L’adoption des Nouvelles Technologies de l’Information et de la Communication (NTIC) a permis la modernisation des méthodes d’enseignement dans les systèmes d’apprentissage en ligne comme l’e-Learning, les systèmes tutoriels intelligents, etc. Ces derniers assurent une formation à distance qui répond aux besoins des apprenants. Un aspect très important à prendre en considération dans ces systèmes est l’évaluation précoce de l’apprenant en termes d’acquisition des connaissances. En général, trois types d’évaluation et leurs relations sont nécessaires durant le processus d’apprentissage, à savoir : (i) diagnostic qui est exécuté avant l’apprentissage pour estimer le niveau des élèves, (ii) évaluation formative qui est appliquée lors de l’apprentissage pour tester l’évolution des connaissances et (iii) évaluation sommative qui est considérée après l’apprentissage pour évaluer l’acquisition des connaissances. Ces méthodes peuvent être intégrées d’une manière semi-automatique, automatique ou adaptée aux différents contextes de formation, par exemple dans le domaine d’apprentissage des langues (français, anglais, etc.), des sciences fondamentales (mathématiques, physique, chimie, etc.) et langages de programmation (java, python, sql, etc.) Cependant, les méthodes d’évaluation usuelles sont statiques et se basent sur des fonctions linéaires qui ne prennent en considération que la réponse de l’apprenant. Elles ignorent, en effet, d’autres paramètres de son modèle de connaissances qui peuvent divulguer d’autres indicateurs de performance. Par exemple, le temps de résolution d’un problème, le nombre de tentatives, la qualité de la réponse, etc. Ces éléments servent à détecter les traits du profil, le comportement ainsi que les troubles d’apprentissage de l’apprenant. Ces paramètres additionnels sont vus dans nos travaux de recherche comme des traces d’apprentissage produites par l’apprenant durant une situation ou un contexte pédagogique donné. Dans ce cadre, nous proposons dans cette thèse une approche d’évaluation de l’apprenant à base des traces d’apprentissage qui peut être exploitée dans un système d’adaptation de la ressource et/ou de la situation pédagogique. Pour l’évaluation de l’apprenant, nous avons proposé trois modèles génériques d’évaluation qui prennent en considération la trace temporelle, le nombre de tentatives et leurs combinaisons. Ces modèles ont servi, par la suite, comme métrique de base à notre modèle d’adaptation de la ressource et/ou de la situation d’apprentissage. Le modèle d’adaptation est également fondé sur les trois traces susmentionnées et sur nos modèles d’évaluation. Notre modèle d’adaptation génère automatiquement des trajectoires d’apprentissage adaptées en utilisant un modèle d’état-transition. Les états présentent des situations d’apprentissage qui consomment des ressources et les transitions entre situations expriment les conditions nécessaires à remplir pour passer d’une situation à une autre. Ces concepts sont aussi implémentés dans une ontologie du domaine et un algorithme d’adaptation a été également proposé. L’algorithme assure deux types d’adaptation : (i) Adaptation de la situation et (ii) Adaptation des ressources dans une situation. Afin de collecter les traces d’apprentissage pour la mise en œuvre de notre approche d’évaluation de l’apprenant et d’adaptation de ressources et de situations d’apprentissage, nous avons effectué des expérimentations sur deux groupes d’étudiants en Licence Informatique (L2). Un groupe en apprentissage classique et un groupe en apprentissage adapté. Sur la base des traces obtenues des sessions de travail des étudiants, nous avons utilisé nos modèles d’évaluation dont les résultats ont été utilisés pour mettre en œuvre l’adaptation. Après comparaison des résultats de l’apprentissage adapté à ceux obtenus de l’apprentissage classique, nous avons constaté une amélioration des résultats en termes de moyenne générale et d’écart-type des moyennes des apprenants. / 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.

Made available in DSpace on 2016-03-15T19:37:32Z (GMT). No. of bitstreams: 1 Samuel Fontes Lima.pdf: 2071921 bytes, checksum: 2d66276b3ab526cbf68f0f727c2e1f3d (MD5) Previous issue date: 2010-08-18 / Nowadays, object oriented languages are among the most used ones. Recent studies with beginners have shown that the object oriented programming language (OOP) concepts are not so easy to learn. Students have difficulties not only in understanding those OOP concepts, but also in applying them to solving problems. Therefore, an Intelligent Tutoring System (ITS) is suggested in order to help in the learning process of these concepts. One of the most important challenge for ITS development is the individual treatment of student, which is mainly obtained by system adaptativity to the characteristics of each student. This adaptativity to student is a complex issue and the focus of several researches. It encloses several areas of personality such as: learning style, intelligence, previous knowledge, student background and emotions. Thus, the student model is extremely important because all individual information of student is in it. It is here where Artificial Intelligence (AI) techniques have been employed. This paper approaches the adaptativity issue of tutoring system to student knowledge through a student model based on Bayesian Network (BN). Taking into consideration that the student knowledge level is an inaccurate information, and even professors deal with this uncertainty, the Bayesian Networks (BNs), also known as Belief Network, have been chosen. They are considered proper for uncertain spheres for employing the probabilistic reasoning, which allows to identify a certain belief degree of student knowledge level. Therefore, the BNs have been employed in ITS in the inferences concerning student behavior as well as in the decision-making process concerning tutor actions. / Atualmente as linguagens orientadas a objeto encontram-se entre as mais utilizadas. Estudos recentes com alunos iniciantes demonstram que os conceitos de programação orientada a objetos (POO) não são tão fáceis de assimilar. Os estudantes encontram dificuldades não somente no entendimento dos conceitos de POO, mas também na aplicação deles na resolução de problemas. Diante disso, propõe-se o emprego de um Sistema Tutor Inteligente (STI) para auxiliar no processo de aprendizagem desses conceitos. Um dos principais desafios para o desenvolvimento de um STI é o tratamento individualizado do estudante, que é obtido principalmente por meio da adaptatividade do sistema às características de cada aprendiz. A adaptatividade ao aprendiz é uma questão complexa, foco de várias pesquisas, abrange várias características da personalidade: estilo de aprendizagem, inteligência, conhecimento anterior, histórico do aprendiz e as emoções. Dessa forma, o modelo do estudante é de fundamental importância, pois contém as informações individuais do aprendiz. É nesse ponto que as técnicas de Inteligência Artificial (IA) têm sido empregadas. Este trabalho aborda a questão da adaptatividade do sistema tutor ao conhecimento do estudante por meio de um modelo de aprendiz baseado em Rede Bayesiana. Considerando-se que o nível de conhecimento do aprendiz é uma informação imprecisa, e que até mesmo professores lidam com essa incerteza, optou-se pela utilização de Redes Bayesianas (RBs), também chamadas de Redes de Crença, que são consideradas adequadas para ambientes sob incerteza pois empregam o raciocínio probabilístico, o qual permite identicar um certo grau de crença sobre o nível de conhecimento do aprendiz. Devido a isso, RBs têm sido empregadas em STIs nas inferências sobre o comportamento do aprendiz e nas tomadas de decisões sobre as ações do tutor.