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

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 . We propose to use (GBGP) with multi-objective optimization techniques as rule discovery method. We have developed a specific tool named EPRules (Education Prediction Rules) to facilitate the knowledge discovery process to non-experts users in data mining.