A soft computing decision support framework to improve the e-learning experience

In this paper an e-learning decision support framework based on a set of soft computing techniques is presented. The framework is mainly based on the FIR methodology and two of its key extensions: a set of Causal Relevance approaches (CR-FIR), which allows reducing uncertainty during the forecast stage; and a Rule Extraction algorithm (LR-FIR), that extracts comprehensible, actionable and consistent sets of rules describing students' learning behavior. The analyzed data set was gathered from the data generated from user's interaction with an e-learning environment. The introductory course data set was analyzed with the proposed framework with the goal to help virtual teachers to understand the underlying relations between the actions of the learners, and make more interpretable the student's learning behavior. The obtained results improve the system understanding and provide valuable knowledge to teachers about the course performance.