Przejdź do treści

Uniwersytet Śląski w Katowicach

  • Polish
  • English
  • Français
Wydział Sztuki i Nauk o Edukacji

Innovative Educational Technologies, Tools and Methods for E-learning
Scientific Editor Eugenia Smyrnova-Trybulska
“E-learning”, 12, Katowice–Cieszyn 2020, pp. 77–87
DOI: 10.34916/el.2020.12.07

el-2020-12-07

CONNECTION BETWEEN ONTOUML AND KNOWLEDGE REPRESENTATION MODEL OF STUDENTS’ ACTIVITIES

David Buchtela-1, Dana Vynikarová-2, 1, Centre of Business Informatics, Faculty of Information Technology, Czech Technical University in Prague, Thakurova 9, 160 00 Prague 6, the Czech Republic, 2, Faculty of Economics and Management, Czech University of Life Sciences Prague, Kamycka 129, 165 00 Prague 6, the Czech Republic, 1, david.buchtela@fit.cvut.cz, 2, vynikarova@pef.czu.cz, ORCID 1, 0000-0002-3564-9198, 2, 0000-0001-8955-6002

Abstract: In every focused system, e.g. the Learning Management System (LMS) Moodle, it is possible to select relevant entities (students, teachers, study resources, assessment, test and other activities) and their relations (associations). A conceptual model in OntoUML is suitable for the entities representation. It is possible to feel a knowledge decision process as a non-determinist finite automaton where entity state transitions are inspected. A way of entity state transition (needed data and conditions) is represented by guideline (procedural) knowledge representation model (like as GLIKREM). This paper aims to describe the possibilities of the conceptual model of the focused system designed in OntoUML and the Guideline Knowledge Representation Model (GLIKREM) for the Knowledge Representation Model of Students’ Activities (KRMSA) based on knowledge and models of students’ activities in a Moodle system. This article describes a link between OntoUML as a conceptual model and GLIKREM as a procedural knowledge model base with the aid of the main components of both models.

Keywords: knowledge representation model, conceptual model, students’ activities,
decision process, OntoUML, Moodle.

REFERENCES
B o x w a l a, A. A., P e l e g, M., T u, S. W., O g u n y e m i, O., Z e n g, Q., & Wa n g, D. (2004). GLIF3: A Representation Format for Sharable Computer-Interpretable Clinical Practice Guidelines, Journal of the Biomedical Informatics, 37(3), pp. 147–161.

B u c h t e l a, D., Ve s e l ý, A., & Vy n i k a r o v á, D. (2010). Guideline Knowledge Representation Model (GLIKREM). Knowledge Management and Modern Information Technologies, pp. 26–41. Prague: Alfa publishing.

G u i z z a r d i, G. (2005). Ontological foundations for structural conceptual models. Enschede: Telematica Instituut / CTIT.

K o ķ e, T. (2003). Continuous Education: the Main Tasks and Their Implementation. Nepārtrauktās iz glītības sociāli  pedagoģiskie aspekti. Rīga: SIA “Izglītībassoļi”, pp. 4–16.

P e l e š k a, J., B u c h t e l a, D., A n g e r, Z., Š e b e s t a, K., T o m e č k o v á, M., Ve s e l ý, A., Zv á r a, K., & Z v á r o v á, J. (2005). Formalisation of Medical Guidelines. European Journal for Biomedical Informatics, pp. 133–141.

R y b o l a, Z., P e r g l, R. (2016). Towards OntoUML for Software Engineering: Transformation of Rigid Sortal Types into Relational Databases. Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, Vol. 8 (pp. 1581–1591). M. Ganzha, L. Maciaszek, M. Paprzycki (Eds).

R y b o l a, Z., P e r g l, R. (2017). Towards OntoUML for Software Engineering: Transformation of Kinds and Subkinds into Relational Databases. Computer Science and Information Systems, 14(3), pp. 913–937.

S t o y a n o v a – D o y c h e v a, A., G l u s h k o v a, T., & I v a n o v a, V. (2019). Application of subject domain ontologies in e-learning. E-learning and STEM Education Scientific. Editor E. Smyrnova-Trybulska. “E-learning”, 11, Katowice–Cieszyn, pp. 93–107.

Ve s e l ý, A., Z v á r o v á, J., P e l e š k a, J., B u c h t e l a, D., & A n g e r, Z. (2006). Medical Guidelines Presentation and Comparing with Electronic Health Record. International Journal of Medical Informatics, 75(3–4), pp. 240–245.

return to top