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

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