A comparative study of the use of fuzzy logic in e-learning systems

Abstract

Currently, there is an increasing interest in educational data mining. E-learning systems have become pervasive in educational contexts, and they are more necessary than ever in order to get relevant information on students and learning activities. However, the amount of raw data produced makes unfeasible its manual processing, so (semi-)automated solutions are required. In this context, the paradigms of data mining and artificial intelligence offer multiple techniques to provide these solutions. Among these techniques, this work makes a review of published and tested techniques with satisfactory results under the different aspects of fuzzy logic. The choice of the fuzzy logic makes sense here, as it is a theory close to some forms of human reasoning. In e-learning systems, the main actors are teachers and students, and the data to manipulate are related to their modelling and the results of their actions. Fuzzy techniques make possible a representation of this knowledge and its manipulation under a human approach, which is well-suited to be understood by the main actors of the domain.