International Journal of Computer Science Research and Application 2013, Vol. 03, Issue.02, pp. 12-22 ISSN 2012-9564 (Print) ISSN 2012-9572 (Online) © Author Names. Authors retain all rights. IJCSRA has been granted the right to publish and share, Creative Commons 3.0

INTERNATIONAL JOURNAL OF COMPUTER SCIENCE RESEARCH AND APPLICATION www.ijcsra.org

A General Framework for Educational Ontologies Development Mihaela Oprea1 Petroleum-Gas University of Ploiesti, Department of Automatics, Computers and Electronics Bdul Bucuresti No 39, Ploiesti, Romania, +40244 573171,+40244 575847, [email protected] 1

Abstract An educational activity has as basic characteristic knowledge sharing. In the case of computer-based education, ontologies provide a way of representing knowledge that is shared. In web-based education and e-learning systems they give the semantics. Educational ontologies can model the content of a course for a full didactical activity cycle: teaching, learning and examination. Good educational ontology engineering requires the use of a methodology, framework or approach that structure and order the ontology development steps in a coherent, consistent and efficient manner. Several methodologies and frameworks were proposed in the literature for the design and development of educational ontologies. However, the majority of them are related to domain specific educational ontologies, and there is no unified view of them. In this paper it is presented a general framework for the development of educational ontologies of a course for all three phases of a full didactical activity cycle. A case study of applying the framework to courses from the Computer Science field is described.

Keywords: Educational ontology, Knowledge representation, Web-based education.

1. Introduction The development of efficient computer-based educational systems involves the use of ontologies as a formal way of knowledge representation from various sources (textbooks, human experts, research papers etc). Moreover, in the particular case of web-based education, ontologies provide the basic modality for knowledge sharing and exchanging. Starting from the general definition of an ontology given in (Gruber, 1995), we define an educational ontology as a conceptualization of a certain domain of expertise for an instructional activity (teaching, and/or learning, and/or examination). The design and development of an educational ontology requires the application of a methodology in order to have a good engineering of it. Several methodologies and frameworks were proposed in the literature, usually, each educational ontology that was developed having its own engineering method, under the form of a methodology, framework or approach. Some of the current research efforts in ontological engineering are directed to the description of a unified view for an ontology development methodology. In the case of educational ontologies such a description will increase the interoperability of the instructional systems. In this paper we propose a general framework, EduOntoFrame, for educational ontologies development in the case of a full didactical activity cycle (i.e. teaching, learning and examination). In our framework eight ontologies are generated for a course. The framework is an extension of the framework initially introduced in (Oprea, 2012). A case study of applying the framework to the development of educational ontologies for courses from the Computer Science field is described. The paper is organized as follows. A brief review of selected methodologies and frameworks that were proposed for educational ontologies development is presented in section 2. Our general framework is described in section 3. A case study of applying the framework to the courses of Object Oriented Programming, and

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Artificial Intelligence is presented in section 4. Finally, the section 5 concludes the paper and highlights the future work.

2. An overview on educational ontologies development methodologies The development of educational ontologies became an important step in the design of web-based education and e-learning systems. Such ontologies provide the solution for knowledge representation and interoperability of the educational systems. A good engineering of ontologies requires the use of a methodology or framework that structure and order the ontology development steps in a coherent, consistent and efficient manner. One of the first proposed methodologies for ontology development is presented in (Uschold & King, 1995). Since 1995, other several frameworks and methodologies were used for the development of ontologies, in general, and for educational ontologies, in particular. At present, there is no unified form of a methodology or framework with common used guidelines that should be followed when developing an ontology. However, various research efforts are directed on this topic. In this section we briefly present a selection of some frameworks and methodologies that were reported in the literature, focusing on the recent ones that were used for educational ontologies development. Also, we first reffer to some reviews of the methodologies proposed for ontology development, that were reported in the literature. An early work that made an overview of methodologies for building ontologies is described in (Fernández Lopéz, 1999). The author presents an analysis of some methodologies that were developed at that time in order to provide a proper selection of an ontology building methodology. A more recent review of existing methodologies for ontology creation is discussed in (Dahlem et al., 2009). The authors analyzed the major characteristics of some existing methodologies, and based on them, provided a unified view for the ontology creation. Other overviews are made in several research papers that propose new methodologies or approaches for ontologies development. We shall make reference to some of them in the next paragraphs. The use of several ontologies for the development of a web-based educational system is discussed in (Doan & Bourda, 2006). The authors propose the specification of the metadata semantics by using OWL, an ontology formal language. Some ontology development methodologies are discussed and analyzed in (Yun et al., 2009). The authors propose a knowledge engineering approach in order to build domain specific ontologies. They used the Hozo ontology editor for developing a C Programming ontology, and demonstrated the applicability of their approach for the development of e-learning course ontology. An educational ontology used to model web-based e-learning system for higher education is introduced in (Bucos et al., 2010). The ontology includes higher education specific concepts and can be used for specialized e-learning systems. A new ontology development methodology, MIOD, is proposed in (Leung et al., 2011). As a novelty, the methodology provides some guidelines for ontology merging and integration. In (Papasalouros et al., 2004) it is described a method for the design of educational adaptive hypermedia applications, based on UML modelling language, RDF encoding of the conceptual model, and RuleML. An automated tool for course content modelling, AIMTool, based on Java, is introduced in (Borges & Barbosa, 2009) for collaborative construction of the IMA-CID models: conceptual, instructional and didactic. The educational ontologies are used as a mechanism that supports the course content modelling. The use of educational ontologies to personalize the course resources according to the learners’ personality and preferences is made in (Kerkiri et al., 2010). The authors propose a methodology for knowledge management that apply recommendation algorithms and is used under the framework of an e-learning system. In (Silva et al., 2011) it is proposed a combined framework for building educational ontologies, based on methontology and a model driven architecture approach. An analysis of using ontologies in processes of collaborative learning and knowledge generation is made in (Allert et al., 2006). Several scenarios of ontology-based collaborative learning and knowledge creation are discussed. The great importance of using ontological engineering in teaching was discussed in an early research work described in (Gavrilova, 2003). In (Sosnovsky & Gavrilova, 2006) it is introduced an approach for practical ontology development and it is presented an educational ontology designed for teaching and learning the C programming language. The authors emphasize the importance of visual representation of an educational ontology. Under their approach an educational ontology has top levels, intermediate levels, and lower levels, in an hierachical view. A methodology for developing learning ontologies is proposed in (Kanellopoulos et al., 2006). For the ontology capturing it is used a method similar with that applied by the object oriented analysis and design methodology. The authors present also a brief overview of some ontology-based educational applications.

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An approach designed for educational ontologies development in higher education of economics is discussed in (Mesaric & Dukic, 2007). The results of another research work on educational ontologies development for knowledge evaluation in higher education are presented in (Kö et al., 2008). The authors propose an adaptive knowledge testing and evaluation system based on an educational ontology. The system was tested in several universities for the business informatics program. The OntoQue system, an engine for objective assessment item generation based on domain ontologies, is introduced in (Al-Yahya, 2011). The system evaluation was made on four OWL ontologies from different domains. Several methodologies proposed for educational ontologies development are discussed in (Fok & Ip, 2007). Based on this analysis, the authors propose PEOnto, a personalized education ontology that was developed by following a systematic ontology approach. An ontology-based framework for the formalization of computer-based collaborative learning scripts in the OWL language is introduced in (Papakonstantinou et al., 2007). The main purpose of the research work was to merge the fields of collaborative learning and semantic web. In (Salem & Cakula, 2011) it is presented the development of two web-based educational ontologies in the artificial intelligence technology area, the Artificial Intelligence in Education ontology and the Expert Systems ontology, both encoded in the OWL-DL format by using the Protégé-OWL editing environment. The authors propose the use of the two ontologies as an assessment procedure for students. The NeOn methodology framework is introduced in (Suárez-Figueroa et al., 2012) as a methodological guidance for ontology engineering. The authors propose nine scenarios as pathways for developing ontologies, that cover commonly occuring situations. Reusing and re-engineering knowledge resources are also tackled by the proposed methodology. An ontology model is proposed in (Chung & Kim, 2012), as well as an effective method for students learning effect enhancing through subject ontology construction. Based on the model it is developed an ontology-based e-learning support system that allow learners to build adaptive learning paths. In (Liu et al., 2008) it is proposed a framework for the automatically generation of adaptive feedback from metadata of items in educational ontologies. The framework can be used in computer assisted assessment systems. In (Boyce & Pahl, 2007) it is presented a method for developing educational ontologies by domain experts for use in the delivery of courseware content. The authors provided some ontological modelling guidelines that are adequate for rich domains, and took as application domain, the course of databases. A survey of several existing methodologies for ontology creation is made in (Todorova, 2007). Based on the survey the author proposes a methodology for ontology development. A semiautomatic framework, TEXCOMON is presented in (Zouaq & Nkambou, 2008), that produces domain concept maps from text, and then, derive educational ontologies from these concept maps. From the brief overview of the methodologies and frameworks that were proposed for ontologies development as well as for the more specific educational ontologies development, we can conclude that there is no methodology or framework widely adopted, they are rather specific to the application that is presented in the reported research work, or they are using adaptation of other computer science specific methodologies, such as the object oriented analysis and design methodology. Educational ontologies engineering is still a challenging research topic.

3. The EduOntoFrame educational ontology development framework We have designed a general educational ontology development framework, named EduOntoFrame, starting from the framework proposed in (Oprea, 2012) as a support tool for didactical activities of teaching, learning and examination. An educational ontology define all terms (i.e. concepts, properties, relationships) from the knowledge domain of a course. Also, as an extension, a set of axioms and reasoning rules are included in the ontology. The axioms constrain the possible interpretations for the defined terms and the rules are used during reasoning processes for solving problems from the course domain. In order to give a general framework for educational ontology development we have concentrated only on the set of terms that are defined by the ontology, ignoring the set of axioms and reasoning rules. The basic idea was that the development of an educational ontology can be done by using some general guidelines grouped under a framework that follow a full didactical activity cycle. Figure 1 shows the block schema of a course didactical activity cycle with the three phases: teaching, learning and examination, and their corresponding feedback. The educational resources are used in the three phases of the instructional process, and are based on the educational ontologies specific to the domain of study and some prerequisite courses. For example, in the case of the Object Oriented

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Programming course, the prerequisite courses are the Computer Programming Languages (e.g. the C standard language), Data Structures and Algorithms, and the Bases of Informatics or any other introductory course in Computer Science.

Figure 1: The block schema of a full course didactical activity cycle In our framework, the educational ontologies of a course include general and specific terms for all the three stages of the didactical activity. Some of the terms are domain independent, and are the basic notions from education (such as curriculum, syllabus, lesson structure, course module, pedagogical resource, pedagogical role, student / learner competences, teacher / instructor competences, exam, test, assessment etc). The main steps of the EduOntoFrame educational ontologies development framework for a given course are the following: 1) identify the main purposes of the educational ontologies related to the course; 2) educational ontologies generation (terms identification and organization, ontologies representation); 3) educational ontologies codification in a formal language by using an ontology editor; 4) educational ontologies testing; where, a term can be a concept, a property or a relationship, and step 2) is detailed in Figure 2. ALGORITHM Educational Ontologies Generation (EduOntoFrame) Input: course, prerequisite courses, student / learner, teacher / instructor Output: Educational Ontologies for the course and specific student / learner competences Begin 1. do Teaching Activity Ontologies Generation // for the teaching process 1.1 * extract all the basic and advanced concepts from the course and generate the Course Basic Subject Ontology and the Course Advanced Subject Ontology; 1.2 * extract all the concepts from the prerequisite courses and generate the Course Prerequisite Subject Ontology or use the existing ontologies of these courses; 1.3 * generate or use the Basic Teaching Ontology; // include teaching models 2. do Learning Activity Ontologies Generation // for the learning process 2.1 * extract from the course all the practical activities with the needed resources and the main competences achieved by the student / learner and generate Course Practical Activities Ontology; 2.2 * generate or use the Basic Learning Ontology; // include learning models 3. do Examination Activity Ontologies Generation // for the examination process 3.1 * extract from the course the tests, questions, exercises, problems, assessment items and generate the Course Examination Ontology; 3.2 * generate or use the Basic Examination Ontology; // include examination models End.

Figure 2: The generic algorithm for educational ontologies generation (EduOntoFrame)

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In step 1 the main purposes of the educational ontologies are identified, by taking into account the course and the type of didactical activity: teaching, learning, and examination. During step 2 the educational ontologies are defined for all three stages of the didactical activity. This step is formalized under the form of a generic algorithm and it is presented in Figure 2. During step 3 it is made the codification of the educational ontologies by using an ontology editor. In our case, we have used Protégé, a Java-based ontology editor. Step 4, educational ontology testing can be done by using the same ontology editor. As the framework core is represented by step 2, we shall focus on this one. The EduOntoFrame generic algorithm for educational ontologies generation provides eight educational ontologies (see Figure 3). Each phase of the instructional process has its specific educational ontologies, and at each phase, the ontologies of the previous phase or phases are also used. Between the eight educational ontologies there are specific links and relationships (e.g. between the Course Prerequisite Subject Ontologies of the courses that are member of the same program study curriculum). Apart from these educational ontologies, other ontologies can be used or generated depending on the specific course that is teached. TEACHING ACTIVITY

LEARNING ACTIVITY

Basic Teaching Ontology

Basic Learning Ontology

EXAMINATION ACTIVITY

Basic Examination Ontology

BS

AS

PS

Course Practical Activities Ontology

Course Examination Ontology

Course specific Ontologies - Basic Subject (BS) - Advanced Subject (AS) - Prerequisite Subject (PS)

Figure 3: The educational ontologies generated by the EduOntoFrame generic algorithm For the teaching activity, four educational ontologies are generated: Basic Teaching Ontology (contains terms specific to any teaching activity), Course Basic Subject Ontology (contains all basic terms specific to the course that is teached), Course Advanced Subject Ontology (contains all advanced terms, specific to the course that is teached), and Course Prerequisite Subject Ontology (contains all terms, basic and advanced, specific to a prerequisite course). For the learning activity, two educational ontologies are generated: Basic Learning Ontology (contains terms specific to any learning activity), and Course Practical Activities Ontology (contains terms specific to the course practical activities, i.e., laboratory or seminar). Finally, for the examination activity, two educational ontologies are generated: Course Examination Ontology (contains the terms specific to the current course examination activity), and Basic Examination Ontology (contains the basic terms common to all didactical examination activities). If previously generated ontologies can be used, only the new course specific ontologies are generated, the others, Basic Teaching Ontology, Basic Learning Ontology, Basic Examination Ontology, and Course Prerequisite Subject Ontology (i.e. the ontology of each prerequisite course) being used and not generated. We have simplified as much as possible the algorithm in order to have a general form of it. For example, the course has a detailed description with information regarding the content, the curriculum, the syllabus, the target audience, the teaching material, the software and hardware resources etc. In the next section it is described a case study of applying our framework to courses from the Computer Science field: Object Oriented Programming and Artificial Intelligence. The implementation of steps 3) and 4) were done in Protégé, one of the most used ontology editors.

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4. Case study We have taken as case study two courses from the Computer Science field, Object Oriented Programming (OOP), a fundamental course, and Artificial Intelligence (AI), a more specialized one, and we present the particularization of the proposed general framework to develop the corresponding educational ontologies. All eight ontologies are discussed in the case of the Object Oriented Programming course, while for the Artificial Intelligence course only the specific ontologies are discussed. I. Course title: Object Oriented Programming Course (in C++ language) - OOP OOP Prerequisite courses: Computer Programming Languages (including the standard C programming language), Data Structures and Algorithms, and an introductory course in informatics, e.g. The Bases of Informatics. The concepts of these three courses are used as known concepts when defining the concepts specific to the Object Oriented Programming course. Examples of terms from the Basic Teaching/Learning/Examination Ontologies are presented in Table 1. These terms are usually, common to all courses that are teached in universities. Table 1: Basic Teaching/Learning/Examination Ontologies Ontology

Terms

Basic Teaching Ontology

Basic Learning Ontology

Basic Examination Ontology

teaching model, interactive teaching, teacher competences, teacher feedback, course, course structure, course outline, course resource, course tutorial, lecture notes, readings, textbook, course document file, bibliography, reference, curriculum, syllabus, target audience, teaching goals, teaching tool, prerequisite knowledge, course chapter, subchapter, module, sub-module, section, sub-section, example, application, problem, course presentation, PowerPoint file, …

learner model, learning style, active reflective, sensing intuitive, visual verbal, interactive learning, learner feedback, learning goals, practical activity, laboratory work, student / learner competences, learning object, resource, FAQ, learned lessons, example, counterexample, problems, solutions, software, …

examination, test, assessment, self-assessment, items, assessment items, questions, answers, exercises, individualized exercises, corrections, problems, solutions, evaluation method, computer-assisted examination, examination goals, teacher feedback, learner feedback,…

Basic Teaching Ontology This ontology contains terms specific to any teaching activity. Examples of such terms are: teaching model, interactive teaching, course title, course duration, course structure, curriculum, syllabus, target audience, teaching goals, teaching tools, course content, course outline, course resource, educational unit, prerequisite knowledge, software, hardware, course chapter, sub-chapter, module, sub-module, section, sub-section, example, application, course presentation, course tutorial, lecture notes and readings, textbook, course document file (ASCII text, doc, html, audio, video, slide, pdf, ps etc), bibliography etc. Basic Learning Ontology This ontology contains terms specific to any learning activity. Examples of such terms are: learner model, learning styles, active reflective, sensing intuitive, visual verbal, interactive learning, learner feedback, learning goals, practical activity, student / learner competences, learning object, resource, FAQ, learned lessons etc. Basic Examination Ontology This ontology contains terms specific to any examination activity of an instructional process. Examples of such terms are: examination, assessment, self-assessment, assessment items, exercises, individualized exercises, questions, answers, items, corrections, tests, problems, solution, evaluation method, computerassisted examination, qualificative, mark, minimal requirements etc. In Table 2 we have included selected terms from the course specific ontologies (i.e. from the other five ontologies).

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Table 2: Object Oriented Programming Course Specific Ontologies Ontology Terms

Basic Subject Ontology

Advanced Subject Ontology

Prerequisite Subject Ontology

Practical Activities Ontology

Course Examination Ontology

class, object, abstraction, abstract data type, message, data member, function member, constructor, destructor, base class, derivative class, inheritance, object oriented modelling, …

multiple inheritance, polymorphism, function overriden, function overloading, object oriented design, object models, object modelling language, …

data type, data structure, function, function call, standard library, programming technique, algorithm, list, stack, queue, tree, graph, search algorithm, sort algorithm, …

object oriented problems solving, C++ language, defining classes in C++, defining the Stack class, defining the constructors of a class, software tool, Borland C++, Microsoft Visual C++, …

key concepts from the other four ontologies of the course, as well as problems and exercises specific terms; definition of a class, class syntax, example of abstract data type, …

1) OOP Course Basic Subject Ontology This ontology contains the basic notions of the object oriented programming with reference to the C++ programming language. The content of the course introductory chapters (modules) are represented by using this ontology. Some examples of introductory chapters are: Introduction in object oriented programming, Classes and methods, Inheritance. Some basic concepts are: class, object, object variable, abstraction, abstract data type, message, class section, private, public, protected, data member, function member, method etc. 2) OOP Course Advanced Subject Ontology This ontology contains advanced notions of the object oriented programming with reference to the C++ programming language. The content of the course advanced chapters are represented by using the Course Basic Subject Ontology and this ontology. Examples of such chapters are: Polymorphism, Multiple inheritance, Object Oriented Modelling. Some advanced concepts are: polymorphism, multiple inheritance, function overriden, function overloading, object models, object oriented modelling language, UML (Unified Modelling Language) etc. 3) OOP Course Prerequisite Subject Ontology This ontology contains all terms from the prerequisite courses that are necessary for defining the concepts specific to the Object Oriented Programming course. Examples of prerequisite concepts are: statement, sequence, decision, selection, iteration, compound statement, expression, program structure, data type, variable, function, function call, programming technique, algorithm, data structure, list, stack, queue, tree, binary tree, graph, search algorithm, sort algorithm, quick sort algorithm etc. 4) OOP Course Practical Activities Ontology This ontology contains terms specific to the learning activity structured in practical activities (laboratory or seminar) that corresponds to the chapters or modules of the course. Example of such terms from the practical activities of the Classes and methods chapter are as follows: defining classes in C++ (class syntax), sections of a class (public, protected, and private), class members, defining the Stack class, defining the constructors of a class (default constructor, copy constructor, constructors with list of parameters, type conversion constructor), C++ language, Borland C++, Microsoft Visual C++, inline functions, static members, static data, static functions, compound classes etc. 5) OOP Course Examination Ontology This ontology contains terms specific to the examination activities that corresponds to the teaching and practical activities of the Object Oriented Programming course. The key concepts from the previous defined course educational ontologies are used. Additional terms are related to problems and exercises proposed to be solved by students. An example of problem is the following: “Write a C++ program that define the ElectronicEquipment base class and the PersonalComputer derivative class with data members and functions members at your choice (defining at least one constructor in each class), declare two objects from the two classes in the main function, and call their corresponding methods”. The second example from our case study is the Artificial Intelligence course for which we shall briefly present the specific educational ontologies.

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II. Course title: Artificial Intelligence Course - AI AI Prerequisite courses: a computer programming course such as Logic Programming (Prolog language), Computer Programming Language (C language), Object Oriented Programming (C++ or Java), Data Structures and Algorithms, an introductory course in Computer Science, an introductory course in Databases. In Table 3 we have included selected terms from the course specific ontologies. Table 3: Artificial Intelligence Course Specific Ontologies Ontology

Terms

Basic Subject Ontology knowledge, inference, deduction, induction, artificial intelligence, artificial intelligence based system, intelligent system, reasoning, Turing test, symbolic calculus, connectionist models, symbolic models,…

Advanced Subject Ontology

Prerequisite Subject Ontology

Practical Activities Ontology

Course Examination Ontology

knowledge based system, expert system, knowledge base, inference engine,rule, fact, rule base, facts base, expert knowledge, heuristics, explanation module, learning module, decision tree, decision table, knowledge extraction, data mining, expert system generator, VP-Expert, H-Expert, informed search strategy, Best First strategy, A* strategy, AO* strategy, …

data, information, knowledge, data base, data structure, algorithm, tree, graph, sort algorithm, search algorithm, search strategy, uninformed strategy, logic programming, Prolog, programming language,…

knowledge modelling, knowledge base design, knowledge base implementation, expert system design for a specific domain of expertise, expert system implementation, expert system generator, VP-Expert, problems solving by informed search, …

key concepts from the other four ontologies of the course, as well as problems and exercises specific terms; design and implement a knowledge base, find the optimal solution of a problem with an informed strategy, …

1) AI Course Basic Subject Ontology This ontology contains the basic notions of the artificial intelligence domain. Examples of basic concepts are: knowledge, inference, deduction, induction, reasoning, artificial intelligence based system etc. 2) AI Course Advanced Subject Ontology This ontology contains advanced notions of the artificial intelligence domain with reference to the following AI sub-domains: knowledge based systems, expert systems and informed search strategies. The content of the course advanced chapters are represented by using the Course Basic Subject Ontology and this ontology. Examples of such chapters are: Knowledge based systems, Expert systems, Informed search strategies. Some advanced concepts are: knowledge based system, expert system, knowledge base, inference engine, heuristics, rule base, facts base, informed search strategy, A* strategy etc. 3) AI Course Prerequisite Subject Ontology This ontology contains all terms from the prerequisite courses that are necessary for defining the concepts specific to the Artificial Intelligence course. Examples of prerequisite concepts are: data, information, data structure, tree, graph, algorithm, search algorithm etc. 4) AI Course Practical Activities Ontology This ontology contains terms specific to the learning activity structured in practical activities (laboratory) that corresponds to the chapters or modules of the course. Examples of such terms from the practical activities of the Expert systems chapter are as follows: knowledge modelling, expert system generator, VP-Expert, problems solving by informed search strategy etc.

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5) AI Course Examination Ontology This ontology contains terms specific to the examination activities that corresponds to the teaching and practical activities of the Artificial Intelligence course. The key concepts from the previous defined course educational ontologies are used. Additional terms are related to problems and exercises proposed to be solved by students. An example of problem is the following: “Design and implement in VP-Expert a knowledge base for the technical diagnosis of a personal computer”. We have implemented the two ontologies in Protégé, a Java based ontology editor. In Figure 4 it is shown a screenshot with some classes (basic and advanced concepts) of the Object Oriented Programming course ontology, OOP_Ontology, implemented in Protégé 3.0. A screenshot from the Artificial Intelligence course ontology, AI_Ontology, is shown in Figure 5.

Figure 4: A screenshot from the Object Oriented Programming course ontology (in Protégé 3.0)

Figure 5: A screenshot from the Artificial Intelligence course ontology (in Protégé 3.0)

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In this case study, we have focused on the vocabulary of the educational ontologies of the two courses. However, when defining ontology apart from the vocabulary, the relationships between the concepts and the axioms of the ontology are also specified. Examples of relationships used between the concepts of an ontology are as follows: is_a, a_kind_of, has, part_of, order, required_by, defined_by, explained_by etc. The educational ontologies can be implemented in any ontology editor such as Protégé, Ontolingua etc. They are used either directly by the main actors of the instructional process (i.e. teacher / instructor, student / learner, tutor) or indirectly, by the e-learning platform or other intelligent instructional support tools (e.g. knowledge based system, multi-agent systems) during the teaching, learning and examination phases of the didactical process.

5. Conclusion The paper proposed a general framework, EduOntoFrame, for the development of educational ontologies for a full course didactical activity cycle with the three phases of teaching, learning and examination. In our framework eight ontologies are generated for a course, five educational ontologies that are course dependent (course basic subject, course advanced subject, course prerequisite subject, course practical activities, and course examination), and three general ontologies, specific to the three phases of any didactical activity (basic teaching, basic learning, and basic examination). A case study of applying the proposed framework to the courses of Object Oriented Programming and Artificial Intelligence from the Computer Science field was also presented.

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Liu, B., Chen, H., He, W., 2008, A Framework of Deriving Adaptive Feedback from Educational Ontologies, Proceedings of the 9th International Conference for Young Computer Scientists, pp. 2476-2480 Mesaric, J., Dukic, B, 2007, An Approach to Creating Domain Ontologies for Higher Education in Economics, Proceedings of the International Conference on Information Technology Interfaces, pp. 75-80 Oprea, M., 2012, On the Use of Educational Ontologies as Support Tools for Didactical Activities, Proceedings of the International Conference on Virtual Learning, pp. 67-73 Papakonstantinou, A., Demetriadis, S., Bassiliades, N., 2007, Ontology Development for Computer-Supported Collaborative Learning Scripts, Proceedings of the Balkan Conference on Informatics, pp. 491-500 Papasalouros, A., Retalis, S., Papaspyrou, N, 2004, Semantic Description of Educational Adaptive Hypermedia based on a Conceptual Model, Educational Technology & Society, vol. 7, no. 4, pp. 129-142 Protégé: http://protégé.stanford.edu Salem, A.-B.M., Cakula, S., 2011, Using Ontological Engineering for Developing Web-Based AI Ontology, Recent Researches in Communications, Information Science and Education, pp. 220-225 Silva, M., Elias, E., Costa, E., Bittencourt, I.I., Barros, H., da Silva, L.D., da Silva, A.P., Véras, D., 2011, Combining Methontology and Ontology Driven Approach to Build an Educational Ontology, IEEE Multidisciplinary Engineering Education Magazine, vol. 6, no. 3, pp. 11-18 Sosnovsky, S, Gavrilova, T., 2006, Development of educational ontology for C-Programming, International Journal Information Theories & Applications, vol. 13, no. 4, pp. 303-308 Suárez-Figueroa, M.C., Gómez-Pérez, A., Fernández-López, M., 2012, The NeOn Methodology for Ontology Engineering, Chapter in: Ontology Engineering in a Networked World, Springer, pp. 9-34 Todorova, K., 2007, Towards a Methodology for Ontology Development, In M. Iskander (ed.), Innovations in E-learning, Instruction Technology, Assessment and Engineering Education, pp. 205-210 Uschold, M., King, M., 1995, Towards a Methodology for Building Ontologies, research report AIAI-TR-183, University of Edinburgh Yun, H.-Y., Xu, J.I., Wei, M.-J., Xiong, J., 2009, Development of domain ontology for e-learning course, Proceedings of the IEEE International Symposium on IT in Medicine & Education, pp. 501-506 Zouaq, A., Nkambou, R., 2008, Building Domain Ontologies from Text for Educational Purposes, IEEE Transactions on Learning Technologies, vol. 1, no. 1, pp. 49-62

A Brief Author Biography Mihaela Oprea received her MSc degree in Computer Science from University Politehnica Bucharest in 1990 and her PhD degree in Automated Systems from Petroleum-Gas University of Ploiesti in 1996. Currently, she is a full professor at the Automatics, Computers and Electronics Department of the Petroleum-Gas University of Ploiesti, Romania. Her main research interests include machine learning, pattern recognition algorithms, knowledge modelling, applications of multiagent systems and artificial intelligence techniques in various domains such as engineering, education, and environmental protection. She has published 10 books and over 80 papers in the field of artificial intelligence in international journals and conferences proceedings.

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