Online and Collaborative Learning Design model based on IMS-LD to Stimulate Collaborative Learning in E-learning Environments

In the e-learning field, there is an urgent need for the sharing, reuse and design of online courses as learning objects. However, in the vast majority of cases, e-learning courses are built in a manner that not stimulating cooperation, interaction, and collaborative learning. The primary aim of this paper is to develop a strategy for constructing learning objects, strategy targeted at supporting instructors in designing educational contents in order to promote collaborative learning in e-learning environments. A key challenge in this work is the definition of a new method of learning design of e-learning contents to stimulate collaborative learning. In addition, we introduce a general model of online and collaborative learning design. Model is based on the methods of instructional design and Educational Modeling Languages, particularly the IMS-LD specification. Firstly, the paper presents the online and collaborative design process of a content based on a life cycle adapted. Then, the paper describes the steps of the modeling process of content. Finally, the paper exposes the adopted technical choices and a first prototype is set up to provide a subjective evaluation of the new framework

Predicting COVID-19 cases using Bidirectional LSTM on multivariate time series

Background: To assist policy makers in taking adequate decisions to stop the spread of COVID-19 pandemic, accurate forecasting of the disease propagation is of paramount importance. Materials and Methods: This paper presents a deep learning approach to forecast the cumulative number of COVID-19 cases using Bidirectional Long Short-Term Memory (Bi-LSTM) network applied to multivariate time series. Unlike other forecasting techniques, our proposed approach first groups the countries having similar demographic and socioeconomic aspects and health sector indicators using K-Means clustering algorithm. The cumulative cases data for each clustered countries enriched with data related to the lockdown measures are fed to the Bidirectional LSTM to train the forecasting model. Results: We validate the effectiveness of the proposed approach by studying the disease outbreak in Qatar. Quantitative evaluation, using multiple evaluation metrics, shows that the proposed technique outperforms state-of-art forecasting approaches. Conclusion: Using data of multiple countries in addition to lockdown measures improve accuracy of the forecast of daily cumulative COVID-19 cases

Approche Transparente de Composition de Services Web

Les services web sont des entités permettant l’accès à des applications disponibles sur Internet. Leur composition permet de créer des services plus complexes en faisant interagir plusieurs entités (entreprises, banques, etc.) offrant chacune un service bien précis. Dans tout processus de composition, la mise en œuvre de l’invocation des services web est très complexe. Elle nécessite une extraction manuelle des informations de chaque service web engagé à partir de son interface de description. Le manque de transparence dans la composition incite le développeur à écrire, d’une manière répétitive, un code d’invocation de bas niveau pour chaque service web. Ce qui rend la tâche de plus en plus complexe dans le cas d’un nombre important de services à invoquer. Ce papier présente une étude critique des solutions existantes via une synthèse analytique d’un cas simplifié d’une composition de services web. Nous proposons ensuite une approche permettant de rendre l’implémentation d’une composition transparente, plus simple et plus flexible

A game theory-based approach for robots deploying wireless sensor nodes

International audienceWireless Sensor Networks (WSNs) are deployed in many fields of application. Depending on the application requirements, sensor nodes can either be mobile and autonomous or static. In both cases, they are able to cooperate together in order to monitor a given area or some given Points of Interest (PoIs). Static sensor nodes need one or several agent(s) (humans or robots) to deploy them. In this paper, we focus on the deployment of static sensor nodes in an area containing obstacles, using two mobile robots. We want to minimize the time needed by the two robots to deploy all the sensor nodes and to return to their starting position. We require that each sensor node is placed at a PoI position, no PoI position is empty and no PoI position is occupied by more than one sensor node. The problem consists in determining the best strategy for each robot in order to meet these constraints. We adopt a game theory approach to solve this problem

Learning of chemistry of solution with help of computer simulation

Le présent article rend compte d'une expérimentation utilisant des simulations informatiques de titrages acide-base dans des situations didactiques d'apprentissage basées sur l'investigation et la participation de l'apprenant dans la construction de son savoir. Deux approches dans l'utilisation des simulations de titrages pH-métriques à l'aide du logiciel «SIMULTI2» sont mises en place et évaluées avec un groupe d'enseignants en formation continue et un groupe d'élèves-professeurs en formation initiale. On montre que les simulations informatiques créent un contexte pédagogique favorisant l'apprentissage par la découverte ou l'exploration basée sur les conflits cognitifs

Design and Development of an e-Learning Project Management System: Modelling and Prototyping

In this work, based on the results obtained concerning the analysis and the needs of our project which concerns the design and development of an e-Learning project management system, we present the modeling stage with UML. Based on one hand, on two diagrams: class diagram and use case diagram, for static modeling and on the other hand, based on three diagrams: activity diagram, sequence diagram, diagram transition state for dynamic modeling. Finally, we offer examples of models for our project

Crowd Counting: A Survey of Machine Learning Approaches

Crowd counting is applied in many areas including efficient resources allocation and effective management of emergency situations. In this paper, we survey and compare various crowd counting methods. Additionally, we identify the limitations of existing approaches and sketch an agenda for future work to address the identified open research challenges. Furthermore, we present an enhanced deep learning-based solution for crowd counting at bus stops. 2020 IEEE.ACKNOWLEDGMENT This research was made possible by UREP24-125-1-030 grant from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

An Evaluation Model of Digital Educational Resources

Abstractâ??Today, the use of digital educational resources in teaching and learning is considerably expanding. Such expansion calls educators and computer scientists to reflect more on the design of such products. However, this reflection exposes a number of criteria and recommendations that can guide and direct any teaching tool design be it campus-based or online (e-learning). Our work is at the heart of this issue. We suggest, through this article, examining academic, pedagogical, didactic and technical criteria to conduct this study which aims to evaluate the quality of digital educational resources. Our approach consists in addressing the specific and relevant factors of each evaluation criterion. We will then explain the detailed structure of the evaluation instrument used : â??evaluation gridâ?. Finally, we show the evaluation outcomes based on the conceived grid and then we establish an analytical evaluation of the state of the art of digital educational resources