abstract
- © Springer International Publishing Switzerland 2015. Sharing student models has long been a problem of interest for the AIED community. Current proposals can use student models that use machine learning, but can¿t modify them. We propose a multiagent architecture for decoupled student models that enables machine learning components to: train with different data, choose what data is more reliable, and to compensate in case its sources of information are missing. The architecture uses a fragmented user model approach. The expected contributions are the architecture for sharing student models and its implementation, guidelines for decoupling or extracting implemented models from intelligent tutoring systems and intelligent learning environments, and an analysis of the portability of current state of the art student models.