ONTOLOGY AND TAXONOMIES FOR eLEARNING

Abstract

In a lexical universe where the acronyms are gaining more ground and the technical jargon is becoming more comprehensive, while developments in information and communications technology (ICT) require a rapid amending of terms - the need for clarification becomes obvious regarding the studied domain's ontology and taxonomy - in this case ICT-based distance learning (eLearning).

Headnote

Abstract: In a lexical universe where the acronyms are gaining more ground and the technical jargon is becoming more comprehensive, while developments in information and communications technology (ICT) require a rapid amending of terms - the need for clarification becomes obvious regarding the studied domain's ontology and taxonomy - in this case ICT-based distance learning (eLearning).

Keywords: TIC, ontologie, taxonomie, educatie, eLearning, modele ontologice.

"Informatics not only restores unity of applied and pure mathematics, of the solid techniques and abstract mathematics, but also that of natural sciences, humanities and society. It revives the concepts of abstract and formal and reconciles art with science, not only within the scientist's soul, where they were always at peace, but also in their philosophy."

Gr. Moisil (1906 - 1973)

Academician, Professor, College of Mathematics, Bucharest - founder of the Romanian logics and science school.

Since the time of Aristotle interest was manifested to define and classify knowledge about the world through the use of methodologies for structuring hierarchically classes and subclasses of objects with common properties. This representation is called an ontology. One of the most cited definitions of ontology defines it as: "a formal and explicit specification of a generally accepted conceptualization"1. Another definition is given by Studer, Bejamins and Fensel in 1998: "An ontology may take a variety of forms, but will necessarily include a vocabulary of terms and the specification of their meaning. This is a domain's semantic sphere generally accepted by several parties, facilitating communication accuracy and efficiency of transmitting the messages, which in turn ensures the development of other benefits such as interoperability, reuse and sharing of knowledge."2

Ontologies in ICT have developed due to the need to facilitate access to the representation and cognitive content of a field. Ontologies are now used in a wide range of ICT areas, from systems' configuration and computer engineering to commerce. Regardless of the representation language used, ontologies present a common set of characteristics that make processes of representation and cognitive inference possible3.

Development of educational ontologies

In the educational field, one of the first researchers interested in the development of cognitive functions in the learning process was Benjamin Bloom. His work from the mid-1900s - "Taxonomy of Educational Objectives: The Classification of Educational Goals. Handbook I: Cognitive Domain", although called taxonomy, it goes beyond the taxonomic level and gets into the scope of ontology by defining various learning and the relationships between them.

Bloom's educational taxonomy4 (Figure 1), first proposed in 1956, grouped educational objects and objectives in three key areas: cognitive, affective and psychomotor, stressing the need for a comprehensive approach to the educational process. The ontology developed by Bloom covers, however, only traditional education (face-to-face), as during 1950s this was the main educational model implemented.

With the development of the distance education via postal mail, radio and television and Bloom's taxonomic and ontological model has been constantly updated to meet the new developments. As Toffler anticipated since the 1970s, the new educational technology not only encourages standardization, but rather leads to a post-industrial diversity5 illustrated by the wide variety of tools / technologies used as support for the development of distance learning (Figure 2.)

The development of the concept of knowledge society in the 21st century led to the implementation of interdisciplinarity in most industries6. In this new paradigm, education is seen less and less as a stand-alone system and more as an interdisciplinary7 system in which educational theories and practices, and elements of the ICT domain share the same importance. Educational technologies led to the improvement and repositioning of distance learning, transforming it into a modelconcept within the knowledge-based society paradigm and while the traditionalist approach, which limits the boundaries of education to f-2-f teaching / learning, has been replaced by a more flexible and open approach where technologies were bivalent in the educational process, being both the instrument and the medium.

The theories discussing the importance communication has acquired in contemporary society were synthesized by McLuhan's theory in his famous phrase "the medium is the message"8. McLuhan demonstrates that the environment used for communication determines the perception, mentality and cultural activity of users, with profound consequences on life and social structure9.

Education is a communicational process by nature, which aims for the transmission of a message via a communication channel, mainly of a cognitive content from a transmitter / carrier of information to a user / student involved in the process of knowledge. Therefore the overlapping of the message with the medium leads us to conclude that electronic media has gained the valence of cognitive content.

Thus, researchers in ICT and eLearning understood the importance of building effective relationships between educational practice, research and theoretical foundation and stressed the need to understand the educational process in the cultural, economic, political and social context of the reality of which it belongs to. This trend is also reflected by the increased interest in ontological models, thus Bloom's taxonomic and ontological model was used by a significant number of researchers and experts in education, updated and supplemented with new terms, classifications and definitions relevant to the new paradigm - the knowledge-based society.

The indisputable influence of ICT on the learning process, now acknowledged and used in all areas of activity, changed learning as Toffler [2000] predicted almost four decades ago - the most part of the study occurs at the student's home, whenever the student wants10.

Acquisition, integration and application of knowledge in practice are essential elements of an effective management of knowledge-based content distribution and cognitive quick access.

ELearning is one of the most effective tools for knowledge management. Information architects in collaboration with experts in teaching11 are those who, using various tools of representation of the cognitive content, build ontological and taxonomical structures necessary for the proper functioning of such a system of knowledge management.

In the sphere of eLearning, ontologies may be developed in two directions. On the one hand, ontologies are constructed to define the studied field, its evolution and utility, the objects belonging to it, and the structures and relationships that are created among these objects, within the field. On the other hand, ontologies aim to define more precisely the meaning and context of terms used to describe entities and realities specific to the field in order to create a common vocabulary for all actors involved, terms which are then categorized and prioritized taxonomically.

The main reasons for building ontological and taxonomical systems are12:

- Establishing a common understanding of the structure of information transmitted both for software applications and among software developers and users;

- Facilitate the use and re-use of the knowledge in the field;

- Facilitate the understanding of the field axioms;

- Distinction between nominal and operational knowledge;

- Clear definition and analysis of knowledge in the field;

- Increasing the interoperability between different fields of knowledge;

- Improve the scalability of field knowledge;

- Facilitating the identification of specific elements in the field.

In practical terms, developing an ontological and taxonomical system includes:

- Defining concepts / classes of domain-specific objects;

- Taxonomic hierarchy of concepts / classes (sub-classes vs. overclasses);

- The definition of variables and accepted values for these variables;

- Metrics system in concrete cases by assigning exact values of variables.

Ontological models for eLearning

There are many authors who analyze the significance of ontology and taxonomy in ICT and eLearning, who propose and study different types of ontologies.

Guarino13 and Muñoz14 discuss the role a well-defined ontology (Figure 3) can play in a computer system and argue in favor of the architectural perspectives where ontology plays a central role in the development and operation of the system.

According to Devambu et al.15, ontologies can serve as the essential foundation of the development of software applications, supporting software developers by linking knowledge within the field of the application with already developed software components, to facilitate their reuse.

Stojanovic, Staab and Studer16 believe that the role of an ontology is mainly to describe the generally accepted meaning of a formal set of symbols for use in a specific field. More precisely, an ontology provides a map of the possible relationships between symbols and their meaning.

Regarding cognitive eLearning content, the problem of effective communication of meanings is structured on three levels that describe certain aspects of the use of educational content (Figure 4).

Furthermore, the same authors17 observe that in the eLearning environment there is the risk two or more authors of educational content refer to the same element or issue using different terms. This means that they can use semantically identical concepts expressed in different terms, even though both terms belong to the domain jargon. Using semantically equivalent terms can create confusion and problems in understanding the message18. For example, the following terms are semantic equivalents of the concept of agent: representative, spy, active factor, provoking factor, participant, microorganism, but their use depends on the context and often these terms, even if all are semantically to "agent", they are not interchangeable.

The problem could be solved by integrating two elements in the ontology:

1. A vocabulary of the field that provides users with a map of possible relationships between different terms and their meaning;

2. An axiom establishing that two terms or two symbols are in a twoway equivalence relationship if and only if both check clarity and coherence of the message sent.

The search for information is the process by which individuals seek to find data to clarify or confirm their knowledge of a particular topic. The underpinning strategy for information search about a particular topic in within educational content relies mainly on finding answers to simple questions, such as how? or where?.

The data search process via web is usually a complicated one. Much of the information found is either inaccurate, biased or outdated. One of the main challenges that students face is filtering irrelevant documents from the results provided by search engines. One example relates to how classic search engines (Google, Yahoo, Bing) operate the search for text in the enormous amount of documents indexed. They seek only words or groups of words requested by the user, and most documents that contain synonyms of these terms will not be identified as relevant by the search engine, while many irrelevant documents from the point of view of the user would be deemed relevant by the search engines. On the other hand, search engines that rely on ontologies find documents associated to key terms used in the search, even if the user searches for a term that the ontology considers synonymous or associated19.

Ontological relations are also used in navigating the educational content. In this respect, an approach to ontology-based search of data allows users / students to express more clearly the need for information and to streamline their navigation style. This involves user interaction with the ontology concepts and relationships through a dialogue which could be interpreted as a search or which may serve to suggest various possible further choices20.

Building a common ontology for the entire Web is an almost impossible task, but if we would focus on a particular area, we could specify concepts or relationships that provide the basis for knowledge dissemination21.

Thus, from the point of view of the user there is the issue of choosing keywords when searching for educational content. In this case, students need to be provided with a technology that would allow them to find relevant data on a given topic, and not just disparate data such as those provided by traditional search engines.

Simple search only by keywords, applies only in situations where users have a clear idea of what they want and the information is well defined. This approach is insufficient for eLearning, where the knowledge and perspectives of content creators and users could be totally different.

This raises the obvious need to create mechanisms for establishing

On the other hand, usually a simple search by keywords will be vitiated, it will not show results for synonyms (eg. Agent / organism) after abbreviations (eg. World Wide Web / www), after the word counterparts in other languages (eg. house - English / haus - German) and often will not provide results after morphological variations (eg. peer to peer / peer2peer), especially since the search ignores the context.

This problem is solved by defining ontological relations of correlation22 (synonyms, abbreviations, acronyms, word families, jargon, etc.). Thus, in navigating the educational content, whether the terms network and protocol there is a well-established ontological relationship, the search results for any of the two terms will contain references to both terms.

In the context of the knowledge society, educational contents must be linked / related (at least through hypertext links) to ensure ontological unity, a greater diversity of search results and improved access to the knowledge of all fields studied. Ontologies are used in smart integration of information as metadata23. Thus, they allow, for example, semantic search for content in databases without requiring the user to know the database structure.

According to Zimmermann, Mimkes and Kamke24, within the ICT paradigm, ontologies represent a digitized semantic description of a concept, having as main defining elements the encoding and the logical structure. They believe ontologies should be implemented as a network of meanings, where heterogeneity is the basic condition. Thus, an ICT ontology helps the codification and logical structuring of existing knowledge so it can be digitized and then accessed efficiently.

The same authors define the ontologies as ordered lists composed of four elements:

- A set of concepts (C)

- A set of relationships (R)

- A set of constants (I - Instances)

- A set of axioms (A).

So, mathematically speaking, a certain ontology can be described as follows25:

In conclusion, eLearning approaches can be structured depending on the technology used and on the educational purposes and methods26.

Thus, within the technology centered approach eLearning can be defined as:

- online - involves the use of both information and communication technologies;

- offline - involves using only information technology.

Content-centered approach

This approach presents the eLearning elements from the perspective of the developers and providers of eLearning platforms, who consider the courses and teaching materials as forming the core concept of eLearning27. They focus on the ergonomics of the platforms, and for this they continuously update the software and make infrastructure improvements.

User-centered approach

This approach has as a central core the student / user, who allegedly has already acquired the necessary experience and skills to use multimedia and information systems and also holds a degree of knowledge of the fields that she/he wants to learn more about.

In this case, when accessing educational content, the user's goal will be to acquire advanced skills and knowledge specific to the selected field. For the students / users to choose elements of the educational content that best suit their training needs, a detailed, well-structured and easily accessible description of the entire content has to be provided. For the user, the accessibility of the content is the most important.

It is generally considered that students ignore teacher's names, as long as their need for information / learning is satisfied. In this context, it is recommended to establish a personal profile for each user where to store specific information about his/her professional needs and aspirations so that the platform could subsequently extract, filter and deliver the right information, according to a preset algorithm28.

Footnote

1 Gruber, T.R., A translation approach to portable ontology specifications, în Knowledge Acquisition, nr. 5(2), 1993

2 Studer, R., Benjamins, V.R., Fensel, D., Knowledge Engineering, Principles and Methods, în Data & Knowledge Engineering 25(1-2), March, 1998.

3 Silva Muñoz, L., Ontology Based Metadata for e-learning Content, Federal University of Rio Grande do Sul, Porto Alegre, Brasil, 2004.

4 Bloom et al., Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain, 1956.

5 Toffler, A., Future Shock, Political Publishing House, Bucharest, 1973.

6 L. Butum, S. Stan, A. Zodieru, Development of new capacities forresearch and teach/learn tools in higher education, using the new financing funds in Romania, Proceedings of EDULEARN15 Conference, 6th-8th July 2015, Barcelona, Spain, ISBN 978-84-606-8243-1, p. 3674.

7 Quillian, M., Word Concepts: A Theory and Simulation of some Basic Semantic Capabilities, în Behavioral Science nr. 12, 1967.

8 McLuhan, M., Fiore, Q., The Medium is the Message, Gingko Press, 2005.

9 McLuhan, M., Powers, B., The Global Village: Transformations in World Life and Media in the 21st Century, Oxford University Press, 1988.

10 Toffler, A., cited works.

11 Sarker, B.K., Wallace, P. Gill, W., Some Observations on Mind Map and Ontology Building Tools for Knowledge Management, 2007.

12 McGuinness, D.L., Wright, J., Conceptual Modeling for Configuration: A Description Logic-based Approach, în Artificial Intelligence for Engineering Design, Analysis, and Manufacturing - special issue on Configuration, 1998.

13 Guarino, N., Formal Ontology and Information Systems, in N. Guarino (Ed.) Formal Ontology and Information Systems, IOS Press, Amsterdam, 1998.

14 Silva Muñoz, L., Moreira de Oliveira, J.P., Applying Semantic Web Technologies to Achieve Personalization and Reuse of Content in Educational Adaptive Hypermedia Systems, 2005.

15 Devambu, P., Brachman, R.J., Selfridge P.J., Ballard L., LASSIE: A Knowledge-Based Software Information System, in Communications of the ACM 34(5), 1991.

16 Stojanovic, L., Staab, S., Studer, R., E-learning Based on the Semantic Web, 2002.

17 Stojanovic, L., Staab, S., Studer, R., cited works.

18 Evans, T.D., Nation, D.E., Critical Reflections on Distance Education, London, Flamer Press, 1989.

19 Desmontils, E., Jacquin, Indexing a Web Site with a Terminology Oriented Ontology - prezentat la International Semantic Web Working Symposium (SWWS 2001), Stanford, CA, USA, july 30 - august 1, 2001.

20 Freitas, V., Autoria Adaptativa de Hipermídia Educacional, Instituto de Informática, UFRGS, Porto Alegre, 2002.

21 Evans, T.D., Nation, D.E., Educational Technologies: Reforming Open and Distance Education, London, Cogan Page, 1993.

22 Gruber, T.R., cited works

23 Musen, M.A., Ontology-Oriented Design and Programming, in Cuena, J., Demazeau, Y., Garcia, A., Treur, J. (Eds.), Knowledge Engineering and Agent Technology. IOS Press, Amsterdam, 2004.

24 Zimmermann, K., Mimkes, J., Kamke, H.U., An Ontology Framework for E-learning in the Knowledge Society, 2005.

25 Kasai, T., Yamaguchi, H., Building an Ontology of IT Education Goals, în International Journal of Engineering Education and Lifelong Learning, Vol. 16, No. 1/2, 2006.

26 Marzano, R.J., Kendall, J.S., The New Taxonomy of Educational Objectives, Thousand Oaks, CA: Corwin Press, 2006.

27 Ivan, Loredana and Frunzaru, Valeriu, 2014, "The Use of ICT in Students' Learning Activities", Journal of Media Research, vol. 7, no. 1-2, p. 6.

28 Anderson, L.W., & Krathwohl (Eds.), A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom's Taxonomy of Educational Objectives, New York: Longman, 2001.

References

REFERENCES

Bloom et al., (1956), Taxonomy of Educational Objectives: The Classification of Educational Goals. Handbook I: Cognitive Domain.

Butum L., Stan S., Zodieru A., (2015), Development of new capacities for research and teach/learn tools in Higher Education, using the new financing funds in Romania, Proceedings of EDULEARN15 Conference, 6th-8th Jul, Barcelona, Spain, ISBN 978-84-606-8243-1

Desmontils, E., Jacquin, (2001), Indexing a Web Site with a Terminology Oriented Ontology - presented at the International Semantic Web Working Symposium (SWWS 2001), Stanford, CA, USA, july 30 - august 1.

Devambu, P., Brachman, R.J., Selfridge P.J., Ballard L., LASSIE, (1991), A knowledge-based Software Information system, in Communications of the ACM 34(5)

Evans, T.D., Nation, D.E., (1989), Critical Reflections on Distance Education, London, Flamer Press.

Evans, T.D., Nation, D.E., (1993), Educational Technologies: reforming open and distance education, London, Cogan Page.

Freitas, V., 2002, Autoria Adaptativa de Hipermídia Educacional, Instituto de Informática, UFRGS, Porto Alegre.

Gruber, T.R., (1993), A Translation Approach to Portable Ontology Specifications, in Knowledge Acquisition, no. 5(2).

Guarino, N., (1998), Formal Ontology and Information Systems, in N. Guarino (Ed.) Formal Ontology and Information Systems, IOS Press, Amsterdam.

Ivan, Loredana and Frunzaru, Valeriu, (2014), The Use of ICT in Students' Learning Activities, Journal of Media Research, vol. 7, no. 1-2.

Kasai, T., Yamaguchi, H., (2006), Building an Ontology of IT Education Goals, in International Journal of Engineering Education and Lifelong Learning, Vol. 16, No. ½

Marzano, R.J., Kendall, J.S., (2006), The New Taxonomy of Educational Objectives, Thousand Oaks, CA: Corwin Press.

McGuinness, D.L., Wright, J., (1998), Conceptual Modeling for Configuration: A Description Logic-based Approach, in Artificial Intelligence for Engineering Design, Analysis, and Manufacturing - special issue on Configuration.

McLuhan, M., Fiore, Q., (2005), The Medium is the Message, Gingko Press.

McLuhan, M., Powers, B., (1988), The Global Village: Transformations in World Life and Media in the 21st Century, Oxford University Press.

Musen, M.A., (2004), Ontology-Oriented Design and Programming, in Cuena, J., Demazeau, Y., Garcia, A., Treur, J. (Eds.), Knowledge Engineering and Agent Technology. IOS Press, Amsterdam.

Quillian, M., (1967), Word Concepts: A Theory and Simulation of some Basic Semantic capabilities, in Behavioral Science nr. 12.

Sarker, B.K., Wallace, P. Gill, W., (2007), Some Observations on Mind Map and Ontology Building Tools for Knowledge Management.

Silva Muñoz, L., (2004), Ontology Based Metadata for e-learning Content, Federal University of Rio Grande do Sul, Porto Alegre, Brasil.

Silva Muñoz, L., Moreira de Oliveira, J.P., (2005), Applying Semantic Web Technologies to Achieve Personalization and Reuse of Content in Educational Adaptive Hypermedia Systems.

Stojanovic, L., Staab, S., Studer, R., (2002), E-learning based on the Semantic Web.

Studer, R., Benjamins, V.R., Fensel, D., (1998), Knowledge Engineering, Principles and Methods, in Data & Knowledge Engineering 25(1-2).

Toffler, A., (1973), Future Shock, Political Publishing House, Bucharest

Zimmermann, K., Mimkes, J., Kamke, H.U., (2005), An Ontology Framework for E-learning in the Knowledge Society.