Design and Creation of a Universal Model of Educational Process with the Support of Petri Nets

  • Zoltán Balogh
  • Milan Turčáni
  • Martin Magdin
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 269)


The paper deals with the design and realization of the model of educational processes by means of Petri nets in the managerial educational environment (LMS-MOODLE). It focuses on the effectiveness and employability of such tool for the creation of models. Based on the created educational models in Petri nets we are able to verify and simulate individual processes, which are carried out during the passage of the student through the e-course in LMS. The realized model can serve as a prototype for the creation of other electronic courses in the virtual educational environment. We can make oneself certain of effectiveness of the created model by evaluating the obtained relevant data from the questionnaire filled in by the students after finishing the passage through the e-course, but also from the modified log files of observed e-courses. We can thus find certain rules of behaviour of applicants in the e-course by means of the usage analysis and compare them with the process models created by us. By means of such comparisons we shall be able to eliminate all interfering elements from process models of e-courses, thus making the other created e-courses more effective and more attractive.


Evaluation Educational process Modelling LMS Petri nets Universal model 


  1. 1.
    Drlik M, Skalka J (2011) Virtual faculty development using top-down implementation strategy and adapted EES model. Procedia Soc Behav Sci 28:616Google Scholar
  2. 2.
    Cavus N (2008) Education technologies of the information age: course management systems. Extend, vol 28, No. 2Google Scholar
  3. 3.
    Cápay M, Tomanová J (2010) Enhancing the quality of administration, teaching and testing of computer science using learning management system. WSEAS Trans Inf Sci Appl 7(9):1126–1136Google Scholar
  4. 4.
    Balogh Z et al (2012) Creating model educational processes using petri nets implemented in the LMS, 2012. In: Efficiency and responsibility in education 2012 : 9th international conference, FEM CULS Prague 7–8 June 2012. Czech University of Life Sciences, Prague, pp 7–16Google Scholar
  5. 5.
    Balogh Z, Turčáni M, Burianová M (2010) Modelling web-based educational activities within the combined forms of education with the support of applied informatics with an e-learning support. In: Proceeding of the 7th international conference efficiency and responsibility in education (ERIE 2010). Czech University of Life Sciences, Praha, pp 14–23Google Scholar
  6. 6.
    Cápay M, Balogh Z, Burianová M (2009) Using of e-learning in teaching of non-medical health personnel. Bratislava : EKONÓM, Inovačný proces v e-learningu 2009, Ekonomická univerzita v Bratislave, ISBN 978-80-225-2724-8Google Scholar
  7. 7.
    Yang S-M (1985) A study on modeling with petri net. The thesis of master degree, Hanyang universityGoogle Scholar
  8. 8.
    Oh G-R (1983) Petri net and its variants. J KISS 2(2):137–144Google Scholar
  9. 9.
  10. 10.
    Bae S-H (2003) A study on an intrusion detection using colored petri nets. The thesis of master degree, Dongguk UniversityGoogle Scholar
  11. 11.
    Jo C-H (2004) An effectiveness analysis on logistics information system using M&S. The thesis of master degree, National Defense UniversityGoogle Scholar
  12. 12.
  13. 13.
    Chang W-C, Lin HW, Shin TK, Yang H-C (2005) SCORM learning sequence modeling with petri nets in cooperative learning. Learn Technol Newsl 7(1):28–33Google Scholar
  14. 14.
    Chang W-C (2006) Applying SCORM in cooperative learning. J Comput 17(3)Google Scholar
  15. 15.
    Lin HW, Chang W-C, Yee G, Shin TK, Wang CC, Yang H-C (2005) Applying petri nets to model SCORM learning sequence specification in cooperative learning. IEEE 1:203–208Google Scholar
  16. 16.
    Markl J (2003) HPSim 1.1—uživatelská příručka, Ostrava: VŠB-Technická univerzitaGoogle Scholar
  17. 17.
    Balogh Z, Turčáni M (2009) Modelling and simulation of education of natural science subjects with e-learning support. Information and communication technology in natural science education. Probl Educ 21st Century16:8–16Google Scholar
  18. 18.
    Magdin M, Cápay M, Mesárošová M (2011) Usage of interactive video in educational process to determine mental level and literacy of a learner. In: 14th international conference on interactive collaborative learning, ICL 2011—11th international conference Virtual University, VU’11, pp 510Google Scholar
  19. 19.
    Brečka P, Magdin M, Koprda Š (2011) Two-state regulation in MATLAB for the comparison of some parameters (damage, power consumption) by PSD regulation. Lecture notes in electrical engineering, vol 121. LNEE, pp 369–376Google Scholar
  20. 20.
    Balogh Z, Koprda Š (2012) Modelling of control in educational process by LMS. DIVAI 2012. In: 9th international scientific conference on distance learning in applied informatics : conference proceedings. Štúrovo, Hotel Thermal, 2–4 May 2012, Nitra: UKF, 2012, pp 43–51Google Scholar
  21. 21.
    Borghuis MGM (1997) User feedback from electronic subscriptions: the possibilities of logfile analysis. Libr Acquisition Pract Theor 21(1997):373–380CrossRefGoogle Scholar
  22. 22.
    Hulshof CD (2005) Log file analysis. Encycl Soc Meas 2005:577–583CrossRefGoogle Scholar
  23. 23.
    Munk M, Kapusta J, Švec P (2009) Data pre-processing dependency for web usage mining based on sequence rule analysis. In: IADIS European conference on data mining, Algarve, pp 179–181Google Scholar
  24. 24.
    Munk M, Kapusta J, Švec P, Turčáni M (2010) Data advance preparation factors affecting results of sequence rule analysis in web log mining. E a M: Ekonomie a Manage 13(4):143–160Google Scholar
  25. 25.
    Škorpil V, Šťastný J (2008) Comparison of learning algorithms. In: 24th biennial symposium on communications. Kingston, Canada, pp 231–234Google Scholar
  26. 26.
    Alsultanny YA (2011) Comparison between data mining algorithms implementation. In: Digital information and communication technology and its application, Part II, CCIS 167, pp 629–641Google Scholar
  27. 27.
    Klocoková D, Munk M (2011) Usage analysis in the web-based distance learning environment in a foreign language education: case study. Procedia Soc Behav Sci 15:993–997 (ISSN 1877-0428)Google Scholar
  28. 28.
    Klocoková D (2011) Integration of heuristics elements in the web-based environment: experimental evaluation and usage analysis. Procedia Soc Behav Sci 15:1010–1014 (ISSN 1877-0428)Google Scholar
  29. 29.
    Cápay M, Mesárošová M, Balogh Z,(2011) Analysis of students’ behaviour in e-learning system. In: Proceedings of the 22nd EAEEIE annual conference (EAEEIE 2011), pp 35–40Google Scholar
  30. 30.
    Cápay M, Balogh Z, Boledovičová M, Mesárošová M,(2011) Interpretation of questionnaire survey results in comparison with usage analysis in e-learning system for healthcare. In: DICTAP 2011, Part II, CCIS 167, pp 504–516Google Scholar
  31. 31.
    Balogh Z, Munk M, Turčáni M,(2011) Analysis of students’ behaviour in the web-based distance learning environment, 2011. In: Recent researches in circuits, systems, communications and computers : proceedings of the 2nd European conference of computer science (ECCS ‘11).WSEAS Press Puerto De La Cruz, ISBN 978-1-61804-056-5, pp 339–344Google Scholar
  32. 32.
    Balogh Z, Munk M, Cápay M, Turčáni M (2010) Usage analysis in e-learning system for healthcare, 2010. In: The 4th international conference on application of information and communication technologies AICT2010, Tashkent, IEEE, 2010, pp 131–136Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  • Zoltán Balogh
    • 1
  • Milan Turčáni
    • 1
  • Martin Magdin
    • 1
  1. 1.Department of Informatics, Faculty of Natural SciencesConstantine the Philosopher University in NitraNitraSlovakia

Personalised recommendations