Du e-Learning au e-Learning 2.0

Derniêrement  avec la génération native appelé aussi génération Y on trouve de plus en plus de difficulté à  imposer aux apprenants  d'utiliser les plates-formes à  distance.  C'est une génération qui arrive aujourd'hui avec de nouvelles habitudes de vie différente de la notre. Ils ont appris à  communiquer, collaborer et partager à  distance en constituant des communautés autours des centres d'intérêts. En parallêle, on parle de plus en plus du e-Learning 2.0 qui se base sur le Web 2.0. Ce dernier apporte à  la génération Y des environnements personnalisés pour vivre en société à  distance. L'objectif de ce papier est de répondre aux questions suivantes : quel est la motivation pour basculer vers le e-Learning 2.0? Quel est l'impact de  cette nouvelle façon de se former sur la qualité d'apprentissage? Quel est le rôle de l'enseignant dans ce nouvel environnement d'apprentissage ? Quels sont les défis pédagogiques et techniques à  relever

Towards optimize-ESA for text semantic similarity: A case study of biomedical text

Explicit Semantic Analysis (ESA) is an approach to measure the semantic relatedness between terms or documents based on similarities to documents of a references corpus usually Wikipedia. ESA usage has received tremendous attention in the field of natural language processing NLP and information retrieval. However, ESA utilizes a huge Wikipedia index matrix in its interpretation by multiplying a large matrix by a term vector to produce a high-dimensional vector. Consequently, the ESA process is too expensive in interpretation and similarity steps. Therefore, the efficiency of ESA will slow down because we lose a lot of time in unnecessary operations. This paper propose enhancements to ESA called optimize-ESA that reduce the dimension at the interpretation stage by computing the semantic similarity in a specific domain. The experimental results show clearly that our method correlates much better with human judgement than the full version ESA approach

Normalization and Personalization of Learning Situation: NPLS

The personalization of learning is a major pedagogical challenge solicited by pedagogues and didacts. There are several projects about the production of personalizable learning situations such as Reload-LDE and Alfanet. These projects are interested in producing new standardized and personalizable learning situations. However, on the Web, an important number of learning situations exist. These situations are rich in information but don't consider all the characteristics of participants taking part in the learning, nor their technical environments. In this paper we suggest a help system that can transform an existing learning situation to another structure standardized and personalizable depending on the context of learning personalization that we have defined

A dropout predictor system in MOOCs based on neural networks

Massive open online courses, MOOCs, are a recent phenomenon that has achieved a tremendous media attention in the online education world. Certainly, the MOOCs have brought interest among the learners (given the number of enrolled learners in these courses). Nevertheless, the rate of dropout in MOOCs is very important. Indeed, a limited number of the enrolled learners complete their courses. The high dropout rate in MOOCs is perceived by the educator’s community as one of the most important problems. It’s related to diverse aspects, such as the motivation of the learners, their expectations and the lack of social interactions. However, to solve this problem, it is necessary to predict the likelihood of dropout in order to propose an appropriate intervention for learners at-risk of dropping out their courses. In this paper, we present a dropout predictor model based on a neural network algorithm and sentiment analysis feature that used the clickstream log and forum post data. Our model achieved an average AUC (Area under the curve) as high as 90% and the model with the feature of the learner’s sentiments analysis attained average increase in AUC of 0.5%

Decision Making in the Connected Learning Environment (CLE)

In the last years, we have witnessed to an increasingly heightened awareness of the potential benefits of a challenging and promising educational research area : Adaptive Learning [1]. It has become one of the central technologies in education [2] and was recently named, by Gartner, as the number one strategic technology to impact education in 2015 [3]. In fact, adaptive learning systems become more accessible to educational institutions, corporations, and individuals, however, the challenges encountered are more structural and operational rather than technological [4]. While a lot of research has focused on development and evaluation of technological aspects [5], serious questions remain about the motivation of learners [6],[7] and also the design of the content (or domain) model [8],[9] including the learner's autonomy issues [9],[10],[11] and the lack of the learner's control [9],[12],[13]. In order to overcome those challenges, we propose CLE “Connected Learning Environment” which is an ubiquitous learning environment [14] that provide to the learners of this generation a learning environment adapted to their expectations and their lifestyle habits and stimulate also their motivation. As a pedagogical approach, CLE adopts the connectivism [15] and take advantage from its benefits (adaptation to the current technological advances [16], management of learning in communities [17], openness with respect to external resources[18], etc.) and adapts this approach in a formal context even though the connectivism was conceived as an informal pedagogical approach [19][20]. CLE introduces a new pedagogical process including four phases detailed later (Knowledge construction, Decision making, Validation, Evaluation) and the knowledge construction phase is characterized by the collaboration and communication between heterogeneous communities composed of humans and smart objects [14]. However, the ability to distinguish relevant information among the knowledge constructed by the actors is a vital point. As part of this article, we focus on the decision making process. To do this, a comparison is made between C4.5 [21] decision tree and MLP [22] neural network on the same data set using the same performance measures in order to take a decision on the relevance of knowledge constructed by the CLE actors

A Bayesian CNN-LSTM Model for Sentiment Analysis in Massive Open Online Courses MOOCs

Massive Open Online Courses (MOOCs) are increasingly used by learn-ers to acquire knowledge and develop new skills. MOOCs provide a trove of data that can be leveraged to better assist learners, including behavioral data from built-in collaborative tools such as discussion boards and course wikis. Data tracing social interactions among learners are especially inter-esting as their analyses help improve MOOCs’ effectiveness. We particular-ly perform sentiment analysis on such data to predict learners at risk of dropping out, measure the success of the MOOC, and personalize the MOOC according to a learner’s behavior and detected emotions. In this pa-per, we propose a novel approach to sentiment analysis that combines the advantages of the deep learning architectures CNN and LSTM. To avoid highly uncertain predictions, we utilize a Bayesian neural network (BNN) model to quantify uncertainty within the sentiment analysis task. Our em-pirical results indicate that: 1) The Bayesian CNN-LSTM model provides interesting performance compared to other models (CNN-LSTM, CNN, LSTM) in terms of accuracy, precision, recall, and F1-Score; and 2) there is a high correlation between the sentiment in forum posts and the dropout rate in MOOCs

Concepts and Tools for Knowledge Management (KM)

International audienceThe governance of knowledge seems to be the Scientific Policy most able to creating value with regard of human and its evolution in cultures and civilizations. The duty of good governance is a consideration of the transfer of knowledge related to scientific and technological progress. Intrinsically, this process requires a system of organization and knowledge management by implementing knowledge production and its influence in society. The objective of the ISKO-Maghreb Chapter is to contribute in understanding the factors that organize knowledge and phenomena that affect the information society. Actions to be undertaken by the scientific ISKO society must take into account socio-cultural, cognitive and economic in the strategic management of knowledge. Towards the knowledge society, it should be seen in its dynamic, its content and its interaction with science and technology associated to universities, companies and politics. In this context, a first orientation is pedagogical attempt to answer the question "what is known about the knowledge and its organization?". Then the issue is moving towards the societal issues of knowledge, theory and practice, to provide clarification to a convergence of KM (Knowledge Management) approaches. Education, science, culture, communication and technology remain the major themes covered by ISKO-Maghreb, for the development of the knowledge organization, expertise management and collective intelligence. In a friendly atmosphere, hospitality and open to exchange, the international symposium ISKOMaghreb' 2011 was thought to enhance the Scientific Society "ISKO" with the universities, the practitioners in the Maghreb countries and the world.La gouvernance de la connaissance paraît être la politique scientifique la plus apte à la création de la valeur au regard de l'Homme et de son évolution dans les cultures et civilisations. Le devoir de cette gouvernance est une prise en considération de la transmission des savoirs liée aux progrès scientifiques et technologiques. Intrinsèquement, ce processus de transmission nécessite un système d'organisation et de management de la connaissance mettant en oeuvre la production du savoir et son rayonnement dans la société. L'objectif du chapitre ISKO-Maghreb est de contribuer dans la compréhension des facteurs qui organisent la connaissance et les phénomènes qui affectent la société de l'information. Les actions à entreprendre par la société savante ISKO devront tenir compte des aspects socio-culturels, cognitifs et économiques dans la gestion stratégique de la connaissance. Vers la société du savoir, la connaissance doit être perçue dans sa dynamique, sa teneur et ses interactions scientifiques et technologiques avec les universités, les entreprises et les politiques. Dans ce contexte, une première orientation est d'ordre pédagogique pour tenter de répondre à la question "que sait-on de la connaissance et de son organisation ?". Ensuite, la question évolue vers les enjeux sociétaux du savoir, en théorie et en pratique, pour apporter des clarifications vers une convergence des démarches KM (Knowledge Management). Éducation, science, culture, communication et technologie restent les thématiques majeures couvertes par ISKO-Maghreb, pour la mise en place de l'organisation de la connaissance, de la gestion des compétences et de l'intelligence collective. Dans un cadre convivial, chaleureux et ouvert aux échanges, le colloque international ISKO-Maghreb'2011 a été pensé en vue de renforcer la Société savante " ISKO " avec les universitaires, les praticiens dans les pays du Maghreb et le reste du monde

Impact of Recursive Feature Elimination with Cross-validation in Modeling the Spatial Distribution of Three Mosquito Species in Morocco

International audienceMany studies in ecology are interested in characterizing the ecological factors; determining the distribution of animal species. The classical approach consists in identifying the combination of ecological factors that allow reproducing observations of the presence and absence of the species of interest. The major difficulty lies in the imbalance between a considerable quantity of ecological factors to be tested and a relatively limited number of presence/absence observations. Selection of the most influential ecological features is a classical data pre-processing strategy that aims to overcome this imbalance and improve model performance. In this paper, we applied recursive feature elimination with cross-validation (RFECV) approach on presence/absence mosquito data in Morocco; to select optimal subsets of ecological features, in order to improve the performance of the predictive models. This method demonstrated the best ability to improve the performance of the predictive models, and can be recommended as a modeling improvement technique for large datasets