Possibilities of Modelling Web-Based Education Using IF-THEN Rules and Fuzzy Petri Nets in LMS

Basic requirements, which are imposed on LMS (Learning Management System) from the point of view of the needs of a teacher, are to present the contents of instruction, manage the instruction, communicate with students, motivate them to study, observe their progress and evaluate them. The article deals with an opportunity to implement fuzzy logic into web-based education using the created IF-THEN rules and modelling in Petri nets. By an application of fuzzy logic into Petri nets there arises a strong tool for modelling teaching processes, mainly thanks to the easy understandability and sophisticated mathematical setup, supporting a rather simple design of educational activities managed by LMS, for the compendious modularity of solution and robustness of the design.

[1]  Shian-Shyong Tseng,et al.  Constructing SCORM compliant course based on High-Level Petri Nets , 2006, Comput. Stand. Interfaces.

[2]  Wolfgang Zielke,et al.  Jak číst rychleji a lépe , 1988 .

[3]  A. Parmar,et al.  Paper Review on Sharable Content Object Reference Model (SCORM): Framework for E-learning Standard , 2012, 2012 Second International Conference on Advanced Computing & Communication Technologies.

[4]  Peter Brusilovsky,et al.  Adaptive Hypermedia: From Intelligent Tutoring Systems to Web-Based Education , 2000, Intelligent Tutoring Systems.

[5]  Martin Capay,et al.  E-learning support for computer graphics teaching and testing , 2010, ICT 2010.

[6]  Mercedes Gómez-Albarrán,et al.  The Teaching and Learning of Programming: A Survey of Supporting Software Tools , 2005, Comput. J..

[7]  Dogan Ibrahim,et al.  Assessing the Success Rate of Students Using a Learning Management System Together with a Collaborative Tool in Web-Based Teaching of Programming Languages , 2007 .

[8]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[9]  Martin Capay,et al.  Enhancing the quality of administration, teaching and testing of computer science using learning management system , 2010 .

[10]  Michal Munk,et al.  Probability modeling of accesses to the web parts of portal , 2011, WCIT.

[11]  Michal Moravčík.  Modelovanie adaptívnych webových systémov , 2006 .

[12]  Alfred Kobsa,et al.  The Adaptive Web, Methods and Strategies of Web Personalization , 2007, The Adaptive Web.

[13]  Peter Svec,et al.  Data preprocessing evaluation for web log mining: reconstruction of activities of a web visitor , 2010, ICCS.

[14]  Peter Brusilovsky,et al.  ELM-ART: An Intelligent Tutoring System on World Wide Web , 1996, Intelligent Tutoring Systems.

[15]  Peter Svec,et al.  Data advance preparation factors affecting results of sequence rule analysis in web log mining , 2010 .

[16]  M. Vujosevic,et al.  Fuzzy Petri net based reasoning for the diagnosis of bus condition , 2004, 7th Seminar on Neural Network Applications in Electrical Engineering, 2004. NEUREL 2004. 2004.

[17]  Nadire Cavus,et al.  Computer aided evaluation of learning management systems , 2009 .

[18]  Peter Brusilovsky,et al.  User Models for Adaptive Hypermedia and Adaptive Educational Systems , 2007, The Adaptive Web.