- 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",
- broken = "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",
