Implementing a Probabilistic Learner Model Into a Course Creation Application

Abstract

In the context of e-learning systems, we can distinguish between two different types of settings. First, we have adaptable systems, which refer to the property of changing the system settings. The learner can change the behavior of the system. Then, the learner is able to customize the system in a specified way to fit the needs of users. The learner model is the key element to generate the adaptation of the system to each specific user. It is an internal representation of the user's properties through which the system is based in order to adapt to the needs of each user. The authors present in this chapter the implementation of a probabilistic learner model developed based on multi-entities Bayesian networks and artificial intelligence into a course creation application (COPROLINE) compatible with LMS-LD. The results presented in this work are arguments in our favor for the implementation of a learner model to endorse the adaptation into some learning situations that the learners have followed during a year of testing.