Adapted Learning Path Using Genetic Algorithm: Introducing Fuzzy Logic

Abstract

In this work, searching for the path adapted to the learner profile and to the pedagogic objective of the formation has being automated. We have studied the problem as an “Optimization Problem”. Using Genetic Algorithms, the system seeks an optimal path starting from the learner profile to the pedagogic objective passing by intermediate courses. This application allows designing a descriptive sheet for each resource existing in the database, in XML format, to facilitate their reuse in the adaptive e-learning. This sheet is conceived basing on the pre-requisite and post-acquired concepts of the course in question, and is used as an input for our system in order to provide each learner with the resources that are most adapted to his profile. This paper discusses the approach of using fuzzy logic to present the input data used in our application and then the optimization using this data. We propose a fuzzy module to integrate in our adaptive e-learning system for selecting a new adapted path. Due to the use of genetic algorithms, we need to separate fuzzy module from other modules in order to keep the same degree of complexity. Thus the system will be more efficient.