APPLYING EVOLUTIONARY ALGORITHMS AS DATA MINING METHODS TO IMPROVE WEB-BASED ADAPTIVE HYPERMEDIA COURSES

Abstract

This paper shows how to use evolutionary algorithms to data mining methods for discovering prediction rules in databases. These rules will be used to improve web-based adpative hypermedia courses. The idea is to discover important rules among the usage data picked up during the students’ execuctions. This information may be very useful to the author of the course, who can decide what modification will be the most apropiate to improve the performance of students. In order to do the discovering of rules we have used grammar-based genetic programming (GBGP) with multiobjective optimization technics. In this work we also present a graphic tools for discovering rules to facilitate the usage of the proposed methodology to improve the courses.

Keywords

• Minería de Datos – Cursos Web Adaptativos – Algoritmos Evolutivos – Reglas de Predicción.