Academic literature on the topic 'Méthodes d’apprentissage en ligne'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Méthodes d’apprentissage en ligne.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Méthodes d’apprentissage en ligne"
Hamida, Soufiane, Bouchaib Cherradi, Abdelhadi Raihani, and Hassan Ouajji. "Evaluation des apprentissages au sein d’un environnement de type MOOC adaptatif." ITM Web of Conferences 39 (2021): 03005. http://dx.doi.org/10.1051/itmconf/20213903005.
Full textBond, Suzie, Émilie Binet, and Béatrice Pudelko. "L’utilisation des technologies pour optimiser la formation des intervenants en santé mentale aux traitements fondés sur les données probantes : où en sommes-nous ?" Santé mentale au Québec 46, no. 1 (September 21, 2021): 71–95. http://dx.doi.org/10.7202/1081510ar.
Full textBarbosa Aleluia, Iêda Maria, and Karla Carolina Nascimento Cardoso. "Évaluation de l’impact sur les apprentissages des étudiants de leur participation comme acteurs pendant un examen clinique objectif structuré de sémiologie médicale." Pédagogie Médicale 20, no. 3 (2019): 141–46. http://dx.doi.org/10.1051/pmed/2020014.
Full textLemay, Richard, and Martine Mottet. "Les méthodes pédagogiques utilisées pour construire un environnement d’apprentissage socioconstructiviste dans uncours en ligne en mode hybride." Revue internationale des technologies en pédagogie universitaire 6, no. 2-3 (2009): 47. http://dx.doi.org/10.7202/1000011ar.
Full textRadusin Bardić, Nataša. "SUR LA MISE EN CONTEXTE DES ACTIVITÉS DE PHONÉTIQUE DANS LES MÉTHODES DE FLE S’INSCRIVANT DANS UNE PERSPECTIVE ACTIONNELLE." Годишњак Филозофског факултета у Новом Саду 46, no. 3 (January 11, 2022): 155–74. http://dx.doi.org/10.19090/gff.2021.3.155-174.
Full textSegura Tornero, Alfredo. "Apprendre sur la plateforme PARKUR la langue de la vie quotidienne, sur objectifs spécifiques et de spécialité à l’aune du CECRL et son Volume complémentaire." Didactique du FLES, no. 1:1 (June 1, 2022): 121–37. http://dx.doi.org/10.57086/dfles.364.
Full textBaker, Nick, Freer John, Fujita Nobuko, Higgison Alicia, Lubrick Mark, Sabourin Brandon, Sylvester Jane, and Van Wyk Paula. "Online Instructor Development: A COOL story." Collected Essays on Learning and Teaching 13 (October 28, 2020): 120–30. http://dx.doi.org/10.22329/celt.v13i0.6011.
Full textIshaka, Noe, and Kalum Muray. "Approche pédagogique pour la littératie et la numératie chez les jeunes autochtones du Canada : Quelle perspective en période de pandémie?" Interdisciplinary Research Journal and Archives 1 (December 16, 2020): 22–27. http://dx.doi.org/10.36966/irjar2020.14.
Full textDeschênes, Marie-France, and Johanne Goudreau. "L’apprentissage du raisonnement clinique infirmier dans le cadre d’un dispositif éducatif numérique basé sur la concordance de scripts." Pédagogie Médicale 21, no. 3 (2020): 143–57. http://dx.doi.org/10.1051/pmed/2020041.
Full textKhelfi, Amira. "L'approche de formation hybride comme méthode d’enseignement facilitant la compréhension en lecture d’un cours de spécialité dispensé en FLE au supérieur Algérien." Médiations et médiatisations, no. 2 (November 15, 2019): 54–76. http://dx.doi.org/10.52358/mm.vi2.90.
Full textDissertations / Theses on the topic "Méthodes d’apprentissage en ligne"
Contal, Emile. "Méthodes d’apprentissage statistique pour l’optimisation globale." Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLN038/document.
Full textThis dissertation is dedicated to a rigorous analysis of sequential global optimization algorithms. We consider the stochastic bandit model where an agent aim at finding the input of a given system optimizing the output. The function which links the input to the output is not explicit, the agent requests sequentially an oracle to evaluate the output for any input. This function is not supposed to be convex and may display many local optima. In this work we tackle the challenging case where the evaluations are expensive, which requires to design a careful selection of the input to evaluate. We study two different goals, either to maximize the sum of the rewards received at each iteration, or to maximize the best reward found so far. The present thesis comprises the field of global optimization where the function is a realization from a known stochastic process, and the novel field of optimization by ranking where we only perform function value comparisons. We propose novel algorithms and provide theoretical concepts leading to performance guarantees. We first introduce an optimization strategy for observations received by batch instead of individually. A generic study of local supremum of stochastic processes allows to analyze Bayesian optimization on nonparametric search spaces. In addition, we show that our approach extends to natural non-Gaussian processes. We build connections between active learning and ranking and deduce an optimization algorithm of potentially discontinuous functions
Collet, Timothé. "Méthodes optimistes d’apprentissage actif pour la classification." Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0084/document.
Full textA Classification problem makes use of a training set consisting of data labeled by an oracle. The larger the training set, the best the performance. However, requesting the oracle may be costly. The goal of Active Learning is thus to minimize the number of requests to the oracle while achieving the best performance. To do so, the data that are presented to the oracle must be carefully selected among a large number of unlabeled instances acquired at no cost. However, the true profitability of labeling a particular instance may not be known perfectly. It can therefore be estimated along with a measure of uncertainty. To Increase the precision on the estimate, we need to label more data. Thus, there is a dilemma between labeling data in order to increase the performance of the classifier or to better know how to select data. This dilemma is well studied in the context of finite budget optimization under the name of exploration versus exploitation dilemma. The most famous solutions make use of the principle of Optimism in the Face of Uncertainty. In this thesis, we show that it is possible to adapt this principle to the active learning problem for classification. Several algorithms have been developed for classifiers of increasing complexity, each one of them using the principle of Optimism in the Face of Uncertainty, and their performances have been empirically evaluated
Collet, Timothé. "Méthodes optimistes d’apprentissage actif pour la classification." Electronic Thesis or Diss., Université de Lorraine, 2016. http://www.theses.fr/2016LORR0084.
Full textA Classification problem makes use of a training set consisting of data labeled by an oracle. The larger the training set, the best the performance. However, requesting the oracle may be costly. The goal of Active Learning is thus to minimize the number of requests to the oracle while achieving the best performance. To do so, the data that are presented to the oracle must be carefully selected among a large number of unlabeled instances acquired at no cost. However, the true profitability of labeling a particular instance may not be known perfectly. It can therefore be estimated along with a measure of uncertainty. To Increase the precision on the estimate, we need to label more data. Thus, there is a dilemma between labeling data in order to increase the performance of the classifier or to better know how to select data. This dilemma is well studied in the context of finite budget optimization under the name of exploration versus exploitation dilemma. The most famous solutions make use of the principle of Optimism in the Face of Uncertainty. In this thesis, we show that it is possible to adapt this principle to the active learning problem for classification. Several algorithms have been developed for classifiers of increasing complexity, each one of them using the principle of Optimism in the Face of Uncertainty, and their performances have been empirically evaluated
Colin, Igor. "Adaptation des méthodes d’apprentissage aux U-statistiques." Thesis, Paris, ENST, 2016. http://www.theses.fr/2016ENST0070/document.
Full textWith the increasing availability of large amounts of data, computational complexity has become a keystone of many machine learning algorithms. Stochastic optimization algorithms and distributed/decentralized methods have been widely studied over the last decade and provide increased scalability for optimizing an empirical risk that is separable in the data sample. Yet, in a wide range of statistical learning problems, the risk is accurately estimated by U-statistics, i.e., functionals of the training data with low variance that take the form of averages over d-tuples. We first tackle the problem of sampling for the empirical risk minimization problem. We show that empirical risks can be replaced by drastically computationally simpler Monte-Carlo estimates based on O(n) terms only, usually referred to as incomplete U-statistics, without damaging the learning rate. We establish uniform deviation results and numerical examples show that such approach surpasses more naive subsampling techniques. We then focus on the decentralized estimation topic, where the data sample is distributed over a connected network. We introduce new synchronous and asynchronous randomized gossip algorithms which simultaneously propagate data across the network and maintain local estimates of the U-statistic of interest. We establish convergence rate bounds with explicit data and network dependent terms. Finally, we deal with the decentralized optimization of functions that depend on pairs of observations. Similarly to the estimation case, we introduce a method based on concurrent local updates and data propagation. Our theoretical analysis reveals that the proposed algorithms preserve the convergence rate of centralized dual averaging up to an additive bias term. Our simulations illustrate the practical interest of our approach
Colin, Igor. "Adaptation des méthodes d’apprentissage aux U-statistiques." Electronic Thesis or Diss., Paris, ENST, 2016. http://www.theses.fr/2016ENST0070.
Full textWith the increasing availability of large amounts of data, computational complexity has become a keystone of many machine learning algorithms. Stochastic optimization algorithms and distributed/decentralized methods have been widely studied over the last decade and provide increased scalability for optimizing an empirical risk that is separable in the data sample. Yet, in a wide range of statistical learning problems, the risk is accurately estimated by U-statistics, i.e., functionals of the training data with low variance that take the form of averages over d-tuples. We first tackle the problem of sampling for the empirical risk minimization problem. We show that empirical risks can be replaced by drastically computationally simpler Monte-Carlo estimates based on O(n) terms only, usually referred to as incomplete U-statistics, without damaging the learning rate. We establish uniform deviation results and numerical examples show that such approach surpasses more naive subsampling techniques. We then focus on the decentralized estimation topic, where the data sample is distributed over a connected network. We introduce new synchronous and asynchronous randomized gossip algorithms which simultaneously propagate data across the network and maintain local estimates of the U-statistic of interest. We establish convergence rate bounds with explicit data and network dependent terms. Finally, we deal with the decentralized optimization of functions that depend on pairs of observations. Similarly to the estimation case, we introduce a method based on concurrent local updates and data propagation. Our theoretical analysis reveals that the proposed algorithms preserve the convergence rate of centralized dual averaging up to an additive bias term. Our simulations illustrate the practical interest of our approach
Tempier, Charlotte. "L'Autorégulation dans un dispositif en ligne d’apprentissage : Signes d’un apprenant opportuniste." Paris 10, 2011. http://www.theses.fr/2011PA100004.
Full textBouaziz, Ameni. "Méthodes d’apprentissage interactif pour la classification des messages courts." Thesis, Université Côte d'Azur (ComUE), 2017. http://www.theses.fr/2017AZUR4039/document.
Full textAutomatic short text classification is more and more used nowadays in various applications like sentiment analysis or spam detection. Short texts like tweets or SMS are more challenging than traditional texts. Therefore, their classification is more difficult owing to their shortness, sparsity and lack of contextual information. We present two new approaches to improve short text classification. Our first approach is "Semantic Forest". The first step of this approach proposes a new enrichment method that uses an external source of enrichment built in advance. The idea is to transform a short text from few words to a larger text containing more information in order to improve its quality before building the classification model. Contrarily to the methods proposed in the literature, the second step of our approach does not use traditional learning algorithm but proposes a new one based on the semantic links among words in the Random Forest classifier. Our second contribution is "IGLM" (Interactive Generic Learning Method). It is a new interactive approach that recursively updates the classification model by considering the new data arriving over time and by leveraging the user intervention to correct misclassified data. An abstraction method is then combined with the update mechanism to improve short text quality. The experiments performed on these two methods show their efficiency and how they outperform traditional algorithms in short text classification. Finally, the last part of the thesis concerns a complete and argued comparative study of the two proposed methods taking into account various criteria such as accuracy, speed, etc
Renaudie, David. "Méthodes d’apprentissage automatique pour la modélisation de l’élève en algèbre." Grenoble INPG, 2005. http://www.theses.fr/2005INPG0008.
Full textBuilding an Intelligent Tutoring System which adapts to student difficulties, needs to develop automatic tools that diagnose their knowledge level. This work is based on a database containing behaviours of students, who are learning algebra with the Aplusix software. We aim at automatically extracting behavioural regularities from this database, in order to support the developpement of an intelligent tutor. To achieve this goal, we apply machine learning methods that detect similarities in datasets, and we propose two distinct approaches to student modelling issues. One one hand, we identify clusters of students that share behavioural similarities on a given exercise, with an unsupervised clustering algorithm. On the other hand, an analysis of procedural regularities in the production of each student, inspired from a theoretical framework of knowledge representation and based on a symbolic learning algorithm, leads to individual models that could be used for adapted remediation
Achoch, Mounia. "Méthodes d’apprentissage et approches expérimentales appliqués aux réseaux d’interfaces protéiques." Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAA022/document.
Full textThe aim of this study is to understand protein assembly mechanisms. The assembly of a protein in an oligomer is particularly important because it is involved in many pathologies going from bacterial infection, Alzheimer like diseases or even some cancers. Protein assembly is the combination of two or more protein chains to induce a biological activity. The B subunit of the cholera toxin pentamer (CtxB5), which belongs to the family of AB5 toxins, is studied as the main model of assembly. Experimental results have provided information on the assembly of the toxin highlighting the involvement of certain amino acids. The first problem addressed in my thesis is to understand their role and see if network approaches are relevant to such investigation. I was able to show using amino acid mutations, that amino acids influence each other by cascade or "peer to peer" mechanisms in order to coordinate the various steps of the assembly (Chapters 4, 5 and 6). The structure and function of the proteins are defined by amino acid sequences which naturally vary due to genetic mutation. So I decided to expand this field of investigation to see if the cascade mechanism was generalized as a mean of disrupting a protein structure. Here it is to understand how a protein loses its function by way of a significant change of structure upon mutation. First, I studied dataset to know the characteristics of healthy protein networks (Chapter 7, 8 and 9), and after I looked at the effects of the systematic mutation of each amino acid of CtxB5 on its overall structure (Chapter 10 and 11). Mutations led from moderate to very large structural changes around the mutated amino acid or at long distances. These results are consistent with known effects of mutation: robustness (maintenance function), evolution or adaptation (emergence of a new feature) and fragility (pathologies). The results also show a weak correlation between the number of amino acid contacts of the mutated amino acid and the amount of structural change induced by its mutation. It is therefore not easy to anticipate the effect of a mutation: The last chapter of my thesis addresses this problem (Chapter 12)
Szilagyi, Ioan. "Technologies sémantiques pour un système actif d’apprentissage." Thesis, Besançon, 2014. http://www.theses.fr/2014BESA1008/document.
Full textLearning methods keep evolving and new paradigms are added to traditional teaching models where the information and communication systems, particularly the Web, are an essential part. In order to improve the processing capacity of information systems, the Semantic Web defines a model for describing resources (Resource Description Framework - RDF), and a language for defining ontologies (Web Ontology Language – OWL). Based on concepts, methods, learning theories, and following a systemic approach, we have used Semantic Web technologies in order to provide a learning system that is able to enrich and personalize the experience of the learner. As a result of our work we are proposing a prototype for an Active Semantic Learning System (SASA). Following the identification and modeling of entities involved in the learning process, we created the following six ontologies that summarize the characteristics of these entities: (1) learner ontology, (2) learning object ontology, (3) learning objective ontology, (4) evaluation object ontology, (5) annotation object ontology and (6) learning framework ontology. Integrating certain rules in the declared ontologies combined with reasoning capacities of the inference engines embedded in the kernel of the SASA, allow the adaptation of learning content to the characteristics of learners. The use of semantic technologies facilitates the identification of existing learning resources on the web as well as the interpretation and aggregation of these resources within the context of SASA
Books on the topic "Méthodes d’apprentissage en ligne"
College, Seneca, Humber College, Kenjgewin Teg, et Nipissing University, and Trent University. Conception et élaboration d’expériences d’apprentissage en ligne et hybrides de haute qualité et centrées sur l’étudiant. eCampusOntario Open Authoring Platform, 2022.
Find full textBook chapters on the topic "Méthodes d’apprentissage en ligne"
Lachance, Lise, Louis Cournoyer, and Louis Richer. "ENQUÊTES EN LIGNE:." In Méthodes qualitatives, quantitatives et mixtes, 2e édition, 753–74. Presses de l'Université du Québec, 2020. http://dx.doi.org/10.2307/j.ctv1c29qz7.33.
Full textLacourse, Eric, Charles-Édouard Giguère, and Véronique Dupéré. "ALGORITHMES D’APPRENTISSAGE ET MODÈLES STATISTIQUES:." In Méthodes qualitatives, quantitatives et mixtes, 2e édition, 581–612. Presses de l'Université du Québec, 2020. http://dx.doi.org/10.2307/j.ctv1c29qz7.28.
Full textRoginsky, Sandrine. "Les terrains de recherche en ligne et hors-ligne : proposition pour une triangulation des méthodes." In Méthodes de recherche en contexte numérique, 119–36. Les Presses de l’Université de Montréal, 2020. http://dx.doi.org/10.1515/9782760642508-009.
Full textMoscarola, Jean. "Chapitre 12. Questionnaires et questionnaire en ligne." In Les méthodes de recherche du DBA, 201. EMS Editions, 2018. http://dx.doi.org/10.3917/ems.cheva.2018.01.0201.
Full textYUN-ROGER, Soyoung. "Dictionnaire ouvert et traducteur automatique au service de l’enseignement/apprentissage du lexique." In Dictionnaires et apprentissage des langues, 33–42. Editions des archives contemporaines, 2021. http://dx.doi.org/10.17184/eac.4501.
Full textHine, Christine. "L’ethnographie des communautés en ligne et des médias sociaux : modalités, diversité, potentialités." In Méthodes de recherche en contexte numérique, 77–101. Les Presses de l’Université de Montréal, 2020. http://dx.doi.org/10.1515/9782760642508-007.
Full textALFIERI, Olivier. "Université de Hearst, un modèle d’apprentissage hybride en ligne pour développer des compétences." In Pratiques et innovations à l'ère du numérique en formation à distance, 185–98. Presses de l'Université du Québec, 2019. http://dx.doi.org/10.2307/j.ctvq4bz91.17.
Full textROCHDI, Sara, and Nadia EL OUESDADI. "Les étudiants et les pratiques numériques informelles: échange et collaboration sur le réseau social Facebook." In Langue(s) en mondialisation, 127–36. Editions des archives contemporaines, 2022. http://dx.doi.org/10.17184/eac.5204.
Full textFuhrer, Chantal. "L'Afrique en transformation." In L'Afrique en transformation, 103–14. EMS Éditions, 2024. https://doi.org/10.3917/ems.kamde.2024.01.0103.
Full textSbihi, Boubker, Imad Belghit, Badr Elhail, and Rachid Gouarti. "Vers une stratégie collaborative centrée sur les apprenants et les données du Big Data pour améliorer le processus d’apprentissage en ligne." In Big Data - Open Data : Quelles valeurs ? Quels enjeux ?, 251. De Boeck Supérieur, 2015. http://dx.doi.org/10.3917/dbu.chron.2015.01.0251.
Full textConference papers on the topic "Méthodes d’apprentissage en ligne"
CHTIOUI, Researcher Jamila. "HOW CAN STUDY GROUPS BE MADE EFFECTIVE AT UNIVERSITY?" In IV. International research Scientific Congress of Humanities and Social Sciences. Rimar Academy, 2023. http://dx.doi.org/10.47832/ist.con4-2.
Full textCatros, S., M. Fenelon, A. Rui, K. Ross, D. Marcio, B. Angel, M. D. S. Luis, et al. "Création d’un site internet Européen de formation au sevrage tabagique." In 66ème Congrès de la SFCO. Les Ulis, France: EDP Sciences, 2020. http://dx.doi.org/10.1051/sfco/20206603002.
Full textReports on the topic "Méthodes d’apprentissage en ligne"
Georgalakis, James, and Fajri Siregar. L’application des connaissances dans les pays du Sud : Établir des liens entre les différents systèmes de connaissances pour un développement équitable. Institute of Development Studies, August 2023. http://dx.doi.org/10.19088/ids.2023.041.
Full textBrinkerhoff, Derick W., Sarah Frazer, and Lisa McGregor. S'adapter pour apprendre et apprendre pour s'adapter : conseils pratiques tirés de projets de développement internationaux. RTI Press, January 2018. http://dx.doi.org/10.3768/rtipress.2018.pb.0015.1801.fr.
Full text