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In this paper we present an audacious solution based on Bayesian networks and educational approach for the construction of evolutionary personalized learning paths. We mean by evolutionary learning paths, paths that are composed gradually as learners advance in their learning, i.e in real time. To do this, the system selects the hypermedia units of learning to apprehend based on the results of formative assessments, psychological and cognitive characteristics of learner. The architecture that we propose is based, firstly, on the semantic web, First, in order to model the domain model and to index learning resources so as to maximize their reuse, and then to represent the personal and cognitive traits of learners in a learner model while integrating their learning styles according to the Felder and Silverman model; and secondly, a probabilistic approach based on Bayesian networks that calculates the probability of success of each candidate hypermedia unit, for selecting those who are most appropriate for the construction of evolutionary personalized learning paths. The proposed Bayesian model is validated with real data collected from an experimental study with a specimen of students.

This paper presents a novel solution for modeling professional bimodal hybrid training based on the ontological engineering and the pedagogical competencies approach. It is an educational solution for personalizing learning under the frame of vocational trainings and providing learners two learning scenarios: The first is called free, where the learner can choose a competency level that he aims to develop. And the second is called guided, where the learner must obey the instructions of the system to develop his competencies as part of a Certificate or diploma training. This solution fits in the context of the development of Adaptive Learning Systems. It is based on the principle of dynamic composition of learning units and the choice of integration situations to deliver learning sessions to meet the needs of learners and reflect their preferences for learning styles. In addition, and to create a coherent model, we used the example of the Regional Centers of trades, education and training of Morocco (CRMEF) to train future teachers.

According to the user profile, a recommender system intends to offer items to the user that may interest him. The recommendations have been applied successfully in various fields. Recommended items include movies, books, travel and tourism services, friends, research articles, research queries, and much more. Hence the presence of recommender systems in many areas, in particular, movies recommendations. Most current Machine Learning recommender systems serve as black boxes that do not provide the user with any insight into or justification for the system's logic. What puts users at risk of losing their confidence. Recommender systems suffer from an overload of information, which poses numerous problems, including high cost, slow data processing, and low time complexity. That is why researchers in have been using graph embeddings algorithms in the recommendation field to reduce the quantity of data, as these algorithms have been successful in the last few years. This work aims to improve the quality of recommendation and the simplicity of recommendation explanation based on the word2vec graph embeddings model.

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<span lang="EN-US">Sentiment analysis is a well-known and rapidly expanding study topic in natural language processing (NLP) and text classification. This approach has evolved into a critical component of many applications, including politics, business, advertising, and marketing. Most current research focuses on obtaining sentiment features through lexical and syntactic analysis. Word embeddings explicitly express these characteristics. This article proposes a novel method, improved words vector for sentiments analysis (IWVS), using XGboost to improve the F1-score of sentiment classification. The proposed method constructed sentiment vectors by averaging the word embeddings (Sentiment2Vec). We also investigated the Polarized lexicon for classifying positive and negative sentiments. The sentiment vectors formed a feature space to which the examined sentiment text was mapped to. Those features were input into the chosen classifier (XGboost). We compared the F1-score of sentiment classification using our method via different machine learning models and sentiment datasets. We compare the quality of our proposition to that of baseline models, term frequency-inverse document frequency (TF-IDF) and Doc2vec, and the results show that IWVS performs better on the F1-measure for sentiment classification. At the same time, XGBoost with IWVS features was the best model in our evaluation.</span>

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