A New Smart Recommendation System of Learning Management Systems

A New Smart Recommendation System of Learning Management Systems

Copyright: © 2021 |Pages: 10
DOI: 10.4018/978-1-7998-4021-3.ch004
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Abstract

This chapter presents the functional and technical architecture of a recommendation system of free learning management systems that the authors have implemented to facilitate the choice of the most suitable LMS to meet the objectives, specifications, and criteria chosen by the institution. Thus, any random choice entails a loss of money, effort, and time loss for porters and device designers, and this is for various reasons (cost, utility, usability, etc.). Notably, this system takes into account more than 20 LMSs. The choice of these LMSs is based on a methodical and systemic approach that identifies the adequate criteria to the objectives and specifications chosen by the institution, depending on the objects and pedagogical tools related to the recommended teaching and learning device, to retain the most suitable LMS. This chapter is motivated by the desire to clarify and support users in their choice of the most suitable LMS to meet their needs and to get maximum benefit from the potential offered by technologies in pedagogy.
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Introduction

E-learning is a fast and efficient way of providing and sharing knowledge with learners in different parts of the world. According to (Liu, 2010; Karim and al., 2013), it is defined as the following: “E-learning uses the Internet or other digital content for learning and education activities, that takes full advantage of modern educational technology to provide a new mechanism for communication and learning environment rich in resources to achieve a new way of learning.”

In the 20th century, there was an international movement in favor of e-learning integration in higher education. This movement has been operationalized due to the variety of the educational offer by universities, which most have opted to diversify knowledge dissemination means (sounds, images, animations ... etc.) to meet the needs of their target public. If access to knowledge was previously conditioned by the physical presence in the classroom, technology enables its learners to exceed this condition of presence and be opened towards other learning modalities today. We can say that e-learning provides solutions in the context of distance learning without claiming to represent the solution to all educational dysfunctions. Among these solutions, distance learning seems to be the challenge ahead to face the new training requirements in the digital era.

In the case of our study, the e-learning solutions that interest us are free LMSs, because their costs, their states of development, their directions, and used technologies rendered them very close to the axis of this study.

During the last decade, LMSs have evolved a lot. However, several comparative studies have been developed previously (Dimet, 2006; Menasri, 2004; DOGBE-SEMANOU and al., 2007; Kaddouri and Bouamri, 2010; EL MAWAS and al., 2014; OVAREP, 2000; Graf and List, 2005; Galloy and al., 2002; Caro Dambreville and al., 2008), but they have been abandoned because their life cycle is changing apace. In such a context of proliferation of the training, the choice becomes difficult.

Consequently, any random choice causes a loss of money, effort, and time.

Thereby, we developed a recommendation system of LMSs, based on a comparative and analytical study of the free LMSs (Ouadoud and al., 2016); to facilitate the choice of the most suitable LMS according to the objectives and specifications of any institution, and this is based on an evaluation approach of the LMSs quality (Ouadoud and al., 2016).

For our study, in section 3, we present the functional architecture of the recommendation system of used LMSs. This section mainly consists of three subsections:

  • 1.

    The system will facilitate the selection of the LMS and will allow choosing the one that suits best your training ecosystem.

  • 2.

    The system gives you the possibility to choose the characteristics and pedagogical tools adapted to your specifications.

  • 3.

    The system does the analysis and treatment of the choices obtained, to recommend the most suitable LMS to institutional goals.

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Leadertice.Com Recommendation System

LeaderTICE.com is a recommendation system of free LMSs, under the GNU / GPL V3 (Ouadoud, 2019), that we implemented (Ouadoud, 2019) to facilitate the choice of LMSs, seeing that any random choice entails money, effort, and time loss to porters and systems designers, and this is for various reasons (cost, utility, usability ...). Notably, this system takes into account several LMSs selected among 600 LMSs listed by the THOT CURSUS directory (LMSs directory, LMS, and LCMS LMSs).

The LeaderTICE.com system is mainly based on our evaluation approach of the LMSs' quality (Ouadoud and al., 2016). Several LMSs evaluation approaches were encountered in the literature (Lablidi and al., 2009; Aska and al., 2000). However, they have not been adopted, because these studies are focusing on the functional aspect mainly, forgetting other very important aspects such as security, maintainability, portability, compatibility, performance efficiency, and usability. For this reason, the implementation of the system is based on a methodical and systemic approach (Ouadoud and al., 2016) taking into account the software engineering aspects, learning theories, and current educational tools. Our main goal is to propose the most suitable LMS, which meshes with the objectives and contexts of teaching and learning of any institution.

Key Terms in this Chapter

HTTP: Hypertext transfer protocol.

MTA: Mail transfer agent.

SMTP: Simple mail transfer protocol.

THOT CURSUS: Is a directory of LMSs, LCMS, and other systems for content and learning management.

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