The Impact of Learning Management System Usage on Cognitive and Affective Performance

1 INTRODUCTION Since learning management systems (LMSs) are offering a great variety of channels and workspaces to facilitate information sharing and communication among learners during learning process, many educational organizations have adopted a specific LMS into their educational context. A LMS is a software that handles learning tasks such as creating course catalogs, registering students, providing access to course components, tracking students within courses, recording data about students, and providing reports about usage and outcomes to teachers [1]. LMSs include several applications such as OLAT, WebCT, Moodle, ATutor, Ilias, and Claroline. However, LMSs can be utilized to integrate a wide range of multimedia materials, blogs, forums, quizzes, and wikis. Therefore, the researchers suggest that studying the influence of technology usage on end-users, especially students, is fundamental in learning and teaching environment. Despite educational organizations routinely make decisions regarding the best pedagogical approaches for supporting students’ performance, there is very little research on the impact of LMSs on learning outcomes [2]. Indeed, a considerable number of studies were conducted to examine the adoption of various LMSs, whereas little researches focused on understanding how educational institutes can enhance learning and teaching process through a particular LMS [3]. Consistent with this, the researchers found virtually no research on investigating the relationship between LMSs usage and attitude toward learning. [...

Distributed and Dynamic Map-less Self-reconfiguration for Microrobot Networks

International audienceMEMS micro robots are low-power and low memory capacity devices that can sense and act. One of the most challenges in MEMS micro robot applications is the self-reconfiguration, especially when the efficiency and the scalability of the algorithm are required. In the literature, if we want a self-reconfiguration of micro robots to a target shape consisting of P positions, each micro robot should have a memory capacity of P positions. Therefore, if P equals to millions, each node should have a memory capacity of millions of positions. Therefore, this is not scalable. In this paper, nodes do not record any position, we present a self-reconfiguration method where a set of micro robots are unaware of their current position and do not have the map of the target shape. In other words, nodes do not store the positions that build the target shape. Consequently, memory usage for each node is reduced to O(1). An algorithm of self-reconfiguration to optimize the communication is deeply studied showing how to manage the dynamicity (wake up and sleep of micro robots) of the network to save energy. Our algorithm is implemented in Meld, a declarative language, and executed in a real environment simulator called DPRSim

Fitness Landscape Analysis for Scalable Multicast RRM Problem in Cellular Network

International audienceThis paper aims to solve the Radio Resource Management (RRM) problem for Multimedia Broadcast Multicast Service (MBMS) system in cellular network. We develop a flexible model to perform dynamic radio resource allocation for MBMS service by using metaheuristic approach. We conduct fitness landscape analysis to study the characteristics of the proposed model, which helps us to select appropriate search strategy. Simulation results show that the proposed algorithm provides better performance than existing algorithms. Keywords: fitness landscape, metaheuristic approach, multimedia multicast, radio resource management

Designing e-research: A framework for researcher’s social online knowledge

Design strategies to support and enhance scientific collaboration are still ambiguous. The ability of universities and research institutes to support a collaborative scientific research environment among researchers through appropriate methods needs to be further investigated. The lack of understanding about the human factors behind collaboration, the nature of scientific tasks, and the institute’s cultural environment are motivations for this study. As a part of our work on a European integrated project, Edu-Tech, this study investigated which factors of collaborative research are important to give us a clear picture for enhancing the social perspective of the project’s webpage. This research purposes a model, Time Environment, Individual and Group (TEIG), in order to provide descriptive variable necessary to understand the transformation of online social knowledge. Accordingly, we provided a new prototype for designing our online community, Edu-Tech, which is now ready to facilitate collaboration among researchers

Coordination and Computation in distributed intelligent MEMS

International audienceOver the last decades, research on microelectromechanical systems (MEMS) has focused on the engineering process which has led to major advances. Future challenges will consist in adding embedded intelligence to MEMS systems to obtain distributed intelligent MEMS. One intrinsic characteristic of MEMS is their ability to be mass-produced. This, however, poses scalability problems because a significant number of MEMS can be placed in a small volume. Managing this scalability requires paradigm-shifts both in hardware and software parts. Furthermore, the need for actuated synchronization, programming, communication and mobility management raises new challenges in both control and programming. Finally, MEMS are prone to faulty behaviors as they are mechanical systems and they are issued from a batch fabrication process. A new programming paradigm which can meet these challenges is therefore needed. In this article, we present CO2Dim, which stands for Coordination and Computation in Distributed Intelligent MEMS. CO2DIM is a new programming environment which includes a language based on a joint development of programming and control capabilities, a simulator and real hardware

Machine learning analysis of instabilities in noise-like pulse lasers

Neural networks have been recently shown to be highly effective in predicting time-domain properties of optical fiber instabilities based only on analyzing spectral intensity profiles. Specifically, from only spectral intensity data, a suitably trained neural network can predict temporal soliton characteristics in supercontinuum generation, as well as the presence of temporal peaks in modulation instability satisfying rogue wave criteria. Here, we extend these previous studies of machine learning prediction for single-pass fiber propagation instabilities to the more complex case of noise-like pulse dynamics in a dissipative soliton laser. Using numerical simulations of highly chaotic behaviour in a noise-like pulse laser operating around 1550 nm, we generate large ensembles of spectral and temporal data for different regimes of operation, from relatively narrowband laser spectra of 70 nm bandwidth at the -20 dB level, to broadband supercontinuum spectra spanning 200 nm at the -20 dB level and with dispersive wave and long wavelength Raman extension spanning from 1150–1700 nm. Using supervised learning techniques, a trained neural network is shown to be able to accurately correlate spectral intensity profiles with time-domain intensity peaks and to reproduce the associated temporal intensity probability distributions.publishedVersionPeer reviewe

Estrategias que contribuyan al Desarrollo del Turismo Interno en Granada, Nicaragua durante el II Semestre 2019

El presente trabajo se realizó en la ciudad de Granada-Nicaragua orientado a determinar estrategias que contribuyan al desarrollo del turismo interno en la ciudad de Granada el cual se efectuó durante el segundo semestre 2019. La metodología utilizada fue de enfoque mixto cuali cuantitativo, según el alcance es descriptivo porque refiere cada una de las características de los establecimientos de servicios turísticos por su clasificación, categoría, dirección así mismo se describen todos los recursos y atractivos turísticos con que cuenta la ciudad, es de corte transversal porque se realizó durante el segundo semestre del año 2019.Con dicho trabajo se logró identificar la oferta turística de la ciudad de granada actual del año 2019, se determinó el comportamiento de la actividad turística mediante un cuadro comparativo de estadistas en semana santa del año 2017, 2018, 2019. Por último, se realizó la propuesta de estrategias específicas con el fin de aportar al desarrollo del turismo interno en dicha ciudad

Feed-forward neural network as nonlinear dynamics integrator for supercontinuum generation

The nonlinear propagation of ultrashort pulses in optical fibers depends sensitively on the input pulse and fiber parameters. As a result, the optimization of propagation for specific applications generally requires time-consuming simulations based on the sequential integration of the generalized nonlinear Schrödinger equation (GNLSE). Here, we train a feed-forward neural network to learn the differential propagation dynamics of the GNLSE, allowing emulation of direct numerical integration of fiber propagation, and particularly the highly complex case of supercontinuum generation. Comparison with a recurrent neural network shows that the feed-forward approach yields faster training and computation, and reduced memory requirements. The approach is generic and can be extended to other physical systems.acceptedVersionPeer reviewe