Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities

Pavla Dráždilová, Gamila Obadi, Kateřina Slaninová, Shawki Al-Dubaee, Jan Martinovič, Václav Snášel

Research output: Chapter in Book/Report/Conference proceedingChapter

15 Citations (Scopus)

Abstract

Enhancing the the effectiveness of web-based eduction has become one of the most important concerns within both educational engineering and information system fields. The development of information technologies has contributed to the growth in elearning as an important education method. This learning environment enables learners to participate in ’any time, any place’ personalized training. It has been known that the application of data mining and computational intelligent approaches can provide better learning environments, and in their effort to participate in this field, the authors introduced this study which consists in its first part of a survey of the applications of data mining and computational intelligence in web based education during (2004-2009), and the second part is a case study that aims to analyze students’ activities performed in a Learning Management System.
Original languageEnglish
Title of host publicationComputational Intelligence for Technology Enhanced Learning
Place of PublicationBerlin
PublisherSpringer
Pages195-224
Number of pages30
Volume273
ISBN (Print)9783642112232
DOIs
Publication statusPublished - 2010

Publication series

NameStudies in Computational Intelligence
Volume273

Keywords

  • data mining
  • association rule
  • soft computing
  • association rule mining
  • data mining technique

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  • Cite this

    Dráždilová, P., Obadi, G., Slaninová, K., Al-Dubaee, S., Martinovič, J., & Snášel, V. (2010). Computational Intelligence Methods for Data Analysis and Mining of eLearning Activities. In Computational Intelligence for Technology Enhanced Learning (Vol. 273, pp. 195-224). (Studies in Computational Intelligence; Vol. 273). Springer. https://doi.org/10.1007/978-3-642-11224-9_9