An open and inspectable learner modeling with a negotiation mechanism to solve cognitive conflicts in an intelligent tutoring system

Some researchers have developed relevant and diverse proposals for improving the content quality of the learner model in Intelligent Tutoring Systems, mainly reducing its uncertainty. Following this aim, this paper proposes an open learner modeling approach using Bayesian networks, focusing on negotiation mechanism to solve detected cognitive conflicts that can emerge when the learner inspects information of his model inferred by the system. Therefore, we addressed some issues concerning the provision of inspectable model and negotiated updating of this model. Its contribution lies in the fact that the learners attempt to change the learner model is met with a challenge, leading to a decision if the learner claims to know more (or less) than the model represents.

In Personalization Approaches in Learning Environments Workshop at UMAP 2012