Exploring the benefits of e-learning for life and earth sciences education in Moroccan high schools

The reliance on online learning systems has increased during the COVID-19 epidemic to maintain education. The effectiveness of online life and earth science instruction is assessed in this study which involves 150 first-year high school students. Methodologically, it juxtaposes e-learning with traditional classroom teaching across various parameters. The study reveals that digital learning yielded better results across all considered variables (p < 0.05), irrespective of student gender (p = 0.216). Better performance was seen in subjects such as "man and the environment" and "greenhouse effect and climate change" when learning was carried out online (p < 0.001). However, no notable scoring differences were found  in practical subjects such as " creation of  ecological  reserves to  preserve  biodiversity," "clean  technologies to protect the  environment" and  " environmental  education and  sustainable  development," (p = 0.627 and p = 0.147). Thus, e-learning proves to be a useful supplement to traditional instruction. It shouldn't be used in place of hands-on activities in all situations

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

E-Learning Recommendation System for Big Data Based on Cloud Computing

In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services

Politic of security, privacy and transparency in human learning systems

Abstract The preservation of confidentiality has become a major issue for themajority of applications that process personal information, the sensitivity of thisinformation requires creators to set rules for the sharing and use of access controlpolicies. A great deal of research has already been conducted in educationalenvironments. However, one aspect that has not received much attention is theimportant role of Information Security, especially in newer education environmentssuch as the e-learning environment. In this article we seek to propose apolicy of security in such a way to affect views of profiles for each user to controlthe information accessed by this user.10 Halama

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