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Zastosowanie technik eksploatacji danych do budowania grup studenckich

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Warianty tytułu
EN
Data mining techniques for buildings student groups
Języki publikacji
PL
Abstrakty
PL
Znalezienie grup studentów o podobnych preferencjach umożliwi dopasowanie do ich potrzeb systemu nauczania na odległość. Celem pracy jest porównanie różnych technik eksploracji danych do budowania grup. Rozważa się zastosowanie klasyfikacji bez nadzoru oraz po nadzorem, jak również wykrywania wzorców sekwencji.
EN
Finding student groups of similar preferences enables to adjust e-learning systems according to their needs. In the paper, it is compared usage of different data mining techniques for creating learners' groups. It is considered application of supervised and unsupervised classification as well as frequent pattern mining.
Czasopismo
Rocznik
Strony
335--346
Opis fizyczny
Bibliogr. 28 poz.
Twórcy
Bibliografia
  • 1. Brusilovsky P., Peylo C: Adaptive and intelligent web-based educational systems. ln:Lr. national Journal of Artificial Intelligence in Education, Vol.13,2003, s. 156-169.
  • 2. Stash N., Cristea A., De Bra P.: Authoring of learning styles in adaptive hypermedia; problems and solutions. Proceedings of WWW Conference, New York 2004 s. 114-123.
  • 3. Brusilovsky P.: Adaptive hypermedia, Use Model. User-Adap., Vol. 11,2001, s. 87+1 Id
  • 4. Beaudoin M. F.: Learning or lurking? Tracking the "invisible" online student. Internet Higher Education, Vol. 5,2002, s. 147-155.
  • 5. Lu J., Yu C. S., Liu G: Learning style, learning patterns and learning performance m a WebCT-based MIS course. Inform. Manage., Vol. 40,2003, s. 497-507.
  • 6. Li Z., Sun Y., Liu M.: A web-based intelligent tutoring system. Artificial Intelligence and Innovations AIAI2005. IFIP International Federation for Information Processus Vol. 187, Springer, Boston 2005, s. 583-591.
  • 7. Cha H. J., Kim Y. S., Park S. H., Yoon T. B., Jung Y. M., Lee J.-H.: Learning styles di.ib- nosis based on user interface behaviors for customization of learning interfaces in an inu.,- ligent tutoring system. Ikeda M., Ashley K., Chan T.-W., (eds.) ITS2006, LNCS, Vol, 4053, Springer, Heidelberg 2006, s. 513-524.
  • 8. Marcus A.: Designing graphical interfaces. Unix World, October 1990.
  • 9. Bauersfeld P. F., Slater J. L.: User-oriented color interface design: direct manipulation (•"• color in context. Proceedings of SIGCHI Conference on Human Factors in Computm" Systems: Reaching through Technology, New Orleans 2001, s. 417-418.
  • 10. Zakrzewska D., Wojciechowski A.: Identifying students usability needs in collaborati e learning environments. Proceedings of 2008 Conference on Human System Intel actu Kraków 2008, s. 862-867.
  • 11. Chen G., Liu C, Ou K., Liu B.: Discovering decision knowledge from web log portfolio for managing classsroom processess by applying decision tree and data cube technology Journal of Educational Computing Research, Vol. 23,2000, s. 305-332.
  • 12. Beck J., Woolf B.: High-level student modeling with machine learning. Proceedings of the 5th International Conference on Intelligent Tutoring System, 2000, s. 584-593.
  • 13. Arroyo I., Woolf B. P.: Inferring learning and attitudes from a Bayesian Network of log file data. Proceedings of the 12th International Conferenec on Artificial Intelligence in Education, 2005, s. 33-40.
  • 14. Perera D., Kay J., Koprinska I., Yacef K, Zad'ane O. R.: Clustering and sequential pattern mining of online collaborative learning data. IEEE T. Knowl. Data En., Vol. 21, 2009, s. 759-772.
  • 15. Talavera L., Guaudioso E.: Mining student data to characterize similar behavior groups in unstructured collaboration spaces. Workshop on Artificial Intelligence in CSCL. 16th European Conference on Artificial Intelligence, 2004, s. 17+23.
  • 16. Zakrzewska D.: Cluster analysis in personalized e-learning systems. Nguyen N. T. & Szczerbicki E. (Eds.): Intelligent Systems for Knowledge Management, Studies in Computational Intelligence, Vol. 252, Springer, Berlin Heidelberg 2009, s. 229-250.
  • 17. Tang T., McCalla G.: Smart recommendation for an evolving e-learning system. International Journal on E-Learning, Vol. 4,2005, s. 105-129.
  • 18. Shen R., Han P., Yang F., Yang Q., Huang J.: Data mining and case-based reasoning for distance learning. Journal of Distance Education Technologies, Vol. 1,2003, s. 46+58.
  • 19. Minaei-Bidgoli B., Tan P., Punch W.: Mining interesting contrast rules for a web-based educational system. The Twenty-First International Conference on Machine Learning Applications, 2004, s. 1-8.
  • 20. Wang F.: On using data-mining technology for browsing log file analysis in asynchronous learning environment. Conference on Educational Multimedia, Hypermedia and Telecommunications, 2002, s. 2005-2006.
  • 21. Romero C, Ventura S.: Educational data mining: a survey from 1995 to 2005. Expert Syst. Appl., Vol. 33,2007, s. 135-146.
  • 22. Felder R. M., Silverman L. K.: Learning and teaching styles in engineering education. Eng. Educ, Vol. 78,1988, s. 674-681.
  • 23. Viola S. R., GrafS., Kinshuk, Leo T.: Investigating relationships within the index of learning styles: a data driven approach. Interactive Technology & Smart Education, Vol. 4, 2007, s. 7-18.
  • 24. ILS Questionnaire, http://www.engr.ncsu.edu/learningstyles/ilsweb.html
  • 25. Lowd D., Domingos P.: Naive Bayes models for probability estimation. Proceedings of 22nd International Conference on Machine Learning, Bonn, Germany, 2005.
  • 26. Kotsiantis S. B.: Supervised machine learning: a review of classification. Informatica, Vol. 31,2007, s. 249-268.
  • 27. Gower J.: A general coefficient of similarity and some of its properties. Biometrics, Vol. 27,1971, s. 857-874.
  • 28. . Witten I. H., Frank E.: Data Mining: Practical Machine Learning Tools and Techrd, 2nd Edition. Morgan Kaufmann Publishers, San Francisco 2005.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSL9-0051-0026
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