The Impact of Implementing a Moodle Plug-in as an AI-based Adaptive Learning Solution on Learning Effectiveness: Case of Morocco

This article presents feedback on the implementation of an Artificial Intelligence-based adaptive learning Moodle plugin aimed at enhancing the engagement levels and academic performance of 102 Moroccan high school students. The primary objective of this study was to assess and compare the performance of students utilizing the adaptive learning system with those employing conventional learning methods. To guarantee the efficacy of this approach, a participant satisfaction survey and a comprehensive summative evaluation were conducted, revealing the positive impact of AI-based adaptive learning on the participants. The results of this study highlight the potential benefits of integrating AI-driven adaptive learning into high school computer science curricula, emphasizing how it may raise student engagement and academic performance. These results strengthen the determination to use this teaching methodology with students in future educational activities

AI-Based Adaptive Learning: A Systematic Mapping of the Literature

With the aid of technology advancement, the field of education has seen a noticeable transformation. The teaching-learning process is now more interactive and is no longer restricted to students' physical presence in the classroom but instead makes use of specialized online platforms. In recent years, solutions that offer learning routes customized to learners' needs have become more necessary. In this regard, artificial intelligence has served as an excellent answer, allowing for the building of educational systems that can accommodate a wide range of student needs. Through this paper, a systematic mapping of the literature on AI-based adaptive learning is presented. The examination of 93 articles published between 2000 and 2022 made it possible to draw several conclusions, including the number of adaptive learning environments based on AI, the types of AI algorithms used, the objectives targeted by these systems as well as factors related to adaptation. This study may serve as a springboard for further investigation into how to address the problems raised by the current state.&nbsp

Artificial Intelligence and Machine Learning in 5G and beyond: A Survey and Perspectives

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed

Mapping the Applications of Vehicular Communications in the Context of Smart Maritime Ports

The maritime transport networks play a critical and major role in an increasingly globalized world economy. Within these networks, the maritime ports play the role of hubs. Any disturbances in these hubs will negatively affect the worldwide economy. Therefore, economy players are transforming the ports through an evolutionary process to become smart maritime ports. These smart ports are built through an ensemble of smart domains that adopt sensing, data transmission, and data intelligence to support intelligent decision-making processes. Examples of such smart domains include smart grid/microgrids, smart container management, and smart/automatized terminal operations. In each of these domains, optimal decisions must be met to optimize the use of resources, increase the economy efficiency of the ports, and increase the safety and security for assets, goods, and people. In smart maritime port environment, vehicular applications are adopted everywhere, such as automated guided vehicles to transport containers, unmanned aerial vehicles for different port operations, etc. In this work, we discuss some concrete examples of these vehicular applications in the smart port environment and suggest the adequate and optimal vehicular communication technologies to be deployed to support a reliable data transmission for these applications

Conception d’un système hypermédia d’enseignement adaptatif centré sur les styles d’apprentissage : modèle et expérience

Cet article traite de style d’apprentissage en tant que critère d’adaptation d’un cours en ligne. Une première étape consiste à choisir le modèle des styles d’apprentissage. La sélection de ces styles est réalisée par un questionnaire dédié. D’autre part, les activités d’apprentissage sont conçues afin de refléter les dimensions liées aux styles d’apprentissage. Enfin, la présentation de ces activités est gérée par un module d’adaptation probabiliste. En nous appuyant sur les méthodes et les techniques proposées pour la modélisation et l’adaptation, nous avons conçu un système hypermédia d’enseignement adaptatif centré sur les styles d’apprentissage. L’approche a été validée expérimentalement et les résultats obtenus sont encourageants

Application d’une approche inspirée des colonies de fourmis pour la recommandation des chemins d’apprentissage dans un cours en ligne : modèle et expérience

Dans cet article, nous présentons la mise en œuvre, l’expérimentation et l’évaluation d’une approche pour la recommandation des chemins d’apprentissage dans un cours en ligne. Le processus de recommandation est inspiré de l’intelligence en essaim et plus particulièrement de l’optimisation par colonies de fourmis (OCF) (ant colony optimization [ACO]). Dans ce contexte, nous avons considéré une différenciation des chemins d’apprentissage en fonction de l’activité explorée pour l’apprentissage d’un cours. Dans l’objectif de recommander des chemins d’apprentissage considérés optimaux et d’évaluer ainsi leur impact sur l’apprentissage d’un cours en ligne, l’approche proposée est basée à la fois sur la recommandation de chemins pertinents par l’enseignant et sur les résultats stockés au fur et à mesure par les apprenants sur les chemins empruntés. Notre approche a été validée expérimentalement et les résultats obtenus ont montré l’émergence d’un chemin d’apprentissage favorisant la réussite d’un nombre d’apprenants relativement considérable

Big Data management in smart grid: concepts, requirements and implementation

Abstract A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. In essence, smart grids bring profound changes in the information systems that drive them: new information flows coming from the electricity grid, new players such as decentralized producers of renewable energies, new uses such as electric vehicles and connected houses and new communicating equipments such as smart meters, sensors and remote control points. All this will cause a deluge of data that the energy companies will have to face. Big Data technologies offers suitable solutions for utilities, but the decision about which Big Data technology to use is critical. In this paper, we provide an overview of data management for smart grids, summarise the added value of Big Data technologies for this kind of data, and discuss the technical requirements, the tools and the main steps to implement Big Data solutions in the smart grid context

Measurement of the inclusive isolated-photon cross section in pp collisions at √s = 13 TeV using 36 fb⁻¹ of ATLAS data

The differential cross section for isolated-photon production in pp collisions is measured at a centre-of-mass energy of 13 TeV with the ATLAS detector at the LHC using an integrated luminosity of 36.1 fb−1. The differential cross section is presented as a function of the photon transverse energy in different regions of photon pseudorapidity. The differential cross section as a function of the absolute value of the photon pseudorapidity is also presented in different regions of photon transverse energy. Next-to-leading-order QCD calculations from Jetphox and Sherpa as well as next-to-next-to-leading-order QCD calculations from Nnlojet are compared with the measurement, using several parameterisations of the proton parton distribution functions. The predictions provide a good description of the data within the experimental and theoretical uncertainties. [Figure not available: see fulltext.