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Research paper (final version)

Curriculum modeling through ontologies Carolina Sarmiento González [email protected] Universidad Nacional de Colombia

Abstract—This article describes the construction of an ontology for a curriculum domain, which aims to represent, organize, formalize and standardize the knowledge of this domain, so that it can be shared and reused by different groups of people in the field of education and engineering. A proposal for documenting METHO TOLOGY (methodology chosen for building the ontology) is presented, in order to facilitate its understanding and application. Finally, the procedure performed during the iterations is illustrated, as an initial step in the life cycle of METHO TOLOGY, applied to the creation of an ontology to represent the undergraduate Electrical Engineering Curriculum of Universidad acional de Colombia.

Index Terms—Ontologies, education, methodologies, METHO TOLOGY, ontology life cycle, curriculum. I. INTRODUCTION

T

he World Wide Web has experienced a rapid development and has become an everyday tool for society in general. It facilitates communication and access to different types of information on cultural, educational, commercial, entertainment environments, between others. Parallel to the growth of the Web, arising the vision of the Semantic Web, an extensive Web that provides Internet searches, providing faster and simpler answers. It is due to the information is well defined, and whose aim is to develop interoperable technologies (specifications, guidelines, software and tools) to lead the Web to get a higher potential. This new vision is based on metadata1 associated with the current web resources in order to semantically enrich the data for easier interpretation, as well as the context to which they belong. The Semantic Web as infrastructure based on metadata, is composed of software agents with the ability to reason on the Web. These agents extend their capabilities to work or perform the humans work, through the optimization of the search results. The Semantic Web adds this metadata to the current Web resources through the use of ontologies [1], which 1

Metadata: data that describes other data and help with the location of them.

defines the terms used to describe and represent an area of knowledge. Ontologies are used by people, data bases and applications that need to share specific information about a particular subject (or domain). For another hand, teaching and learning are part of a unique process that aims the pupil formation. This process must be organized and developed so that it becomes a facilitator of the appropriation of knowledge. Moreover it have to take into account the change that has emerged in the pedagogical perspective, in which the teacher is a facilitator of information, whereas the student seeks to create knowledge using learning resources available. From this new perspective we identified the use of ontologies as an alternative on teaching processes, based on research and creation of new knowledge. Thanks to their ability to represent knowledge, ontologies can satisfy the need to design and implement teaching-learning tools to support current educational processes, characterized by being interoperable, reusable, scalable and easy to maintain. Within this pedagogical perspective, the industry has identified the importance of evaluating the engineer’s education. It argues that engineers have an adequate level of technical knowledge but they lack of some fundamental skills for the exercise of their profession, such as skills to spoken and writing communication and group work [2, 3, 4, 5]. This new vision of the engineers role in the globalized world, led to many universities in several countries to rethink their curricula, to train engineers capable of responding to what is expected of them [2, 4, 6]. In Colombia the Engineering Faculty of Universidad Nacional, launched to design the curriculum to meet their needs, in early 2007. Also it has initiated a process of updating of all curricula, oriented towards the modernization of them [7]. Given this process of curricular reform, we decided to obtain a guide to represent a curriculum through ontologies, because currently there is not a full characterization of these programs as an open and dynamic system. Existing representations are partial, such as a syllabus, and to display the deep and complex relations between its elements are not possible. The representation can provide new projects in the educational systems, focused on the management of different learning styles of the user, monitoring and

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Research paper (final version) evaluation during the training process, the flexibility for content creation, consultation skills and content of an educational program and the relationship between them, and the facility for teachers in the work of managing knowledge. This article presents the progress made to represent the undergraduate Electrical Engineering Curriculum of Universidad Nacional de Colombia, through ontologies. The process of updating this curriculum is based on the CDIO Syllabus, whose general objective is to summarize formally a set of knowledge, skills, and attitudes that alumni, industry, and academia desire in a future generation of young engineers [8]. Therefore this representation will include CDIO Syllabus, and other important sources, such as regulatory issues, curriculum and knowledge in engineering field. Section II presents an introduction to the concept of ontology, some applications and methodologies for building ontologies. METHONTOLOGY details, the methodology that is being used, are described in section III. The section IV describes a proposed documentation for the use of METHONTOLOGY. The application of METHONTOLOGY for the representation of the curriculum is described in section V. Finally, section VI provides conclusions and future work. II. ONTOLOGIES

There are many definitions about what an ontology is. Gruber’s definition became one of the most quoted in literature and by the ontology community: “An ontology is an explicit specification of a conceptualization” [1]. Gómez Pérez et al., present another definition that collected the most relevant definitions of the word ontology: “Ontologies aim to capture consensual knowledge in a generic way, and that they may be reused and shared across software applications and by groups of people. They are usually built cooperatively by different groups of people in different locations” [9]. The ontologies components vary by domain, usually consist of classes (set of objects that describe the domain concepts), relationships (to represent the interactions between classes), instances (which represent objects of a certain class), taxonomies (hierarchical organization of the set of concepts), axioms (used to model sentences that are always true and which allow, together with the legacy of concepts, knowledge inference) and attributes (to describe the objects) [1]. Ontologies are used in various application fields such as bioinformatics, medicine and electronic commerce, including definitions used by machines, about domain basic concepts and relations between them. Encode knowledge in a domain and also knowledge that expands across multiple domains. Thus, making this knowledge to be reusable [10].

process, draw conclusions, make decisions and negotiate with other agents or persons. As the development of ontologies grow, developers use different tools and languages, as a result of this growth, they must be able to find and compare existing ontologies, reuse complete ontologies or their parts, and maintain different versions [11]. II-A. Successful applications II-A1. Building legal ontologies with METHOTOLOGY methodology and tool WebODE: The ontology presented in this article is an adaptation to the Spanish legal framework of taxonomy of classes on legal entities proposed by Breuker. It is addressed to experts in this field who wish to build ontologies for the legal domain [12]. 2

II-A2. GALE : Technology developed by the non-profit organization OpenGALEN, is a clinical terminology represented in the formal and medical oriented language GRAIL. This language was specially developed for specifying restrictions used in medical domains. GALEN is based on a semantically sound model of clinical terminology known as the GALEN Coding Reference (CORE) model [9]. 3

II-A3. UMLS : (Unified Medical Language System),

developed by the United States National Library of Medicine, is a large database designed to integrate a great number of biomedical terms collected from various sources such as clinical vocabularies or classifications [9]. 4

II-A4. EngMath : Contain mathematical models that

engineers use to analyze the behavior of physical systems. The ontology includes conceptual foundations for scalar, vector, and tensor quantities, physical dimensions, units of measure, functions of quantities, and dimensionless quantities [13]. These ontologies are created to enable the sharing and reuse of engineering models among engineering tools and their users. II-A5. PhysSys: Is an engineering ontology for modeling, simulating and designing physical systems. This ontology provides the foundation for the conceptual database schema of a library of reusable engineering model components, covering a variety of disciplines such as mechatronics and thermodynamics [14]. II-B. Methodologies for building ontologies

Until mid-1990’s the process for building ontologies was an art rather than an engineering activity, due to the absence of common and structured guidelines to develop this process. Each development team usually employed their own criteria for manually building the ontology. The IEEE defines a methodology as “a comprehensive, integrated series of techniques or methods creating a general systems theory of how a class of thought-intensive work ought be performed” (IEEE, 1990).

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http://www.opengalen.org/ http://www.nlm.nih.gov/research/umls/ 4 http://www-ksl.stanford.edu/knowledge-sharing/papers/engmathtree.html 3

With the knowledge stored in ontologies, software agents can interpret the meaning of data on Web pages,

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Research paper (final version) There are a series of methodologies for developing ontologies and the most representative are: II-B1. Grüninger & Fox [15]: This methodology is inspired by the development of knowledge based systems using first order logic. They propose identifying intuitively the main scenarios, that is, possible applications in which the ontology will be used. After that, a set of natural language questions, called competency questions, are used to determine the scope of the ontology. This is a very formal methodology that takes advantage of the robustness of classic logic and can be used as a guide to transform informal scenarios in computable models [9]. II-B2. On-To-Knowledge [16]: In this project was developed a methodology and tools for intelligent access to large volumes of semi-structured and textual information sources in intra-, extra-, and internet-based environments. The methodology proposes to build the ontology taking into account how the ontology will be used in further applications. Also, On-To-Knowledge includes the identification of goals to be achieved by knowledge management tools, and is based on al analysis of usage scenarios [9]. II-B3. Methontology: It allow for building ontologies either from scratch, reusing other ontologies as they are, or by a process of re-engineering . The framework enables the construction of ontologies at the knowledge level, i.e., the conceptual level, as opposed to the implementation level [9]. (More information about this methodology is presented on section III). III. METHONTOLOGY [9]

This methodology was developed within the Ontology Group at Universidad Politécnica de Madrid. METHONTOLOGY has its roots in the activities identified by the software development process proposed by IEEE5 organization and in knowledge engineering methodologies.

Figure 1. Ontology life cycle [17].

- Specification: this activity allows determining why the ontology is being built, which its intended uses are and the end users are. - Conceptualization: this activity is responsible for organizing and converting an informal perception of the domain in a semi-formal specification, using a set of intermediate representations (RRII), based on tables and graphics, which can be easily understood by domain experts and developers of ontologies. this activity transforms the - Formalization: conceptual model into a formal or semi-computable model. - Implementation: this activity builds computable models in an ontology language (Ontolingua, RDF Schema, OWL, etc.). - Maintenance: this activity updates and corrects the ontology if needed. METHONTOLOGY also identifies management activities (planification, control and quality assurance) and support (knowledge acquisition, integration, evaluation, documentation and configuration management).

The Foundation for Intelligent Physical Agents (FIPA)6, which promotes inter-operability across agent-based applications, has proposed METHONTOLOGY for ontology construction. III-A. Life cycle of METHOTOLOGY METHONTOLOGY proposes an ontology building life cycle based on evolving prototypes because it allows adding, changing, and removing terms in each new version (figure 1). III-B. Development Process of METHOTOLOGY METHONTOLOGY provides guidelines about how to carry out the development of the ontology through the activities of specification, conceptualization, formalization, implementation and maintenance, as shown in Figure 2. The following describes briefly what each of these activities is: 5 6

http://www.ieee.org/portal/site http://www.fipa.org/specs/fipa00086/

Figure 2. Development process of METHONTOLOGY. IV. METHONTOLOGY DOCUMENTATION PROPOSAL

To facilitate the understanding and application of METHONTOLOGY has been designed a process diagram, which presents the sequence of activities within the process of building an ontology. The proposed diagram consist in apply principles of Quality Management and Documentation Control to Methontology.

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Research paper (final version) Each of the processes is described taking into account the input and procedure required to achieve the process as well as the final product. Table 1 presents the description for one of the process.

V. PROPOSED MODEL APPLYING METHONTOLOGY

Process:

Design schedule

Given that METHONTOLOGY is based on evolving prototypes, a diagram that contain each support activities to be conducted for each of the processes of building the ontology has been designed.

Input:

Activities to develop

V-A. SUPPORT ACTIVITIES ITERATIOS

Product:

Schedule

Procedure:

- Identify tasks to develop - Identify tasks order - Identify time and resources required for compliance

The name of the prototypes is given by the word: iteration, accompanied by an ascending sequence of numbers started at 0. Figure 3 represents support activities diagram for iteration 0.

Table 1. Description process to design schedule.

Figure 3. Support Activities Design Schedule Process (Iteration 0)

V-B. ITERATIO 0 REPORT

V-B2. SUPPORT DOCUMETS

For each of the iterations is prepared a report containing details of the process undertaken during the iteration. Report for iteration 0 is presented below:

1) References: The result of the references review is gathered in a table that contains author(s), title, year, entry type and reference type.

Table 2 presents general description of the iteration 0.

2) Basic sources for conceptualization of the ontology: Four basic sources for creation of the conceptual model are:

General description iteration 0

Topic

Prepared by

Reviewer

Date

Representation of Undergraduate Electrical Engineering Curriculum of Universidad Nacional, through ontologies. Carolina Sarmiento González Universidad Nacional Bogotá, Colombia [email protected] Ing. Oscar Duarte Universidad Nacional Bogotá, Colombia [email protected] March 25th 2009

Table 2. General description iteration 0 V-B1. SCHEDULE: It includes each of the tasks to develop

during the iteration and the time that it takes.

- Regulations: They are based on Article 033 of 2007.

It lays down the basic guidelines for the process of training students of the Universidad Nacional de Colombia through its curriculum, which defines four levels of training: Curriculum Area, Curriculum Program, Curriculum and Subject [18]. - Curriculum:

Universidad Curriculum.

This information is taking from Nacional Electrical Engineering

- Skills: The CDIO Syllabus is the source to obtain

engineer’s skills [8]. - Knowledge: It is taken from Electrical Engineering

Handbook [19]. 3) Specification: During the specification of the ontology is needed to answer the following questions:

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Research paper (final version) - Why is constructed the ontology?

Currently the Universidad Nacional does not have a full characterization of curriculums as a dynamic and open system.

because through this representation will be defined the terms used to describe and represent the knowledge area of Electrical Engineering Program. Ontologies created will share information specific to the area and make that knowledge reusable.

- What is the use of ontology?

The ontology will be a guide to represent a curriculum. - Who are the end users?

Teachers, students, administrators from the Universidad Nacional or people interested in the Electrical Engineering curriculum, taking into account the elements that comprise it and the relationships between them. 4) Conceptualization: Four basic sources that will be used to implement the ontology conceptual model are represented in figure 4.

Currently the development of the iterations is been worked as part of the life cycle of METHONTOLOGY, so as to get a domain conceptualization. Subsequently available platforms for the development of ontologies will be reviewed, and one of these will be chosen, as a tool that will set the model of knowledge for the conceptualization of the ontology.

REFERENCES [1]

Gruber, T. R., “A translation approach to portable ontology specifications, Knowledge Acquisition,” ISSN 1042-8143, vol. 5, Nº 2, pp. 199-220, 1993.

[2] Tadmor, Zehev, “Redefining engineering disciplines for the twenty-first century,” The Bridge- National Academy of Engineering, vol. 36, Nº 2, pp. 33–37, 2006. [3] Kennedy, Theodore C., “The “value-added” approach to engineering education: An industry perspective,” The Bridge- National Academy of Engineering, vol. 36, Nº 2, pp. 14–16, 2006. Figure 4. Iteration 0 Conceptualization

5) Evaluation and maintenance: In this stage is reviewed activities support documentation to different processes. 6) Quality control: Table 3 shows an example of how this activity is carried out, taking into account the fulfillment for each one of the tasks.

[4] Wulf, W.A., “The Urgency of Engineering Education Reform,” The Bridge -National Academy of Engineering, vol. 28, Nº 1, pp. 48, 1998. [5] The Boeing Company, “Desired attributes of an engineer: Participation with universities,” 1996. Available: http://www.boeing.com/companyoffices/pwu/attributes/ attributes.html. [6] R. Lattuca, Lisa and T. Terenzini, Patrick and Volkwein, J. Fredericks and D. Peterson, George, “The changing face of engineering education,” The Bridge – National Academy of Engineering, vol. 36, Nº 2, pp. 5–13, 2006.

Table 3. Iteration 0 Evaluation

VI. CONCLUSIONS AND FUTURE WORK

METHONTOLOGY allows building ontologies using graphical and tabular intermediate representations that are easily understood by domain experts who are not involved in the field of ontological engineering. It also permits the updating of terms in each of the iterations, which demonstrates its flexibility. The final representation of the ontology provides the community of a well-structured, standardized and formalized knowledge, acquired from experts in Electrical Engineering field. This work will support the educational processes, related to the creation or modification of curriculums,

[7] Universidad Nacional de Colombia, Facultad de Ingeniería Eléctrica y Electrónica, “Competencias: La opinión de los profesores,” Sobre el “syllabus” CDIO, Junio 2008. [8] Crawley, Edward F. “The CDIO Syllabus,” A Statement of Goals for Undergraduate Engineering Education, Department of Aeronautics and Astronautics Massachusetts Institute of Technology, January 2001. [9] Gómez-Pérez A. and Fernández-López M and Corcho O, “Ontological Engineering: with examples from the areas of nowledge management, e-commerce and the Semantic Web,” Springer-Verlag, New York, 2003. [10] W3C, “OWL Web Ontology Language Use Cases and Requirements,” W3C Recommendation 10 February 2004. Available: http://www.w3.org/TR/2004/RECwebont-req-20040210/ [11] Noy, N.F. and Musen, M.A., “Ontology versioning in an ontology management framework,” Intelligent Systems, IEEE, , vol. 19, Nº 4, pp. 6-13, Jul-Aug 2004

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Research paper (final version) [12] Corcho O and Fernández-López M and Gómez-Pérez A and López-Cima Angel, “Construcción de ontologías legales con la metodología METHONTOLOGY y la herramienta WebODE,” Facultad de Informática, Universidad Politécnica de Madrid. [13] Thomas R. Gruber and Gregory R. Olsen, “An Ontology for Engineering Mathematics,” Fourth International Conference on Principles of Knowledge Representation and Reasoning, Gustav Stresemann Institut, Bonn, Germany, Morgan Kaufmann, 1994. [14] Borst, W.N. and Akkermans, J.M. and Top, J.L.. “Engineering Ontologies,” International journal of human-computer studies, ISSN 10715819, vol. 46, Nº 23, pp. 365-406, 1997. Available: http://doc.utwente.nl/18019/1/Borst97engineering.pdf [15] Gruninger, Michael and Fox, Mark S., “Methodology for the design and evaluation of ontologies,” In Proceedings of the Workshop on Basic Ontological Issues in Knowledge Sharing held in conjunction with IJCAI-95, pp. 6.1–6.10, 1995. Available: https://eprints.kfupm.edu.sa/50622/1/50622.pdf [16] Staab S. and Schnurr HP. and Studer R. and Sure Y., “Knowledge Processes and Ontologies,” IEEE Intelligent Systems, vol. 16, Nº 1, pp. 26–34, 2001. Available: http://www.bases.unal.edu.co:2365/stamp/stamp.jsp?tp= &arnumber=912382&isnumber=19693 [17] Gómez-Pérez, A., “Knowledge Sharing and Reuse,” In the Handbook of Applied Expert Systems, Ed CRC Press, 1998. [18] Universidad Nacional de Colombia, Consejo Superior Universitario. Acuerdo número 033 de 2007. http://www.unal.edu.co/secretaria/normas/csu/2007/A00 33_07S.pdf. [19]

Dorf C. Richard, “The Electrical Handbook,” IEEE Press, 2° edition.

Engineering

Author Carolina Sarmiento González, Systems Engineer, Universidad de San Buenaventura 2002; Master candidate in Systems Engineering and Computer Science 2008, Universidad Nacional de Colombia.