abstract
- © 2016, International Artificial Intelligence in Education Society.Sharing user information between systems is an area of interest for every field involving personalization. Recommender Systems are more advanced in this aspect than Intelligent Tutoring Systems (ITSs) and Intelligent Learning Environments (ILEs). A reason for this is that the user models of Intelligent Tutoring Systems and Intelligent Learning Environments, i.e. their student models, tend to be more heterogeneous and complex than traditional models used in Recommender Systems. To share and reuse student models we must first understand the restrictions for porting or reusing student models in new ITSs or ILEs. This paper proposes a classification of student models in terms of their portability. Portability is measured via each model¿s accessibility, complexity, architecture, popularity, and description. We use this classification to analyse and then grade student models that have been published in the AIED, EDM and ITS research communities in 2013 and 2014. The classification is intended to be used by researchers both as a methodology to measure the portability of a student model and as a guide to find existing reusable models.