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A KNOWLEDGE ONTOLOGY AND ITS APPLICATION INTO A LEARNING ENVIRONMENT MODEL

A KNOWLEDGE ONTOLOGY AND ITS APPLICATION INTO A LEARNING ENVIRONMENT MODEL. Inés Friss de Kereki ORT Uruguay University Javier Azpiazu, Andrés Silva Polytechnic University of Madrid. Second International Conference on Multimedia and ICTs in Education MICTE 2003 – Badajoz - Spain.

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A KNOWLEDGE ONTOLOGY AND ITS APPLICATION INTO A LEARNING ENVIRONMENT MODEL

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  1. A KNOWLEDGE ONTOLOGY AND ITS APPLICATION INTO A LEARNING ENVIRONMENT MODEL Inés Friss de Kereki ORT Uruguay University Javier Azpiazu, Andrés Silva Polytechnic University of Madrid Second International Conference on Multimedia and ICTs in Education MICTE 2003 – Badajoz - Spain

  2. Part I: Introduction Part II: Knowledge Ontology Part III: Application Part IV: Conclusion

  3. Part I: Introduction

  4. Knowledge Management Engineering Learning, Teaching Ontologies MODEL Environments Requirements Engineering, Knowledge Engineering Introduction:Context and related areas PhD Thesis: A learning environment model based on knowledge management techniques

  5. Introduction • Knowledge: the most versatile and important of all production factors (Toffler et al., 1998) • Accurate definition of knowledge related terms is required • knowledge ontology (Part II) • application (Part III)

  6. Part II: Knowledge Ontology

  7. Ontology • Defines the vocabulary of an area • basic terms • relationships (Neches et al., 1991) • An explicit formal specification of a shared conceptualization (Gruber, 1993)

  8. Ontology of knowledge • descriptive knowledge • procedural knowledge • heuristic knowledge • anecdotic knowledge

  9. Examples • Object Oriented Programming • Classes • Elementary Math • Arithmetic Progression

  10. Hierarchy

  11. Knowledge main attributes • Description • Learning strategies • Importance • Medium • Required level

  12. Part III: Application

  13. What is a Learning Environment? • It is the space where it is possible to manage knowledge or, rather, ignorance • Adaptable • Non sequential approaches

  14. The Proposed Model

  15. Implementation: PLE:ASE

  16. PLE:ASE

  17. Experimentation • Understanding teaching • Search for new ways to solve problems • Knowledge transferability

  18. Experimentation • ORT Uruguay University • 1st year, Programming, Systems Engineering • Groups (random): • Control (8 students) • Training (19 students) • Training and environment (21 students) • 2 test to each student • Statistical Tests: • Mann-Whitney, Kruskal-Wallis • No significant differences at the beginning

  19. Experimentation results • The use of the environment allowed the students to enlarge or improve: • their problem resolution methods and • their capacities to carry out the transfer of knowledge.

  20. Part IV: Conclusion

  21. It is possible to define a knowledge ontology and to apply it, with good results, into a learning environment model

  22. Questions?

  23. Thanks!

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