Academic literature on the topic 'Traces numériques et indicateurs d'apprentissage'
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Journal articles on the topic "Traces numériques et indicateurs d'apprentissage":
Flavia Irene, Santamaria,. "“Un estudio multimodal y dinámico de los conocimientos numéricos de estudiantes de primer grado”." RIDAA Tesis Unicen, September 27, 2021. http://dx.doi.org/10.52278/2850.
Dissertations / Theses on the topic "Traces numériques et indicateurs d'apprentissage":
Ben, Soussia Amal. "Analyse prédictive des données d’apprentissage, en situation d’enseignement à distance." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0216.
Over the past few decades, the adoption of e-learning has evolved rapidly and its use has been pushedeven further with the COVID-19 pandemic. The objective of this learning mode is to guarantee thecontinuity of the learning process. However, the online learning is facing several challenges, and themost widespread is the high failure rates among learners. This issue is due to many reasons such asthe heterogeneity of the learners and the diversity of their learning behaviors, their total autonomy, thelack and/or the inefficiency of the pedagogical provided follow-up. . .. Therefore, teachers need a systembased on analytical and intelligent methods allowing them an accurate and early prediction of at-risk offailure learners. This solution is commonly adopted in the state of the art. However, the work carried outdoes not respond to some particularities of the learning process (the continuity and evolution of learning,the diversity of learners and their total autonomy) and to some teachers expectations such as the alertgeneration.This thesis belongs to the field of learning analytics and uses the numeric traces of online learnersto design a predictive system (Early Warning Systems (EWS)) dedicated to teachers in online establish-ments. The objective of this EWS is to identify learners at risk as soon as possible in order to alertteachers about them. In order to achieve this objective, we have dealt with several sub-problems whichhave allowed us to elaborate four scientific contributions.We start by proposing an in-depth methodology based on the Machine Learning (ML) steps and thatallows the identification of four learning indicators among : performance, engagement, reactivity andregularity. This methodology also highlights the importance of temporal data for improving predictionperformance. In addition, this methodology allowed to define the model with the best ability to identifyat-risk learners.The 2nd contribution consists in proposing a temporal evaluation of the EWS using temporal metricswhich measure the precocity of the predictions and the stability of the system. From these two metrics,we study the trade-offs that exist between ML precision metrics and temporal metrics.Online learners are characterized by the diversity of their learning behaviors. Thus, an EWS shouldrespond to this diversity by ensuring an equitable functioning with the different learners profiles. Wepropose an evaluation methodology based on the identification of learner profiles and that uses a widespectrum of temporal and precision metrics.By using an EWS, teachers expect an alert generation. For this reason, we design an algorithm which,based on the results of the prediction, the temporal metrics and the notion of alert rules, proposes anautomatic method for alert generation. This algorithm targets mainly at-risk learners.The context of this thesis is the French National Center for Distance Education (CNED). In parti-cular, we use the numeric traces of k-12 learners enrolled during the 2017-2018 and 2018-2019 schoolyears
Chachoua, Soraya. "Contribution à l'évaluation de l'apprenant et l'adaptation pédagogique dans les plateformes d'apprentissage : une approche fondée sur les traces." Thesis, La Rochelle, 2019. http://www.theses.fr/2019LAROS003/document.
The adoption of new Information and Communication Technologies (ICT) has enabled the modernization of teaching methods in online learning systems such as e-Learning, intelligent tutorial systems (ITS), etc. These systems provide a remote training that which meets the learner needs. A very important aspect to consider in these systems is the early assessment of the learner in terms of knowledge acquisition. In general, three types of assessment and their relationships are needed during the learning process, namely : (i) diagnostic which is performed before learning to estimate the level of students, (ii) formative evaluation which is applied during learning to test the knowledge evolution and (iii) summative evaluation which is considered after learning to evaluate learner’s knowledge acquisition. These methods can be integrated into a semi-automatic, automatic or adapted way in different contexts of formation, for example in the field of languages literary learning such as French, English, etc., hard sciences (mathematics, physics, chemistry, etc.) and programming languages (java, python, sql, etc.). However, the usual evaluation methods are static and are based on linear functions that only take into account the learner’s response. They ignore other parameters of their knowledge model that may disclose other performance indicators. For example, the time to solve a problem, the number of attempts, the quality of the response, etc. These elements are used to detect the profile characteristics, behavior and learning disabilitiesof the learner. These additional parameters are seen in our research as learning traces produced by the learner during a given situation or pedagogical context. In this context, we propose in this thesis a learner evaluation approach based on learning traces that can be exploited in an adaptation system of the resource and/or the pedagogic situation. For the learner assessment, we have proposed three generic evaluation models that take into consideration the temporal trace, number of attempts and their combinations. These models are later used as a base metric for our resource adaptation model and/or learning situation. The adaptation model is also based on the three traces mentioned above and on our evaluation models. Our adaptation model automatically generates adapted paths using a state-transition model. The states represent learning situations that consume resources and the transitions between situations express the necessary conditions to pass from one situation to another. These concepts are implemented in a domain ontology and an algorithm that we have developed. The algorithm ensures two types of adaptation : (i) Adaptation of the situation and (ii) Adaptation of resources within a situation. In order to collect traces of training for the implementation of our approaches of learner evaluation and adaptation of resources and learning situations, we conducted experiments on two groups of students in Computer Science (L2). One group in classical training and the other group in adapted training. Based on the obtained traces from the students’ training sessions, we assessed merners based on our evaluation models. The results are then used to implement the adaptation in a domain ontology. The latter is implemented within oracle 11g which allows a rule-based semantic reasoning. After comparing the results of the adapted training with those obtained from the classical one, we found an improvement in the results in terms of general average and standard deviation of the learner averages
Toqué, Florian. "Prévision et visualisation de l'affluence dans les transports en commun à l'aide de méthodes d'apprentissage automatique." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2029.
As part of the fight against global warming, several countries around the world, including Canada and some European countries, including France, have established measures to reduce greenhouse gas emissions. One of the major areas addressed by the states concerns the transport sector and more particularly the development of public transport to reduce the use of private cars. To this end, the local authorities concerned aim to establish more accessible, clean and sustainable urban transport systems. In this context, this thesis, co-directed by the University of Paris-Est, the french institute of science and technology for transport, development and network (IFSTTAR) and Polytechnique Montréal in Canada, focuses on the analysis of urban mobility through research conducted on the forecasting and visualization of public transport ridership using machine learning methods. The main motivations concern the improvement of transport services offered to passengers such as: better planning of transport supply, improvement of passenger information (e.g., proposed itinerary in the case of an event/incident, information about the crowd in the train at a chosen time, etc.). In order to improve transport operators' knowledge of user travel in urban areas, we are taking advantage of the development of data science (e.g., data collection, development of machine learning methods). This thesis thus focuses on three main parts: (i) long-term forecasting of passenger demand using event databases, (ii) short-term forecasting of passenger demand and (iii) visualization of passenger demand on public transport. The research is mainly based on the use of ticketing data provided by transport operators and was carried out on three real case study, the metro and bus network of the city of Rennes, the rail and tramway network of "La Défense" business district in Paris, France, and the metro network of Montreal, Quebec in Canada
Terrat, Hélène. "Apports et limites des TICE dans les apprentissages de la langue chez les élèves handicapés moteurs présentant des troubles associés : utilisation des traces numériques pour favoriser l'apprentissage de la langue écrite." Thesis, Lyon 2, 2015. http://www.theses.fr/2015LYO20039.
In our time, Information and Communications Technology (ICT) has become all-pervading in our daily life and allows disabled children more access to the school system, while at the same time drastically changing the way we define the identity and missions of such institution s: ICT tools therefore lead us to question the very foundations of learning and teaching, especially as regards linguistic skills.This study offers a few perspectives on these tools and the possibilities they entail, in order to improve both the assessment of the needs of children with motor disability and associated disorders, in terms of self-esteem, success and autonomy, and the identification and development of strategies for these pupils. Our study first describes, from an ethnographical point of view, the observation and analysis of three videos by three pupils engaged in a course of learning via a digital tracer tool designed especially for the experiment, and the reflexive feedback derived from the traces that were produced. The study then goes on to analyze the results of a survey done with specialized teachers who used our digital tool with a hundred handicapped children. The survey questions users about our initial hypotheses concerning auditory and visual traces, and the tool’s contribution to the development of phonological, morphological and syntactical consciousness, as well as the emergence of metacognition, in order to confirm or disconfirm our observations on the case of the three children mentioned above. For these children with motor disability associated with severe sensory disabilities (linguistic, mnemonic, attention-related) we have privileged, in this tool, vocal feedback as auditory trace, personalization and registering of the work environment, increased reflexivity in the form of a dynamic visual feedback on every action of the child with the help of a “tracer” module, and recording of all events of the history likely to be replayed after the event in a “film” module. A tool used as a means of schooling and learning, Pictop is intended, first and foremost, as an aid to the development of thought contents. This study offers perspectives on new uses of digital traces as an instrument learning and autonomy, especially important for handicapped children, but probably useful to other pupils as well
Book chapters on the topic "Traces numériques et indicateurs d'apprentissage":
ROCHDI, 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.