Improving Online Education Using Big Data Technologies

In a world in full digital transformation, where new information and communication technologies are constantly evolving, the current challenge of Computing Environments for Human Learning (CEHL) is to search the right way to integrate and harness the power of these technologies. In fact, these environments face many challenges, especially the increased demand for learning, the huge growth in the number of learners, the heterogeneity of available resources as well as the problems related to the complexity of intensive processing and real-time analysis of data produced by e-learning systems, which goes beyond the limits of traditional infrastructures and relational database management systems. This chapter presents a number of solutions dedicated to CEHL around the two big paradigms, namely cloud computing and Big Data. The first part of this work is dedicated to the presentation of an approach to integrate both emerging technologies of the big data ecosystem and on-demand services of the cloud in the e-learning field. It aims to enrich and enhance the quality of e-learning platforms relying on the services provided by the cloud accessible via the internet. It introduces distributed storage and parallel computing of Big Data in order to provide robust solutions to the requirements of intensive processing, predictive analysis, and massive storage of learning data. To do this, a methodology is presented and applied which describes the integration process. In addition, this chapter also addresses the deployment of a distributed e-learning architecture combining several recent tools of the Big Data and based on a strategy of data decentralization and the parallelization of the treatments on a cluster of nodes. Finally, this article aims to develop a Big Data solution for online learning platforms based on LMS Moodle. A course recommendation system has been designed and implemented relying on machine learning techniques, to help the learner select the most relevant learning resources according to their interests through the analysis of learning traces. The realization of this system is done using the learning data collected from the ESTenLigne platform and Spark Framework deployed on Hadoop infrastructure

Identification of the causes of onion (Allium cepa) post-harvest losses in Morocco

L’oignon figure parmi les principales cultures maraîchères au Maroc. Cependant, plusieurs types de pertes peuvent avoir lieu après leur récolte. Notre objectif est d’étudier la situation de cette filière en post récolte et d’identifier les causes et les contraintes rencontrées au cours de la mise en vente et de la consommation de ce produit dans la région de Khémisset (Maroc). L’enquête auprès de certains acteurs de cette filière nous a permis de déterminer les types et l’ampleur des pertes. Les dégâts observés sont causés principalement par des facteurs mécaniques et pathologiques. Nous avons identifié deux champignons phytopathogènes, Fusarium sp. et Aspergilus niger. En outre, l’étude a révélé des pertes moyennes d’environ 17% chez les grossistes, 15 % chez les commerçants et 27% chez les ménages. Mots-clés: Pertes en post-récolte, oignon, champignons phytopathogènesOnion is one of the main commodities in Morocco. However, several types of losses may occur at postharvest. Our objective is to study the situation of this post-harvest sector and to identify the causes and constraints encountered during the sale and consumption of this product in Khémisset region (Morocco). The survey of certain stakeholders allowed us to determine the types and extent of losses. The damage observed is caused mainly by mechanical and pathological factors. We identified two phytopathogenic fungi, Fusarium sp. and Aspergilus niger. In addition, we found that average losses were of about 17% among wholesalers, 15% among traders and 27% among households. Keywords: Postharvest losses, onion, phytopathogenic fung

PLATEFORME MULTISERVICE INNOVANTE MEDECIN/PATIENT A BASE DE RESEAU DE CAPTEURS

Dans les hôpitaux, chaque patient voit ses constantes physiologiques surveillées en permanence, jour et nuit, par une infirmière. Par conséquent, il est préférable d’avoir des outils et des appareils sans fil pour la prise des données vitales sur une personne hospitalisée, permettant une diminution des erreurs et des oublis de notification dans le dossier médical. Il est avéré que la guérison la plus rapide a lieu au domicile même du patient ; c'est pourquoi les médecins souhaitent le développement de capteurs ou moniteurs à technologies sans fil pour suivre l'évolution d'une thérapie ou d'un traitement à distance. Celle-ci deviendra donc propre à chaque personne et adaptable selon les besoins. Chaque citoyen devient de plus en plus acteur de sa santé. Par exemple, un patient peut consulter le nom et le dosage d’un médicament spécifique à prendre en suivant les conseils et les messages des médecins figurant sur son compte depuis son ordinateur personnel. Grâce aux capteurs, la société peut permettre aux malades cardiaques de mener une vie indépendante et à domicile tant qu'elles en sont capables. Ce papier présente notre réflexion sur la mise en place d’un système à base des réseaux de capteurs dans le domaine de télémédecine, et spécialement le suivi de l’état des malades cardiaques à domicile

Design and Implementation of P2P Based Mobile App for Collaborative Learning in Higher Education

Within the recent years, mobile technologies have made major progress in its de-velopment. They have become an integral part of our daily life. Therefore, the big interest of using mobile technology in education process is comprehensible. A new technology was born called mobile learning (m-learning). The main benefits of this technology is sharing three important dimensions of learning resources namely learning contents, learning collaborators and learning services. In univer-sity courses, different forms of collaborative and peer learning are progressively used in order to assist students meet an assortment of learning outcomes. There-fore, the use of mobile technologies in education has become a crucial topic for research. Because mobile phone is the first choice of everyone to communicate, our research is focused on the use of smartphones as learning resources. In this paper, we propose an implementation of a new mobile application based peer-to-peer learning system named "EachOther" and a scenario for its usage applied on students of an IT course at the High School of Technology of Fez (ESTF). The aim of this work consists of the conception, the development and the implementa-tion of the mobile application based on Android system to support collaborative learning, by applying an object-oriented modeling language which is Unified Modeling Language (UML)

Approach for Eliciting Learners' Preferences in Moocs Through Collaborative Filtering

Les MOOC (massive open online courses) deviennent rapidement des incontournables pour assurer la continuité pédagogique et accompagner la vie et les modes de travail futurs. Il est donc nécessaire que les MOOC s'éloignent de leur ancien modèle. Ce cadre présentera un système de recommandation basé sur un algorithme qui utilisera un filtrage collaboratif basé sur les préférences des apprenants MOOC. Le filtrage collaboratif est une technique permettant d'anticiper les intérêts d'un utilisateur en étudiant les préférences des utilisateurs similaires à l'individu en question. Cette approche assure l'analyse de nombreux éléments en utilisant le choix de notation des participants.Un système de recommandation devient de plus en plus courant dans les activités d'étude en ligne ; nous voulons étudier comment cela pourrait aider à l'apprentissage et favoriser une implication plus efficace. Nous baserons notre système de recommandation proposé sur l'évaluation du contenu du cours. L'idée est que les apprenants évaluent les cours et le contenu auxquels ils se sont inscrits sur la plateforme entre 1 et 5. Suite à l'évaluation, nous extrayons les données dans un fichier de valeurs séparées par des virgules (CSV) et utilisons la programmation Python pour fournir des recommandations à l'aide des données de apprenants avec des modèles de notation similaires. Le but était d'utiliser la programmation Python pour proposer des cours à différents utilisateurs en mode éditeur de texte. Nous utiliserons des modèles d'évaluation similaires via un filtrage collaboratif pour recommander des cours à divers apprenants, améliorant ainsi leur expérience d'apprentissage et leur passion

Design and Implementation of P2P Based Mobile App for Collaborative Learning in Higher Education

Within the recent years, mobile technologies have made major progress in its de-velopment. They have become an integral part of our daily life. Therefore, the big interest of using mobile technology in education process is comprehensible. A new technology was born called mobile learning (m-learning). The main benefits of this technology is sharing three important dimensions of learning resources namely learning contents, learning collaborators and learning services. In univer-sity courses, different forms of collaborative and peer learning are progressively used in order to assist students meet an assortment of learning outcomes. There-fore, the use of mobile technologies in education has become a crucial topic for research. Because mobile phone is the first choice of everyone to communicate, our research is focused on the use of smartphones as learning resources. In this paper, we propose an implementation of a new mobile application based peer-to-peer learning system named "EachOther" and a scenario for its usage applied on students of an IT course at the High School of Technology of Fez (ESTF). The aim of this work consists of the conception, the development and the implementa-tion of the mobile application based on Android system to support collaborative learning, by applying an object-oriented modeling language which is Unified Modeling Language (UML)

Approach for Eliciting Learners' Preferences in Moocs Through Collaborative Filtering

Les MOOC (massive open online courses) deviennent rapidement des incontournables pour assurer la continuité pédagogique et accompagner la vie et les modes de travail futurs. Il est donc nécessaire que les MOOC s'éloignent de leur ancien modèle. Ce cadre présentera un système de recommandation basé sur un algorithme qui utilisera un filtrage collaboratif basé sur les préférences des apprenants MOOC. Le filtrage collaboratif est une technique permettant d'anticiper les intérêts d'un utilisateur en étudiant les préférences des utilisateurs similaires à l'individu en question. Cette approche assure l'analyse de nombreux éléments en utilisant le choix de notation des participants.Un système de recommandation devient de plus en plus courant dans les activités d'étude en ligne ; nous voulons étudier comment cela pourrait aider à l'apprentissage et favoriser une implication plus efficace. Nous baserons notre système de recommandation proposé sur l'évaluation du contenu du cours. L'idée est que les apprenants évaluent les cours et le contenu auxquels ils se sont inscrits sur la plateforme entre 1 et 5. Suite à l'évaluation, nous extrayons les données dans un fichier de valeurs séparées par des virgules (CSV) et utilisons la programmation Python pour fournir des recommandations à l'aide des données de apprenants avec des modèles de notation similaires. Le but était d'utiliser la programmation Python pour proposer des cours à différents utilisateurs en mode éditeur de texte. Nous utiliserons des modèles d'évaluation similaires via un filtrage collaboratif pour recommander des cours à divers apprenants, améliorant ainsi leur expérience d'apprentissage et leur passion

Adoption of MOOCs by Emerging Countries Seeking Solutions to University Overcrowding: Literature Review and Feedback from the First Scientific MOOC Held by Sidi Mohammed Ben Abdullah University – Fez, Morocco

The adoption of various forms of distance education, particularly MOOCs (an acronym for Massive Open Online Courses), by universities worldwide has continuously gained momentum over the past decade. This is due not only to the importance of maintaining a parallel educational model alongside face-to-face courses in order to complete students’ training, but also in response to the limits of academic infrastructure faced with an increasingly large mass of learners, typically in emerging countries. Universities view MOOCs as a remedy to this dilemma—one which promises reasonable development costs—especially taking into account the ubiquity of the internet and digital communication tools. In a country such as Morocco, whose university capacity has been stretched to 186%, the quest to dematerialize lectures can support universities in producing well-rounded professional profiles as well as improving institutional and academic services overall. In this paper, we present the feedback from Sidi Mohammed Ben Abdellah University concerning its first scientific MOOC, launched within the framework of the MarocUniversitéNumérique (Morocco Digital University) or MUN project in collaboration with the France UniversitéNumérique (France Digital University) or FUN platform. The objectives of this paper are threefold: to assess the possibility of adopting further MOOCs in a Moroccan setting, to seek insight on the profiles of learners who have completed MOOCs and to draw lessons in order to improve future experiences

Large-scale e-learning recommender system based on Spark and Hadoop

Abstract The present work is a part of the ESTenLigne project which is the result of several years of experience for developing e-learning in Sidi Mohamed Ben Abdellah University through the implementation of open, online and adaptive learning environment. However, this platform faces many challenges, such as the increasing amount of data, the diversity of pedagogical resources and a large number of learners that makes harder to find what the learners are really looking for. Furthermore, most of the students in this platform are new graduates who have just come to integrate higher education and who need a system to help them to take the relevant courses that take into account the requirements and needs of each learner. In this article, we develop a distributed courses recommender system for the e-learning platform. It aims to discover relationships between student’s activities using association rules method in order to help the student to choose the most appropriate learning materials. We also focus on the analysis of past historical data of the courses enrollments or log data. The article discusses particularly the frequent itemsets concept to determine the interesting rules in the transaction database. Then, we use the extracted rules to find the catalog of more suitable courses according to the learner’s behaviors and preferences. Next, we deploy our recommender system using big data technologies and techniques. Especially, we implement parallel FP-growth algorithm provided by Spark Framework and Hadoop ecosystem. The experimental results show the effectiveness and scalability of the proposed system. Finally, we evaluate the performance of Spark MLlib library compared to traditional machine learning tools including Weka and R

A multi-formalism method for designing and validating intelligent agent-based systems

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