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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
Pawlak, Z.: Rough Set Elements. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery. Springer, Heidelberg (1998)
Rutkowski, L.: Methods and techniques of artificial intelligence. PWN Press, Warszawa (2005) (in Polish)
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)
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)
Tadeusiewicz, R.: Cybernetic Model of the Computer Supported Learning. In: Automatyka, vol. 3 (8), pp. 643–664. AGH Academic Press, Krakow (2004) (in Polish)
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)
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)
Przybyszewski, K., Cader, A., Filutowicz, Z.: Information management in the interactive e-learning systems. Scientific Biull. WSHE 4(9), 90–102 (2000) (in Polish)
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)
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)
Guilford, J.P.: Fundamental Statistics in Psychology and Education. McGraw-Hill Book Company, New York (1942)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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)