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Assessment of the didactic measurement results using FCM type networks

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Purpose: The paper presents students egzamination system developer for the scaleable e-learning system. Organisation of the teaching and examination processes, as well as the implementation details are described. An intelligent system based on one of the Artificial Intelligence methods - FCM (Fuzzy Cognitive Maps) type network is being developed within the framework of current work on the e-learning process topic, to model the behaviour and functioning the system as a whole. Design/methodology/approach: The intelligent examination system for students was developed based on mechanism derived from HotPotatoes system. Programming languages like PHP and JavaScript were also used. Fuzzy Cognitive Maps were used to model the e-learning process and an example of the system use is presented. Findings: The project effect is the intelligent examination system supporting the statistical analysis of the difficulty level of test problems, generating comments and materials individually for every user. The didactic process was modelled using FCM method. Practical implications: Reduction of test checking time consumption, individual attitude to every student, score advised to the students along with the comments pertaining to the wrong answers and recommended study topics - all immediately after the test, sent to the student's mailbox. Originality/value: Employment of FCM AI tool for evaluation of the teaching process effectiveness.
Rocznik
Strony
45--52
Opis fizyczny
Bibliogr. 25 poz.
Twórcy
autor
autor
  • Division of Materials Processing Technology, Management and Computer Techniques in Materials Science, Institute of Engineering Materials and Biomaterials, Silesian University of Technology, ul. Konarskiego 18a, 44-100 Gliwice, Poland, monika.guminska@polsl.pl
Bibliografia
  • [1] L.A. Dobrzański, F. Brom, Z. Brytan, Teaching of material science matters using e-learning techniques, Archives of Materials Science and Engineering 28/11 (2007) 691-694.
  • [2] L.A. Dobrzański, R. Honysz, Z. Brytan, Application of interactive course management system in distance learning of material science, Journal of Achievements in Materials and Manufacturing Engineering 17 (2006) 429-432.
  • [3] L.A. Dobrzański, F. Brom, Blended learning in teaching materials science subjects at full time studies, Archives of Materials Science and Engineering 34/1 (2008) 57-60.
  • [4] L.A. Dobrzański, A. Jagiełło, R. Honysz, Virtual tensile test machine as an example of Material Science Virtual Laboratory post, Journal of Achievements in Materials and Manufacturing Engineering 27/2 (2008) 207-210.
  • [5] L.A. Dobrzański, R. Honysz, Development of virtual light microscope for a material science virtual laboratory, Journal of Achievements in Materials and Manufacturing Engineering 20 (2007) 571-574.
  • [6] M. Gumińska, J. Madejski, Scaleable model of e-learning platform, Journal of Achievements in Materials and Manufacturing Engineering 21/1 (2007) 95-98.
  • [7] M. Gumińska, J. Madejski, Web based e-learning platform as a source of the personalized teaching materials, Journal of Achievements in Materials and Manufacturing Engineering 24/2 (2007) 227-230.
  • [8] L.A. Dobrzański, F. Brom, The assessment of teaching materials science subject using e-learning method, Journal of Achievements in Materials and Manufacturing Engineering 30/2 (2008) 204-210.
  • [9] M. Gumińska, J. Madejski, Computer aided didactic measurement in the scaleable e-learning platform, Proceedings of the 2nd Domestic Scientific Conference, Data Processing Technologies, Poznan, 2007,
  • [10] L. Welling, L. Thompson, PHP and MySQL Web Development, Third Edition, Helion, Gliwice, 2005.
  • [11] S. Steward, PDF Hacks. 100 Industrial-Strength Tips & Tools, O’Reilly, 2004
  • [12] B. Kosko, Fuzzy Cognitive Maps, International Journal of Man-Machine Studies, 24 (1986) 65-75.
  • [13] J. Aguilar, A Survey about Fuzzy Cognitive Maps, International Journal of Computational Cognition 3/2 (2005) 27-33.
  • [14] W. Stach, L. Kurgan, W. Pedrycz, M. Reformat, Genetic learning of fuzzy cognitive maps, Fuzzy Sets and Systems 153/3 (2005) 371-401.
  • [15] J. Aguilar, A dynamic Fuzzy-Cognitive-Map Approach Based on Random Neural Network, International Journal of Computational Cognition 1/4 (2003) 91-107.
  • [16] C.D. Stylios, V.C. Georgopoulos, Genetic algorithm enhanced Fuzzy Cognitive Maps for medical diagnosis, IEEE World Congress on Computational Intelligence, Fuzz-IEEE 2008, 2123-2128.
  • [17] L. Rodriguez-Repiso, R. Setchi, J.L. Salmeron, Modelling IT projects success with Fuzzy Cognitive Maps, Expert Systems with Applications 32/2 (2007) 543-559.
  • [18] J. Aguilar, A Fuzzy Cognitive Map Based on the Random Neural Model, Engineering of Intelligent Systems 2070 (2001) 333-338.
  • [19] D.C. Stylios, P.P. Groumpos, The challenge of modeling supervisory systems using fuzzy cognitive maps, Journal of Intelligent Manufacturing 9 (1998) 339-345.
  • [20] A.L. Laurano-Cruces, J. Ramirez-Rodriguez, A. Teran-Gilmore, Evaluation of the Teaching-Learning Process with Fuzzy Cognitive Maps, Advances in Artificial intelligence - Iberamia 2004, Springer, 3315 (2004) 922-931, 9th Ibero-American Conference on Al, Puebla, Mexico, November 22-26.
  • [21] E.I. Papageorgiou, C.D. Stylios, P.P. Groumpos, Active Hebbian learning algorithm to train fuzzy cognitive maps, International Journal of Approximate Reasoning 37 (2004) 219-249.
  • [22] A.L. Laureano Cruces, A. Terán-Gilmore, F. de Arriaga, A Learning Model Based on a Didactic Cognitive Approach: The Case Of Single-Degree-Of-Freedom Systems, Computer Applications in Engineering Education, to appear, 2004.
  • [23] W. Froelich, A. Wakulicz-Deja, Learning Fuzzy Cognitive Maps from the Web for the Stock Market Decision Support System, Advances in intelligent Web Mastering 43 (2007) 106-111.
  • [24] W. Stach, L. Kurgan, W. Pedrycz, M. Reformat, Evolutionary Development of Fuzzy Cognitive Maps, The IEEE International Conference on Fuzzy Systems, 2005, 619-624.
  • [25] S.K. Golmohammadi, A. Azadeh, A. Gharehgozli, Action Selection in Robots Based on Learning Fuzzy Cognitive Map, International Conference on Industrial Informatics IEEE, 2006, 731-736
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL8-0030-0006
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