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AI Methods for a Prediction of the Pedagogical Efficiency Factors for Classical and e-Learning System

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Artifical Intelligence and Soft Computing (ICAISC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6114))

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Abstract

The idea to apply the selected AI methods for the determination and prediction of the pedagogical efficiency of the classical and e-learning systems have been described in the paper. The partial and total information functions have been defined for such systems treated like the information systems. The values of the partial information function for the system elements or granules of them are the pedagogical efficiency factors and it is possible to use them for the prediction of the total pedagogical efficiency factor of systems. It is possible to do a prediction only by the AI methods.

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References

  1. Przybyszewski, K.: Application of the Fuzzy Sets for Evaluation of the Different Aspects of the Learning Systems. In: Automatyka, vol. 3 (12), pp. 1033–1045. AGH Academic Press, Krakow (2008) (in Polish)

    Google Scholar 

  2. Przybyszewski, K., Cader, A., Filutowicz, Z.: An Automation and Objectivization of the evaluation process of the learning/teaching processes. In: Cader, A., et al. (eds.) Selected Problems of the Knowledge Engineering, pp. 36–76. SWSPiZ Academic Press, Lodz (2008)

    Google Scholar 

  3. Niewiadomski, A., Kryger, P., Szczepaniak, P.S.: Fuzzyfication of Indiscernibility Relation for Structurizing Lists of Synonyms and Stop-Lists for Search Engines. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 504–509. Springer, Heidelberg (2004)

    Google Scholar 

  4. Pawlak, Z.: Rough Set Elements. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery. Springer, Heidelberg (1998)

    Google Scholar 

  5. Rutkowski, L.: Methods and techniques of artificial intelligence. PWN Press, Warszawa (2005) (in Polish)

    Google Scholar 

  6. Krus, L.: Problems of the Decision Support Systems Construction. In: Kulikowski, R., et al. (eds.) Computer and System Support for the Knowledge Management, pp. 141–151. AOW Exit, Warszawa (2006) (in Polish)

    Google Scholar 

  7. Przybyszewski, K., Filutowicz, Z., Cader, A.: Standardization of the Students Evaluation Results for a Prediction of the Classical and E-education System Efficiency. In: Niedwiedziski, M., Lange-Sadziska, K. (eds.) Selected Problems of the E-platforms Applifications, pp. 21–32. Consulting Press, Lodz (2009) (in Polish)

    Google Scholar 

  8. Tadeusiewicz, R.: Cybernetic Model of the Computer Supported Learning. In: Automatyka, vol. 3 (8), pp. 643–664. AGH Academic Press, Krakow (2004) (in Polish)

    Google Scholar 

  9. Przybyszewski, K.: A new evaluation method for E-learning systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1209–1216. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  10. Przybyszewski, K.: The Application of Selected AI Methods to the Evaluation of Students Progress. In: Niedwiedziski, M., Lange-Sadziska, K. (eds.) Selected Problems of E-economy, pp. 163–173. Consulting Press, Lodz (2008) (in Polish)

    Google Scholar 

  11. Przybyszewski, K., Cader, A., Filutowicz, Z.: Information management in the interactive e-learning systems. Scientific Biull. WSHE 4(9), 90–102 (2000) (in Polish)

    Google Scholar 

  12. Pokropek, A.: Accuracy of the Educational Added Value Method. In: Educationa Added Value. Part 2, CKE Research Biull. no 14, Warszawa, pp. 100–139 (2007) (in Polish)

    Google Scholar 

  13. Przybyszewski, K., Cader, A., Marchlewska, A.: On the Possibility of the Selected Methods of the Classical and E-learnings Systems Efficiency Methods. In: Automatyka, vol. 13(3). AGH Academic Press, Krakow (2009) (in Polish)

    Google Scholar 

  14. Guilford, J.P.: Fundamental Statistics in Psychology and Education. McGraw-Hill Book Company, New York (1942)

    Google Scholar 

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Author information

Authors and Affiliations

  1. IT Institute, Department of International Studies and Computer Science, Academy of Management in Lodz (SWSPiZ), Sienkiewicza 9, Lodz, Poland

    Krzysztof Przybyszewski

Authors
  1. Krzysztof Przybyszewski
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Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Academy of Humanities and Economics, Poland

    Leszek Rutkowski

  2. Academy of Humanities and Economics in Łódź, ul. Rewolucji 1905 nr 64, Łódź, Poland

    Rafał Scherer

  3. Institute of Automatics, AGH University of Science and Technology, Al. Mickiewicza 30, PL-30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  4. Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley Initiative in Soft Computing (BISC), 94720-1776, CA

    Lotfi A. Zadeh

  5. Computational Intelligence Laboratory Department of Electrical and Computer Engineering, University of Louisville, 40292, Louisville, KY

    Jacek M. Zurada

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Przybyszewski, K. (2010). AI Methods for a Prediction of the Pedagogical Efficiency Factors for Classical and e-Learning System. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artifical Intelligence and Soft Computing. ICAISC 2010. Lecture Notes in Computer Science(), vol 6114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13232-2_78

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  • DOI: https://doi.org/10.1007/978-3-642-13232-2_78

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13231-5

  • Online ISBN: 978-3-642-13232-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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