Dynamic Properties of Knowledge Networks and Student Profile in e-Learning Environment

  • Radoslav Fasuga
  • Libor Holub
  • Michal Radecký
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


Goal of this article is to describe relevant data structures that can be used for future adaptation of study materials. This Article provides discussion about three basic e-learning areas of interest. First part describes material structure by adding descriptive attributes and behaviors through Explanation and Tests. Second part is oriented to Student. Student profile, which represents virtualized profile of student requirements, its preferences, actual knowledge, requested knowledge etc., is produced in background In third part there are discussed possibilities of usage of Student objective and subjective response to find optimal explanation for particular student. Last part is oriented to implementation of the knowledge network structure and the model scheme of requested modules. Article discuses benefits of the automatic study material adaptation in opposite to the adaptation based on rules defined by authors.


Knowledge Network Virtual Learning Environment Student Profile Optimal Explanation Student Objective 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    McPherson, M., Baptista Nunes, M.: The Role of Tutors as an Integral Part of Online Learning Support. European Journal of Open, Distance, and E-Learning (2004), ISSN 1027-5207Google Scholar
  2. 2.
    Anderson, T., Whitelock, D.: The Educational Semantic Web: Visioning and Practicing the Future of Education. Journal of Interactive Media in Education 2004(1) (2004); Special Issue on the Educational Semantic Web, ISSN:1365-893XGoogle Scholar
  3. 3.
    Bloom, B.S.: Taxonomy of Educational Objectives. In: Handbook I: The Cognitive Domain. David McKay Co Inc., New York (1956)Google Scholar
  4. 4.
    Dave, R.H.: Developing and Writing Behavioural Objectives. In: Armstrong, R.J. (ed.). Educational Innovators Press (1975)Google Scholar
  5. 5.
    Ho, T.P.W., Lee, T.Y.: Teaching Thinking Skills In E-Learning - Application of the BLOOM’S TAXONOMY. In: ITE Teachers Conference (2004)Google Scholar
  6. 6.
    Bober, M., Fasuga, R., Šarmanová, J.: Adaptive Mechanisms in Virtual Education Environment. In: ICEE 2007 Coimbra. University of Coimbra, Portugal (2007), ISBN 978-972-8055-14-1Google Scholar
  7. 7.
    Fasuga, R., Bober, M., Šarmanová, J.: Knowledge and Skills representation in Virtual Education. University of Coimbra, Coimbra (2007), ISBN 978-972-8055-14-1Google Scholar
  8. 8.
    Bober, M., Fasuga, R., Holub, L., Šarmanová, J.: Student activity protocol oriented to questions, their evaluation, define correct and wrong solutions, optimalization. In: Erika (ed.) ICTE 2007. Přírodovědecká fakulta Ostravské univerzity (2007), ISBN 978-80-7368-388-7Google Scholar
  9. 9.
    Fasuga, R.: Intelligent education: Making decision based on objective and subjective student response. In: Wofex 2006. VŠB - Technical University of Ostrava, pp. 315–320 (2006), ISBN 80-248-1152-9Google Scholar
  10. 10.
    Holub, L., Fasuga, R., Šarmanová, J.: Possibilities of application of artificial intelligence techniques to learning styles. In: Jakab, F., Fedák, V., Sivý, I., Bučko, M. (eds.) ICETA 2005, pp. 151–156. Technical University Kosice (2005), ISBN 80-8086-016-6 Google Scholar
  11. 11.
    Fasuga, R.: Using artificial intelligence in education process, CVUT Praha 2004, technol-ogy for e-education (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Radoslav Fasuga
    • 1
  • Libor Holub
    • 1
  • Michal Radecký
    • 1
  1. 1.Department of Computer ScienceVŠB Technical University of OstravaOstrava-PorubaCzech Republic

Personalised recommendations