Rule mining with GBGP to improve web-based adaptive educational systems

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InCollection{RVHE2006,
  author =       "C. Romero and S. Ventura and C. Hervas and 
                 P. Gonzalez",
  title =        "Rule mining with GBGP to improve web-based adaptive
                 educational systems",
  booktitle =    "Data mining in e-learning",
  publisher =    "WitPress",
  year =         "2006",
  editor =       "C. {Romero Morales} and S. Ventura",
  volume =       "4",
  series =       "Advances in Management Information",
  pages =        "171--188",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-84564-152-3",
  URL =          "http://library.witpress.com/pages/PaperInfo.asp?PaperID=18311",
  URL =          "http://library.witpress.com/pages/listPapers.asp?q_bid=392",
  abstract =     "In this chapter we describe how to discover
                 interesting relationships from student's usage
                 information to improve adaptive web courses.We have
                 used AHA! to make courses that adapt both the
                 presentation and the navigation depending on the level
                 of knowledge that each particular student has.We use
                 data mining methods for providing feedback to
                 courseware authors.

                 The discovered information is presented in the form of
                 prediction rules since these are highly comprehensible
                 and they show important relationships among the
                 presented data. The rules will be used to improve
                 courseware, specially Adaptive Systems for Web-based
                 Education.

                 We propose to use grammar-based genetic programming
                 (GBGP) with multi-objective optimisation techniques as
                 the rule discovery method.

                 We have developed a specific tool named EPRules
                 (Education Prediction Rules) to facilitate the
                 knowledge discovery process for non-experts users in
                 data mining.",
  notes =        "doi:10.2495/1-84564-152-3/10 seems to direct to web
                 pages",
  size =         "17 pages",
}

Genetic Programming entries for Cristobal Romero Morales Sebastian Ventura Cesar Hervas Martinez Pedro Gonzalez Espejo

Citations