Causal Relevancy Approaches to Improve the Students' Prediction Performance in an e-Learning Environment

In this work, four different causal relevancy (CR) approaches are implemented within the inference engine of the fuzzy inductive reasoning (FIR) methodology. The idea behind CR is to quantify how much influence each system feature has, on the forecasting of the output. This paper presents and discusses the FIR inference engine, and describes how it can be enhanced using the causal relevancy methods proposed in this study. The first two CR methods compute the relevancy of each feature by means of the quality of the optimal mask, obtained in the qualitative model identification step of the FIR methodology. The last two CR methods are based on the prediction error of a validation data set, not used in the model identification process. The CR approaches presented in the paper are applied to a real e-learning course with the goal of improve studentspsila behavior predictions. The experiments carried out with the available data indicate that lower prediction errors are obtained using the CR approaches when compared with the results obtained by the classical FIR inference engine. The new approaches help to improve the understanding of the educative process by describing how much influence each system feature has on the output.