Systemic-based, predictive model for e-learning

This papers is a summary of a research proposal oriented to develop a systemic-based, predictive model for e-learning. The purpose is to forecast student's performance on learning and identify who is on risk to fail. The rationale is to build an isomorphic model mining the interactions between the student and Learning Management System (LMS), the student's motivation, emotions and environment then the student's learning will be stimulated using tailored Learning Objects (LO) and professor's scaffolding. Using Beer's account of scientific modeling and Soft System Methodology the model will be developed and tested. The novelty is to present a holistic solution to the problem of student's failure that includes inner components (emotions and motivation) and outer component (scaffolding) hosted by a supra system (learning environment).