. This should be minimized because knowledge-sharing agents may be implemented in different representation systems and styles of representation. Ontological commitment: An ontology should make as few claims as possible about the world being modelled. The following are some reasons for developing an ontology: Sharing common understanding of the structure of information among people or software agents is one of the more common goals in developing ontologies. Enabling reuse of domain knowledge was one of the driving forces behind recent surge in ontology research. If one group of researchers develops an ontology in detail, others can simply reuse it for their domains. Additionally, if we need to build a large ontology, we can integrate several existing ontologies describing portions of the large domain. We can also reuse a general ontology, and extend it to describe 29

30 our domain of interest. Making explicit domain assumptions underlying an implementation makes it possible to change these assumptions easily if our knowledge about the domain changes. In addition, explicit specifications of domain knowledge are useful for new users who must learn what terms in the domain mean. Separating the domain knowledge from the operational knowledge is another common use of ontologies. We can describe a task of configuring a product from its components according to a required specification and implement a program that does this configuration independent of the products and components themselves. Analyzing domain knowledge is possible once a declarative specification of the terms is available. Formal analysis of terms is extremely valuable when both attempting to reuse existing ontologies and extending them. Often an ontology of the domain is not a goal in itself. Developing an ontology is akin to defining a set of data and their structure for other programs to use. Problem-solving methods, domain-independent applications, and software agents use ontologies and knowledge bases built from ontologies as data Application Areas for Ontologies The following are different areas where ontologies are useful: 1. Knowledge engineering, knowledge representation, knowledge management, knowledge sharing, knowledge integration. Knowledge Interchange Format (KIF) is designed as a framework for the interchange of knowledge among disparate programs and which comprehends modules designed to allow the representation of, for example, mathematical and physical information. The Ontolingua, the KIF translation language, is used as a working tool for the construction of medical ontologies. 2. Information retrieval and extraction. Common Web ontologies could in principle provide means to navigate diversity in a way which would involve not only producers but also the consumers of on-line information. 3. Natural language translation. One goal of applied information systems ontology is the provision of an Interlingua, a common target language for natural language translation which would alleviate the need to construct ad hoc translators for each pair of natural languages by constructing for each such language a single translator into the common target. 30