Implementing a Probabilistic Learner Model Into a Course Creation Application

Implementing a Probabilistic Learner Model Into a Course Creation Application

Mouenis Anouar Tadlaoui (Abdelmalek Essaâdi University, Morocco), Fauzi El Moudden (Abdelmalek Essaâdi University, Morocco) and Mohamed Khaldi (Abdelmalek Essaâdi University, Morocco)
Copyright: © 2020 |Pages: 15
DOI: 10.4018/978-1-7998-1492-4.ch012

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.
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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.

Key Terms in this Chapter

Learner Situation: A part of a learning path that the learner takes to achieve a diploma or a certification.

E-Learning: A concept that describes the cognitive science principles of effective multimedia learning using electronic educational technology.

Learner Profile: A part of the learner model that only contain the static information of the learner that could be gathered before developing a learner model.

Adaptive Hypermedia Systems: An online information and help systems, as well as institutional information systems that provide hyperlinks that are most relevant to the user in an effort to shape the user's cognitive load.

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