Implementing a Probabilistic Learner Model Into a Course Creation Application

Introduction

Adaptive e-learning systems often employ learner models. A learner model 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. Before this model can be used, it must be built. This process requires a lot of effort to collect the required information and ultimately generate a learner model. Thus, an adaptive e-learning system takes all the properties of adaptive systems. To meet the needs of the application in the field of e-learning, adaptive e-learning systems adapt the learning material using user templates.

The behavior of an adaptive system varies depending on the data from the learner model and the learner’s profile. Without knowing anything about the learner who uses the system, a system would behave in exactly the same way for all learners. We try in this chapter, to implement the probabilistic learner model (Anouar Tadlaoui et al., 2019) in the LMS-LD Course creation application COPROLINE (El Moudden et al., 2015) to give this application the possibility to adapt the learning path of each learner individually, following the three steps of implementation: data collection, initialization and the update of the learner model.

We will start by presenting the learner model in adaptive hypermedia, the characteristics and components of this model. Then, we will present the modalization of this module based on multi entity Bayesian networks and artificial intelligence. And, we will follow the steps of implementation by presenting the results of each step beginning by the data collection and the initialization and the update phase. 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.