A learner model in a distributed environment

Abstract

A learner model must store all the relevant information about a student, including knowledge and attitude. This paper proposes a domain independent learner model based in the classical overlay approach that can be used in a distributed environment. The model has two sub-models: the learner attitude model, where the static information about the user is stored (user's personal and technical characteristics, user's preferences, etc.) and the learner knowledge model, where the user's knowledge and performance is stored. The knowledge model has four layers: estimated, assessed, inferred by prerequisite and inferred by granularity. The learner model is used as a part of the MEDEA system, so the first and second layers are updated directly by the components of MEDEA and the third and fourth are updated by Bayesian inference. © Springer-Verlag; 2004.