Genetic fuzzy system for predictive and decision support modelling in e-learning

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Abstract
In this research a genetic fuzzy system (GFS) is proposed that performs discretization parameter learning in the context of the Fuzzy Inductive Reasoning (FIR) methodology and the Linguistic Rule FIR (LR-FIR) algorithm. The main goal of the GFS is to take advantage of the potentialities of GAs to learn the fuzzification parameters of the FIR and LR-FIR approaches in order to obtain reliable and useful predictive (FIR) models and decision support (LR-FIR) models. The GFS is evaluated in an e-learning context, with two main goals: obtaining FIR predictive models that allow teachers to predict student's performance and obtaining LR-FIR decision support models that allow teachers to understand students' learning behaviour. The obtained results indicate that the metrics that assess the performance of both kinds of models are better when the discretization parameters are learned by GAs instead of using the expert's criteria.

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