abstract: For this master's thesis, an open learner model is integrated with Quinn, a teachable robotic agent developed at Arizona State University. This system is represented as a feedback system, which aims to improve a student’s understanding of a subject. It also helps to understand the effect of the learner model when it is represented by performance of the teachable agent. The feedback system represents performance of the teachable agent, and not of a student. Data in the feedback system is thus updated according to a student's understanding of the subject. This provides students an opportunity to enhance their understanding of a subject by analyzing their performance. To test the effectiveness of the feedback system, student understanding in two different conditions is analyzed. In the first condition a feedback report is not provided to the students, while in the second condition the feedback report is provided in the form of the agent’s performance. / Dissertation/Thesis / Masters Thesis Computer Science 2016

A identificação de características que sejam importantes sobre um dado estudante durante o processo de ensino-aprendizagem tem sido alvo de muitos estudos nos últimos tempos. Docentes, coordenadores e pedagogos têm defendido que cada indivíduo possui características particulares e importantes dentro do processo de desenvolvimento do conhecimento. A necessidade de adaptar as estratégias de ensino de acordo com as preferências do aprendiz é uma realidade dentro das salas de aula, sejam estas presenciais ou virtuais. Porém, isto não significa que numa sala de aula deva haver criação de um método para cada aluno, mas sim que se aponte qual a melhor forma de interação para cada um deles construindo grupos de aprendizes com características comuns. Trabalhando desta forma se torna possível identificar perfis de aprendizes dentro de um conjunto de estudantes, possibilitando que se possa trabalhar com conteúdos e ambientes de aprendizagem mais adequados aos aprendizes.O objetivo deste trabalho é apresentar a arquitetura de um sistema que possibilita a criação de cenários de aprendizagem baseados no perfil do aluno. O docente deve indicar as características que devem ser observadas durante as interações do aprendiz em um dado ambiente de aprendizagem eletrônica. Estas características serão modeladas como informações de contexto, permitindo que os pontos definidos como observáveis sejam organizados e modelados de forma a facilitar a monitoração das interações. A classificação do aluno em um determinando tipo de perfil de aprendizagem, previamente definido pelo docente, é realizado considerando-se as informações sobre o contexto da interação e os tipos de perfis de aprendizagem. Para que seja possível construir os cenários de aprendizagem considerando o perfil do aluno este trabalho propõe criar um relacionamento entre os tipos de perfis de aprendizagem e as estratégias de ensino através das categorias descritoras dos objetos de aprendizagem. / The identification of characteristics of a given student that are important during the teachinglearning process has been the focus of numerous studies in recent years. Teachers, coordinators and pedagogues have defended the notion that each individual presents particular and important characteristics in the developing knowledge process. The need to adapt teaching strategies to the student\'s preferences is a reality in classrooms, being physical or virtual classrooms. However, this does not mean that a method should be created for each student in a classroom, but that the best form of interaction for each of them be identified, building groups of learners with common characteristics. Working in this way makes it possible to identify learner profiles within a group of students, allowing one to work with learning contents and environments more suited to the learners. The objective of this work is to present the architecture of a system that allows for the creation of learning scenarios based on the studen profile. The teacher should point out the features which may be observed during the student interaction in a given e-elearning environment. These features are designed as context information, allowing defined observation points to be organized and modeled for facilitating the monitoring of interactions. The student classification in a given learning profile type, defined previously by the teacher, is realized considering information about the context of interaction and the learning profile types. For the learning environment to build the learning scenarios according to the student profile, this work proposes to create a relationship between the learning profile types and the teaching strategies through the description of learning objects categories.

A identificação de características que sejam importantes sobre um dado estudante durante o processo de ensino-aprendizagem tem sido alvo de muitos estudos nos últimos tempos. Docentes, coordenadores e pedagogos têm defendido que cada indivíduo possui características particulares e importantes dentro do processo de desenvolvimento do conhecimento. A necessidade de adaptar as estratégias de ensino de acordo com as preferências do aprendiz é uma realidade dentro das salas de aula, sejam estas presenciais ou virtuais. Porém, isto não significa que numa sala de aula deva haver criação de um método para cada aluno, mas sim que se aponte qual a melhor forma de interação para cada um deles construindo grupos de aprendizes com características comuns. Trabalhando desta forma se torna possível identificar perfis de aprendizes dentro de um conjunto de estudantes, possibilitando que se possa trabalhar com conteúdos e ambientes de aprendizagem mais adequados aos aprendizes.O objetivo deste trabalho é apresentar a arquitetura de um sistema que possibilita a criação de cenários de aprendizagem baseados no perfil do aluno. O docente deve indicar as características que devem ser observadas durante as interações do aprendiz em um dado ambiente de aprendizagem eletrônica. Estas características serão modeladas como informações de contexto, permitindo que os pontos definidos como observáveis sejam organizados e modelados de forma a facilitar a monitoração das interações. A classificação do aluno em um determinando tipo de perfil de aprendizagem, previamente definido pelo docente, é realizado considerando-se as informações sobre o contexto da interação e os tipos de perfis de aprendizagem. Para que seja possível construir os cenários de aprendizagem considerando o perfil do aluno este trabalho propõe criar um relacionamento entre os tipos de perfis de aprendizagem e as estratégias de ensino através das categorias descritoras dos objetos de aprendizagem. / The identification of characteristics of a given student that are important during the teachinglearning process has been the focus of numerous studies in recent years. Teachers, coordinators and pedagogues have defended the notion that each individual presents particular and important characteristics in the developing knowledge process. The need to adapt teaching strategies to the student\'s preferences is a reality in classrooms, being physical or virtual classrooms. However, this does not mean that a method should be created for each student in a classroom, but that the best form of interaction for each of them be identified, building groups of learners with common characteristics. Working in this way makes it possible to identify learner profiles within a group of students, allowing one to work with learning contents and environments more suited to the learners. The objective of this work is to present the architecture of a system that allows for the creation of learning scenarios based on the studen profile. The teacher should point out the features which may be observed during the student interaction in a given e-elearning environment. These features are designed as context information, allowing defined observation points to be organized and modeled for facilitating the monitoring of interactions. The student classification in a given learning profile type, defined previously by the teacher, is realized considering information about the context of interaction and the learning profile types. For the learning environment to build the learning scenarios according to the student profile, this work proposes to create a relationship between the learning profile types and the teaching strategies through the description of learning objects categories.

Le module de l'apprenant est l'une des composantes les plus importantes d’un Système Tutoriel Intelligent (STI). L'extension du modèle de l'apprenant n'a pas cessé de progresser. Malgré la définition d’un profil cognitif et l’intégration d’un profil émotionnel, le module de l’apprenant demeure non exhaustif. Plusieurs senseurs physiologiques sont utilisés pour raffiner la reconnaissance des états cognitif et émotionnel de l’apprenant mais l’emploi simultané de tous ces senseurs l’encombre. De plus, ils ne sont pas toujours adaptés aux apprenants dont les capacités sont réduites. Par ailleurs, la plupart des stratégies pédagogiques exécutées par le module du tuteur ne sont pas conçues à la base d’une collecte dynamique de données en temps réel, cela diminue donc de leur efficacité. L’objectif de notre recherche est d’explorer l’activité électrique cérébrale et de l’utiliser comme un nouveau canal de communication entre le STI et l’apprenant. Pour ce faire nous proposons de concevoir, d’implémenter et d’évaluer le système multi agents NORA. Grâce aux agents de NORA, il est possible d’interpréter et d’influencer l’activité électrique cérébrale de l’apprenant pour un meilleur apprentissage. Ainsi, NORA enrichit le module apprenant d’un profile cérébral et le module tuteur de quelques nouvelles stratégies neuropédagogiques efficaces. L’intégration de NORA à un STI donne naissance à une nouvelle génération de systèmes tutoriels : les STI Cérébro-sensibles (ou STICS) destinés à aider un plus grand nombre d’apprenants à interagir avec l’ordinateur pour apprendre à gérer leurs émotions, maintenir la concentration et maximiser les conditions favorable à l’apprentissage. / The learner module is the most important component within an Intelligent Tutoring System (ITS). The extension of the learner module is still in progress, despite the integration of the cognitive profile and the emotional profile, it is not yet exhaustive. To improve the prediction of the learner’s emotional and cognitive states, many physiological sensors have been used, but all of these sensors are cumbersome. In addition, they are not always adapted to the learners with reduced capacities. Beside, most of the pedagogical strategies that are executed by the tutor module are based on no-live collections of data. This fact reduces their efficiency. The objective of our research is to explore the electrical brain activity and use it as a communication channel between a learner and an ITS. To reach this aim, we suggest to conceive, to implement and to evaluate the multi-agent system NORA. Integrated to an ITS, this one became a Brain Sensitive Intelligent Tutoring System (BS-ITS). Agents of NORA interpret the learner’s brain electrical signal and react to it. The new BS-ITS is the extension of an ITS and enrich the learner module with the brain profile and the tutor module with a new Neuropedagogical Strategies. We aim to reach more categories of learners and help them to manage their stress, anxiety and maintain the concentration, the attention and the interest.

Le module de l'apprenant est l'une des composantes les plus importantes d’un Système Tutoriel Intelligent (STI). L'extension du modèle de l'apprenant n'a pas cessé de progresser. Malgré la définition d’un profil cognitif et l’intégration d’un profil émotionnel, le module de l’apprenant demeure non exhaustif. Plusieurs senseurs physiologiques sont utilisés pour raffiner la reconnaissance des états cognitif et émotionnel de l’apprenant mais l’emploi simultané de tous ces senseurs l’encombre. De plus, ils ne sont pas toujours adaptés aux apprenants dont les capacités sont réduites. Par ailleurs, la plupart des stratégies pédagogiques exécutées par le module du tuteur ne sont pas conçues à la base d’une collecte dynamique de données en temps réel, cela diminue donc de leur efficacité. L’objectif de notre recherche est d’explorer l’activité électrique cérébrale et de l’utiliser comme un nouveau canal de communication entre le STI et l’apprenant. Pour ce faire nous proposons de concevoir, d’implémenter et d’évaluer le système multi agents NORA. Grâce aux agents de NORA, il est possible d’interpréter et d’influencer l’activité électrique cérébrale de l’apprenant pour un meilleur apprentissage. Ainsi, NORA enrichit le module apprenant d’un profile cérébral et le module tuteur de quelques nouvelles stratégies neuropédagogiques efficaces. L’intégration de NORA à un STI donne naissance à une nouvelle génération de systèmes tutoriels : les STI Cérébro-sensibles (ou STICS) destinés à aider un plus grand nombre d’apprenants à interagir avec l’ordinateur pour apprendre à gérer leurs émotions, maintenir la concentration et maximiser les conditions favorable à l’apprentissage. / The learner module is the most important component within an Intelligent Tutoring System (ITS). The extension of the learner module is still in progress, despite the integration of the cognitive profile and the emotional profile, it is not yet exhaustive. To improve the prediction of the learner’s emotional and cognitive states, many physiological sensors have been used, but all of these sensors are cumbersome. In addition, they are not always adapted to the learners with reduced capacities. Beside, most of the pedagogical strategies that are executed by the tutor module are based on no-live collections of data. This fact reduces their efficiency. The objective of our research is to explore the electrical brain activity and use it as a communication channel between a learner and an ITS. To reach this aim, we suggest to conceive, to implement and to evaluate the multi-agent system NORA. Integrated to an ITS, this one became a Brain Sensitive Intelligent Tutoring System (BS-ITS). Agents of NORA interpret the learner’s brain electrical signal and react to it. The new BS-ITS is the extension of an ITS and enrich the learner module with the brain profile and the tutor module with a new Neuropedagogical Strategies. We aim to reach more categories of learners and help them to manage their stress, anxiety and maintain the concentration, the attention and the interest.

This work presents an interactive environment for learning about fractions, with mechanisms to support cooperative and adaptive interactions offered by tutors agents to human learners, focusing mainly on activities to solve problems. For this purpose, an architecture based on software agents and semantic Web services was proposed, therefore, we verify the functional viability of the proposal and, posteriorly, to present a revision of that architecture to suply some requirements not previously covered, beyond models that support to those interactions. With respect to interactions, the learner will receive support from both a pedagogical agent tutor, as some of their peers who are part of the environment. Particularly, a tutor agent has an open learner model, from which it obtains information to guide their actions. The idea of this model be opened is to allow the learner seeing the evaluation that the system has about him, and also the opportunity to disagree with this assessment, and thus contribute to the refinement of the content of such a model / Fundação de Amparo a Pesquisa do Estado de Alagoas / Este trabalho apresenta um ambiente interativo de aprendizagem sobre Frações, dotado de mecanismos de suporte a interações cooperativas e adaptativas oferecidas por seus agentes tutores aos aprendizes humanos, focando principalmente em atividades de resolução de problemas. Para isso, propõe-se uma arquitetura baseada em agentes de software e serviços Web semânticos, daí, pôde-se verificar a viabilidade funcional da proposta e, posteriormente, apresentar uma revisão de tal arquitetura para suprir alguns requisitos anteriormente não visados, além de modelos que dão suporte às referidas interações. No que diz respeito às interações, o aprendiz receberá suporte pedagógico tanto de um agente tutor, quanto de algum de seus pares que fazem parte do ambiente. Particularmente, um agente tutor conta com um modelo aberto do aprendiz, a partir do qual passa a dispor de informações úteis para orientar suas ações. A idéia deste modelo ser aberto é a de permitir que o aprendiz possa ver qual a avaliação que o sistema tem a seu respeito, tendo ainda a oportunidade de discordar de tal avaliação, e assim contribuir para o refinamento do conteúdo de tal modelo. Palavras-chave: Modelagem Aberta do Aprendiz; Sistemas Tutores Inteligentes; Sistemas Multi-agentes

Made available in DSpace on 2015-04-11T14:03:19Z (GMT). No. of bitstreams: 1 Vitor Bremgartner da Frota.pdf: 2212741 bytes, checksum: d795bab086a9fe7a83cb7bac13963dc1 (MD5) Previous issue date: 2012-02-29 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Com a disseminação dos cursos de Educação a Distância, um problema cada vez mais frequente é a falta de um acompanhamento personalizado ao aluno e a demora em responder por parte de mediadores e demais colegas às dúvidas e requisições dos alunos em Ambientes Virtuais de Aprendizagem (AVAs), normalmente postadas em fóruns de discussão ou manifestada via e-mails. A abordagem adotada para solução deste problema apresentada nesta dissertação baseia-se em Sistemas Multiagente e em uma ontologia estendida da especificação IMS Learner Information Package (LIP) a partir da teoria de habilidades e competências desenvolvida pelo sociólogo suíço Phillipe Perrenoud. Por meio dos agentes e da ontologia são detectadas as eventuais dúvidas dos alunos e estas são direcionadas aos membros da comunidade que possuem o perfil mais adequado a solucioná-la, isto é, para aqueles que possuem as habilidades e competências adequadas, diminuindo a demora de resposta às dúvidas dos alunos. Dessa forma, os alunos poderão interagir entre si com o propósito de solucionar eventuais dúvidas ou erros em determinadas atividades, compartilhando conhecimentos. A solução descrita nesta dissertação se baseia na aprendizagem colaborativa pela interação entre aprendizes, na qual é uma estratégia empregada com o propósito de construir conhecimento de maneira mais significativa em um AVA. O processo de recomendação personalizada é realizado por agentes que utilizam a ontologia. Para validação do sistema, foi desenvolvida uma Rede de Petri que representa as interações entre os agentes, além de simular a correta ordem de execução ou o paralelismo entre eles. A partir dessas concepções, foi implementado um SMA que utiliza a ontologia de modelo de aluno desenvolvida e este foi empregado para auxiliar alunos e tutores em um curso de Cálculo Numérico que faz uso do AVA Moodle. Os resultados obtidos com os testes aplicados no sistema comprovam a validade e a viabilidade da solução encontrada, composta de duas etapas: uma simulação em uma turma fictícia, a fim de avaliar a eficácia dos agentes e da ontologia e outro teste em uma turma real, com o objetivo de avaliar a eficácia do processo de recomendação entre os alunos. Questionários de pesquisa de opinião foram passados em sala de aula a fim de obter e avaliar as impressões dos alunos quanto ao recurso disponível aos mesmos no AVA.

Les ordinateurs peuvent-ils enseigner ? Pour répondre à cette question, la recherche dans les Systèmes Tuteurs Intelligents est en pleine expansion parmi la communauté travaillant sur les Technologies de l'Information et de la Communication pour l'Enseignement (TICE). C'est un domaine qui rassemble différentes problématiques et réunit des chercheurs venant de domaines variés, tels que la psychologie, la didactique, les neurosciences et, plus particulièrement, le machine learning. Les technologies numériques deviennent de plus en plus présentes dans la vie quotidienne avec le développement des tablettes et des smartphones. Il semble naturel d'utiliser ces technologies dans un but éducatif. Cela amène de nombreuses problématiques, telles que comment faire des interfaces accessibles à tous, comment rendre des contenus pédagogiques motivants ou encore comment personnaliser les activités afin d'adapter le contenu à chacun. Au cours de cette thèse, nous avons développé des méthodes, regroupées dans un framework nommé HMABITS, afin d'adapter des séquences d'activités pédagogiques en fonction des performances et des préférences des apprenants, dans le but de maximiser leur vitesse d'apprentissage et leur motivation. Ces méthodes utilisent des modèles computationnels de motivation intrinsèque pour identifier les activités offrant les plus grands progrès d'apprentissage, et utilisent des algorithmes de Bandits Multi-Bras pour gérer le compromis exploration/exploitation à l'intérieur de l'espace d'activité. Les activités présentant un intérêt optimal sont ainsi privilégiées afin de maintenir l'apprenant dans un état de Flow ou dans sa Zone de Développement Proximal. De plus, certaines de nos méthodes permettent à l'apprenant de faire des choix sur des caractéristiques contextuelles ou le contenu pédagogique de l'application, ce qui est un vecteur d'autodétermination et de motivation. Afin d'évaluer l'efficacité et la pertinence de nos algorithmes, nous avons mené plusieurs types d'expérimentation. Nos méthodes ont d'abord été testées en simulation afin d'évaluer leur fonctionnement avant de les utiliser dans d'actuelles applications d'apprentissage. Pour ce faire, nous avons développé différents modèles d'apprenants, afin de pouvoir éprouver nos méthodes selon différentes approches, un modèle d'apprenant virtuel ne reflétant jamais le comportement d'un apprenant réel. Les résultats des simulations montrent que le framework HMABITS permet d'obtenir des résultats d'apprentissage comparables et, dans certains cas, meilleurs qu'une solution optimale ou qu'une séquence experte. Nous avons ensuite développé notre propre scénario pédagogique et notre propre serious game afin de tester nos algorithmes en situation réelle avec de vrais élèves. Nous avons donc développé un jeu sur la thématique de la décomposition des nombres, au travers de la manipulation de la monnaie, pour les enfants de 6 à 8 ans. Nous avons ensuite travaillé avec le rectorat et différentes écoles de l'académie de bordeaux. Sur l'ensemble des expérimentations, environ 1000 élèves ont travaillé sur l'application sur tablette. Les résultats des études en situation réelle montrent que le framework HMABITS permet aux élèves d'accéder à des activités plus diverses et plus difficiles, d'avoir un meilleure apprentissage et d'être plus motivés qu'avec une séquence experte. Les résultats montrent même que ces effets sont encore plus marqués lorsque les élèves ont la possibilité de faire des choix. / Can computers teach people? To answer this question, Intelligent Tutoring Systems are a rapidly expanding field of research among the Information and Communication Technologies for the Education community. This subject brings together different issues and researchers from various fields, such as psychology, didactics, neurosciences and, particularly, machine learning. Digital technologies are becoming more and more a part of everyday life with the development of tablets and smartphones. It seems natural to consider using these technologies for educational purposes. This raises several questions, such as how to make user interfaces accessible to everyone, how to make educational content motivating and how to customize it to individual learners. In this PhD, we developed methods, grouped in the aptly-named HMABITS framework, to adapt pedagogical activity sequences based on learners' performances and preferences to maximize their learning speed and motivation. These methods use computational models of intrinsic motivation and curiosity-driven learning to identify the activities providing the highest learning progress and use Multi-Armed Bandit algorithms to manage the exploration/exploitation trade-off inside the activity space. Activities of optimal interest are thus privileged with the target to keep the learner in a state of Flow or in his or her Zone of Proximal Development. Moreover, some of our methods allow the student to make choices about contextual features or pedagogical content, which is a vector of self-determination and motivation. To evaluate the effectiveness and relevance of our algorithms, we carried out several types of experiments. We first evaluated these methods with numerical simulations before applying them to real teaching conditions. To do this, we developed multiple models of learners, since a single model never exactly replicates the behavior of a real learner. The simulation results show the HMABITS framework achieves comparable, and in some cases better, learning results than an optimal solution or an expert sequence. We then developed our own pedagogical scenario and serious game to test our algorithms in classrooms with real students. We developed a game on the theme of number decomposition, through the manipulation of money, for children aged 6 to 8. We then worked with the educational institutions and several schools in the Bordeaux school district. Overall, about 1000 students participated in trial lessons using the tablet application. The results of the real-world studies show that the HMABITS framework allows the students to do more diverse and difficult activities, to achieve better learning and to be more motivated than with an Expert Sequence. The results show that this effect is even greater when the students have the possibility to make choices.