Hybrid approach of the fuzzy C-Means and the K-Nearest neighbors methods during the retrieve phase of dynamic case based reasoning for personalized Follow-up of learners in real time

The goal of adaptive learning systems is to help the learner achieve their goals and guide their learning. These systems make it possible to adapt the presentation of learning resources according to learners' needs, characteristics and learning styles, by offering them personalized courses. We propose an approach to an adaptive learning system that takes into account the initial learning profile based on Felder Silverman's learning style model in order to propose an initial learning path and the dynamic change of his behavior during the learning process using the Incremental Dynamic Case Based Reasoning approach to monitor and control its behavior in real time, based on the successful experiences of other learners, to personalize the learning. These learner experiences are grouped into homogeneous classes at the behavioral level, using the Fuzzy C-Means unsupervised machine learning method to facilitate the search for learners with similar behaviors using the supervised machine learning method K- Nearest Neighbors

Identifying learning style through eye tracking technology in adaptive learning systems

Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning

Arabic Text Summarization Challenges using Deep Learning Techniques: A Review

Text summarization is a challenging field in Natural Language Processing due to language modelisation and used techniques to give concise summaries.  Dealing with Arabic language does increase the challenge while taking into consideration the many features of the Arabic language, the lack of tools and resources for Arabic, and the Algorithms adaptation and modelisation. In this paper, we present several researches dealing with Arabic Text summarization applying different Algorithms on several Datasets. We then compare all these researches and we give a conclusion to guide researchers on their further work

Factors influencing cloud computing adoption in small medium enterprises

Cloud computing offers information technology (IT) infrastructure, platform, and various applications via the Internet with minimum start-up cost, network access to a shared pool of configurable computing resources, and pay-per-use services. Although the potential for cloud computing is evident and much of the extant research has been carried out on cloud computing adoption, empirical studies on the factors that influence cloud computing adoption in the Malaysian Small and Medium Enterprises (SMEs) are, however, lacking. The objective of this study was to examine the factors that influence cloud computing adoption by the SMEs. We conducted a quantitative survey-based study to examine the relationship between perceived benefits, top management support, IT resources, external pressure, and cloud computing adoption. A free-form comment provided at the end of each section of the survey questionnaire was treated as qualitative data. We find that IT resources and external pressure significantly influence cloud computing adoption. Nonetheless, there is not enough evidence to support perceived benefits and top management support as significant factors of cloud computing adoption

A systematic reading in statistical translation: From the statistical machine translation to the neural translation models

Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades.Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automatic translation as a key application in the natural language processing domain has developed many approaches, namely statistical machine translation and recently neural machine translation that improved largely the translation quality especially for Latin languages.They have even made it possible for the translation of some language pairs to approach human translation quality.In this paper, we present a survey of the state of the art of statistical translation, where we describe the different existing methodologies, and we overview the recent research studies while pointing out the main strengths and limitations of the different approaches

Contribution à l’intégration de l’apprentissage mixte dans le système éducatif marocain

Dans cet article, nous nous intéressons à l’intégration de la formation en ligne dans le système éducatif marocain. Nous proposons une solution basée sur l’apprentissage mixte. L’apprentissage mixte combine les meilleurs éléments de la formation classique avec ceux de la formation à distance. Nous nous focalisons sur la problématique de la non-disponibilité de la connexion Internet chez les apprenants qui veulent suivre des formations en ligne. Nous proposons une solution permettant à ces apprenants d’effectuer leurs apprentissages dans une plateforme d’apprentissage en ligne installée localement (hors ligne) et de communiquer avec la plateforme en ligne quand la connexion sera disponible.In this paper we focus on the integration of online learning in Moroccan education system. We propose a solution based on blended learning, which combines the best elements of online and face to face learning. We focus on the problem of Internet unavailability among learners who want to take online courses. We propose a solution allowing learners to complete their learning in a platform installed locally (offline) and to communicate with the platform online when the connection becomes available

Contribution à l’intégration de l’apprentissage mixte dans le système éducatif marocain

Dans cet article, nous nous intéressons à l’intégration de la formation en ligne dans le système éducatif marocain. Nous proposons une solution basée sur l’apprentissage mixte. L’apprentissage mixte combine les meilleurs éléments de la formation classique avec ceux de la formation à distance. Nous nous focalisons sur la problématique de la non-disponibilité de la connexion Internet chez les apprenants qui veulent suivre des formations en ligne. Nous proposons une solution permettant à ces apprenants d’effectuer leurs apprentissages dans une plateforme d’apprentissage en ligne installée localement (hors ligne) et de communiquer avec la plateforme en ligne quand la connexion sera disponible

Revisiting the Didactic Triangle in the Case of an Adaptive Learning System

In this paper we revisit the classical approach of the didactic triangle designed for the classical learning situation (face to face) and adapt it to the situation of an adaptive learning system, we discuss also the different components involved in this didactic triangle and how they interact and influence the learning process in an adaptive learning system

A SYSTEMATIC READING IN STATISTICAL TRANSLATION: FROM THE STATISTICAL MACHINE TRANSLATION TO THE NEURAL TRANSLATION MODELS.

Achieving high accuracy in automatic translation tasks has been one of the challenging goals for researchers in the area of machine translation since decades. Thus, the eagerness of exploring new possible ways to improve machine translation was always the matter for researchers in the field. Automatic translation as a key application in the natural language processing domain has developed many approaches, namely statistical machine translation and recently neural machine translation that improved largely the translation quality especially for Latin languages. They have even made it possible for the translation of some language pairs to approach human translation quality. In this paper, we present a survey of the state of the art of statistical translation, where we describe the different existing methodologies, and we overview the recent research studies while pointing out the main strengths and limitations of the different approaches.