Dissertations / Theses on the topic 'Learning objects'

PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
O desenvolvimento de conteúdos instrucionais utilizando as novas tecnologias de informação é um processo caro, demorado e complexo, que aponta para o estabelecimento de novas metodologias. É neste contexto que surge o conceito de Objeto de Aprendizagem (LO), cujo enfoque está em promover a reutilização do conteúdo. Entretanto, ao considerar o reuso de conteúdo, também se observa uma necessidade de seqüência - lo para formar conteúdos mais elaborados ou mais complexos. Nesta tese adota-se uma estratégia de representar LOs cada vez menores, representando separadamente conteúdo e prática, aqui denominados Objetos Componentes (OCs). Para a estruturação do conteúdo, adaptou-se uma proposta já existente e definiu-se um esquema conceitual adequado à representação de atividades (ou práticas) de aprendizagem. Com vista à composição dos OCs, foi igualmente definido um esquema conceitual envolvendo conteúdos e práticas. Assim, com base em um algoritmo de seqüenciamento de OCs, um professor pode compreender melhor a forma de implementar um objeto complexo, como uma aula ou um curso, reduzindo erros e eventuais omissões na implementação da solução. Este seqüenciamento deve seguir uma metodologia e deve ser especificado de modo não ambíguo. É neste contexto que também é apresentada uma linguagem para especificação de seqüências de objetos de aprendizagem, com uma sintaxe adequada à descrição das possíveis formas de seqüenciamento de LOs. Finalmente, descreve-se um estudo de caso ilustrando a utilização dos esquemas conceituais desenvolvidos, do algoritmo proposto e da linguagem de especificação de seqüências OCs.
The development of instructional content using new Information Technologies is an expensive, time-consuming and complex process that leads to the development of new methodologies. It was in this context that the concept of Learning Objects (LOs) was proposed as an approach that promotes content reuse. However, if content is expressed as small LOs, it is also necessary to sequence them in order to build more elaborated and complex content. In this thesis we adopt a strategy to represent smaller LOs, modeling not only content but also practice, called Component Objects (COs) herein. In order to structure content we adapted an existing proposal and defined a conceptual schema to structure learning practices (or activities). We also defined a conceptual schema for composing these COs. Then, based on these conceptual schemas it was possible to propose an algorithm for sequencing COs, which supports a teacher/professor to better control the implementation of a complex content such as a class or a course, thus reducing errors and eventual omissions in its implementation. The sequencing process must follow a methodology and must be specified in a nonambiguous way. It is in this context that we also present a specification language for sequences of LOs, with a syntax that is adequate to the description of the possible ways of sequencing LOs. Finally, we describe a case study that shows the conceptual schemas that were proposed and the use of the sequencing algorithm and the specification language.

Ingeniera Civil Eléctrica
El reconocimiento (o detección) de objetos es un área activa y en continua mejora de la visión computacional. Recientemente se han introducido distintas estrategias para mejorar el desempeño y disminuir los costos y el tiempo de detección. Entre estas, se encuentran la generación de Object Proposals (regiones en la imágen donde hay alta probabilidad de encontrar un objeto) para acelerar la etapa de localización, como respuesta al paradigma de ventana deslizante; el cada vez más popular uso de redes Deep Learning y, en particular, para la clasi cación y detección de imágenes, las redes convolucionales (CNN). Si bien existen diversos trabajos que utilizan ambas técnicas, todos ellos se centran en tener una buena performance en conocidas bases de datos y competencias en lugar de estudiar su comportamiento en problemas reales y el efecto que tiene la modi cación de arquitecturas de redes convencionales y la elección adecuada de un sistema de generación de proposals. En este trabajo de título, entonces, se tiene como objetivo principal el caracterizar métodos de generación de proposals para su uso en el reconocimiento de objetos con redes CNN, comparando el desempeño tanto de los proposals generados como del sistema completo en bases de datos fabricadas manualmente. Para estudiar el sistema completo, se comparan dos estructuras conocidas, llamadas R-CNN y Fast R-CNN, que utilizan de distintas formas ambas técnicas (generación de proposals y detección) y donde se considera en el estado del arte mejor Fast R-CNN. Se propone en este trabajo que esta hipótesis no es del todo cierta en el caso de que se trabaje con un número su cientemente bajo de proposals (donde las bases de datos acá construidas se enfocan en precisamente asegurar una cantidad baja de objetos de tamaños similares presentes en cada una: objetos sobre super cies y objetos de una sala de estar) y se acelere el proceso de clasi cación alterando el tamaño de entrada de la red convolucional utilizada. Se eligieron tres métodos de generación de Proposals de la literatura a partir de su desempe ño reportado, y fueron comparados en distintos escenarios sus tiempos de procesamiento, calidad de proposals generados (mediante análisis visual y numérico) en función del número generados de estos. El método llamado BING presenta una ventaja sustancial en términos del tiempo de procesamiento y tiene un desempeño competitivo medido con el recall (fracción de los objetos del ground truth correctamente detectados) para las aplicaciones escogidas. Para implementar R-CNN se entrenan dos redes del tipo SqueezeNet pero con entradas reducidas y seleccionando los 50 mejores proposals generados por BING se encuentra que para una red de entrada 64x64 se alcanza casi el mismo recall (~ 40%) que se obtiene con el Fast R-CNN original y con una mejor precisión, aunque es 5 veces más lento (0.75s versus 0.14s). El sistema R-CNN implementado en este trabajo, entonces, no sólo acelera entre 10 y 20 veces la etapa de generación de proposals en comparación a su implementación original, si no que el efecto de reducir la entrada de la red utilizada logra disminuir el tiempo de detección a uno que es sólo 5 veces más lento que Fast R-CNN cuando antes era hasta 100 veces más lento y con un desempeño equivalente.

Mental representation of hidden objects by pigeons was tested for. Experiment 1 used a series of Piagetian tests of "object permanence" to measure pigeons' capacity to find a stationary food target behind a screen. Performance on these tests did not differ significantly from chance in spite of manipulations designed to enhance the motivational value of the hidden object. Experiment 2 used operant contingencies to test whether pigeons could mentally represent a moving dot on a computer monitor that temporarily "disappeared" behind a screen. Two target durations were used (12 and 24 seconds) for the dot to move across a computer screen. Pigeons were reinforced if their first keypeck occurred when the dot was hidden but not if it occurred when the dot was visible. Phase 1 consisted of target-12 trials, phase 2 consisted of target-24 trials, and phase 3 consisted of alternating sessions of target-12 and target-24 trials. Results demonstrate that while pigeons were able to use timing strategies to respond correctly with an unconstrained choice method, evidence for the use of mental representations by pigeons was inconclusive. Both experiments are discussed in terms of the mechanisms used by pigeons to find hidden objects.

A paradigm shift has occurred in the learning field with the emergence, advances and proliferation of the latest, rapid technologies coupled with improvements in the understanding of learning environments and advances in instructional design. This takes the traditional notions of learning and transports it into the modern world to forge new learning environments that offer substantial benefits over traditional learning environments. Part of this revolution is headed by the learning object paradigm. This research focuses on the development of a personalised learning environment using learning objects as the primary tool and the basis to facilitate the production of a fully robust system and environment that offer learners personalised learning and the benefits that such learning brings.

COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
Objetos de Aprendizagem ou Learning Objects (LOs) são porções de material didático tais como textos que podem ser reutilizados na composição de outros objetos maiores (aulas ou cursos). Um dos problemas da reutilização de LOs é descobri-los em seus contextos ou documentos texto originais tais como livros, e artigos. Visando a obtenção de LOs, este trabalho apresenta um processo que parte da extração, tratamento e carga de uma base de dados textual e em seguida, baseando-se em técnicas de aprendizado de máquina, uma combinação de EM (Expectation-Maximization) e um classificador Bayesiano, classifica-se os textos extraídos. Tal processo foi implementado em um sistema chamado SQLLOMining, que usa SQL como linguagem de programação e técnicas de mineração de texto na busca de LOs.
Learning Objects (LOs) are pieces of instructional material like traditional texts that can be reused in the composition of more complex objects like classes or courses. There are some difficulties in the process of LO reutilization. One of them is to find pieces of documents that can be used like LOs. In this work we present a process that, in search for LOs, starts by extracting, transforming and loading a text database and then continue clustering these texts, using a machine learning methods that combines EM (Expectation- Maximization) and a Bayesian classifier. We implemented that process in a system called SQLLOMining that uses the SQL language and text mining methods in the search for LOs.

Thesis (M.S.)--University of Missouri-Columbia, 2005.
The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file viewed on (December 13, 2006) Includes bibliographical references.

Mathematics learning seems to be a demanding and time-consuming task for many learners. Information and communication technology (ICT) is an attractive tool of learning for students at any level and it can provide an effective atmosphere for understanding mathematics. The question is how to combine mathematics teaching contents, approaches, curricula, and syllabus with new media. The key issue in European educational policy (and other countries as well) is exchange and sharing digital learning resources (learning objects) among countries. In order to accumulate the practice of various countries and use the best digital resources created by different countries, it is necessary to localize learning objects (LO). The paper deals with some problems connected with localization of LO, developed for mathematics education, and presents some solution. Software localization is mainly referred to as language translation (e.g., translation of user interface texts and help documents). However, there are many other important elements depending on the country and people who will use the localized software. In this paper, the main attention is paid to localization of learning objects used for teaching and learning mathematics.

Metadata is descriptive information for data. The purpose of metadata is to facilitate describing, managing and discovering resources in huge distributed repositories. Metadata experts worldwide create the Dublin Core (DC), which acts as a fundamental core metadata standard on which industrial metadata standards based. For educational industry, the need is increasing for description and exploration of a learning object (LO) in distributed learning object repositories (LOR) worldwide. Several organizations aim to establish metadata standards for facilitating better identifying, exchanging and reusing learning objects according to their specific needs. The Institute of Electrical and Electronics Engineers (IEEE) published Learning Object Metadata (LOM), which is a credited standard on the global level since it best represents the characteristics of digital learning objects. Conversion from one metadata standard to another is necessary in case that people want to exchange and reuse learning objects tagged using different kinds of metadata standards. Mapping between the DC and the LOM is an essential job in many e-learning systems. In this thesis, we present a new web based metadata editor for the DC and LOM, and a Web Services oriented mapping tool between them. Other clients can use our editor to create DC or LOM metadata records when catalog their learning objects into our LOR, and integrate the mapping web services as a part of different systems regardless of different platforms, protocols, and displaying devices. Our objective is to promote the reusability and interoperability for both the DC and the LOM users, therefore benefit the learning object industry by lowering the cost of using metadata. The DC-LOM mapping tool is demonstrated in our e-learning system called UbiLearn developed at the MCRLab of University of Ottawa.

O presente trabalho avaliou o uso de Tecnologias de Informação e Comunicação (TICs) com o objetivo de melhorar os processos de ensino e de aprendizagem em cursos de engenharia. Com base na Teoria da Aprendizagem Significativa, Teoria da Carga Cognitiva, Teoria da Autodeterminação e Análise de Estilos de Aprendizagem foi desenvolvido um modelo que utiliza diagramas esquemáticos que definem caminhos possíveis de serem trilhados e assim permitir identificar trajetórias de aprendizagem conceitual. Para cada conceito a ser aprendido existem várias trajetórias de aprendizagem possíveis que podem ser modeladas através do modelo MOTRAC proposto que é uma das principais contribuições da Tese. Para mostrar a viabilidade do modelo proposto (MOTRAC) foram modelados diferentes Objetos de Aprendizagem, desenvolvidos e aplicados em situações reais de aprendizagem. Foi também mantido um grupo de controle que assistiu aulas expositivas tradicionais do mesmo conteúdo. O desempenho dos estudantes que aprenderam com os Objetos de Aprendizagem foi significativamente melhor do que o desempenho do grupo de controle devido a: (i) o Modelo de Trajetórias de Aprendizagem Conceitual garante o principal aspecto da aprendizagem significativa que é o ancoramento de novos conceitos em conceitos previamente conhecidos, (ii) os Objetos de Aprendizagem possibilitam implementar em larga escala algumas práticas da tutoria individual como o ajuste de ritmo e uma intensa interatividade de respostas; (iii) os Objetos de Aprendizagem permitem implementar processos de aprendizagem por descoberta guiada; (iv) os Objetos de Aprendizagem permitem introduzir fatores de motivação extrínseca, como atribuição de pontos adicionais a estudantes que resolvam questões; (v) os Objetos de Aprendizagem podem ser utilizados a qualquer hora, em qualquer lugar, permitindo que o estudante escolha hora e lugar mais convenientes para a aprendizagem. Outro aspecto relevante apresentado diz respeito à necessidade de um número mínimo de estudantes para assegurar a viabilidade econômica do desenvolvimento de Objetos de Aprendizagem. A metodologia proposta aborda os processos de ensino e aprendizagem sob uma perspectiva de projeto, isto é, adota uma linguagem que facilita a sua adoção por parte de professores que estejam mais familiarizados com as práticas de projeto do que com as teorias de aprendizagem.
This work evaluates the use of Information and Communication Technology to improve learning and teaching processes in engineering courses. Based on Meaningful Learning Theory, Cognitive Load Theory, Auto-determination Theory and Learning Styles Analysis, a model that uses schematic diagrams to describe meaningful learning trajectories has been developed. These diagrams are used to project meaningful learning objects in a framework called Project Based on Learning Trajectories. Learning Objects produced with this framework have been used in experimental studies that compare the performance of students who have learned using Learning Objects against the performance of students who have learned in typical engineering classrooms. A comparative analysis of knowledge tests shows significant better performance of students who learned using Learning Objects due the following reasons: i) The proposed Model of Meaningful Learning Objects and Project Based on Learning Trajectories framework ensures a Learning Object development that presents propositions connecting new information to students’ previous knowledge, i. e. it assures the main factor of meaningful learning; ii) Learning Objects implement some common practices of one-to-one tutorial as the individual learning pace adjust and intense interactivity; iii) Learning Objects allow to implement a guided discover learning process; iv) Learning Objects allow to introduce extrinsic motivational factors, as the attribution of additional points to students who solve the proposed questions; v) Learning Objects may be executed anytime and anywhere, allowing the students to choose place and time more convenient than the scheduled classrooms. The findings of this work show evidences of the potential effectiveness of the proposed project framework. Other relevant aspect of developing Learning Objects presented refers to the need of a minimum number of users in order to assure economic viability. The proposed framework of Project Base on Learning Trajectories introduces an engineering approach to the learning and teaching processes. This new approach potentially brings to engineering a new perspective of using the project and design engineering skills to actuate improving learning and teaching processes.

Developing computer vision algorithms able to learn from unsegmented images containing multiple objects is important since this is how humans constantly learn from visual experiences. In this thesis we consider images containing views of multiple objects and our task is to learn about each of the objects present in the images. This task can be approached as a factorial learning problem, where each image is explained by instantiating a model for each of the objects present with the correct instantiation parameters. A major problem with learning a factorial model is that as the number of objects increases, there is a combinatorial explosion of the number of configurations that need to be considered. We develop a greedy algorithm to extract object models sequentially from the data by making use of a robust statistical method, thus avoiding the combinatorial explosion. When we have video data, we greatly speed up the greedy algorithm by carrying out approximate tracking of the multiple objects in the scene. This method is applied to raw image sequence data and extracts the objects one at a time. First, the (possibly moving) background is learned, and moving objects are found at later stages. The algorithm recursively updates an appearance model so that occlusion is taken into account, and matches this model to the frames through the sequence. We apply this method to learn multiple objects in image sequences as well as articulated parts of the human body. Additionally, we learn a distribution over parts undergoing full affine transformations that expresses the relative movements of the parts. The idea of fitting a model to data sequentially using robust statistics is quite general and it can be applied to other models. We describe a method for training mixture models by learning one component at a time and thus building the mixture model in a sequential manner. We do this by incorporating an outlier component into the mixture model which allows us to fit just one data cluster by "ignoring" the rest of the clusters. Once a model is fitted we remove from consideration all the data explained by this model and then repeat the operation. This algorithm can be used to provide a sensible initialization of the mixture components when we train a mixture model.

The focus of this thesis is the study of mobile learning, specifically learning in different locations and under various contextual situations, from the perspective of university students. I initially derived and designed a theoretical mobile context-aware learning schedule (mCALS) framework from an extensive literature review. Its objective is to recommend appropriate learning materials to students based on their current locations and circumstances. The framework uses a learning schedule (i.e. electronic-based diary) to inform the location and available time a student has for learning/studying at a particular location. Thereafter, a number of factors are taken into consideration for the recommendation of appropriate learning materials. These are the student’s learning styles, knowledge level, concentration level, frequency of interruption at that location and their available time for learning/studying. In order to determine the potential deployment of the framework as a mobile learning application by intended users, I carried out three types of feasibility studies. First, a pedagogical study was conducted using interviews to explore together with students (a) what their learning requirements were when studying in a mobile environment, (b) whether the framework could potentially be used effectively to support their studies and, (c) using this user-centred understanding, refined user requirements of the framework. Second, a diary study was conducted where I collected data and analysed the usability feasibility of the framework by (a) determining whether students could plan their daily schedule ahead and keep to it, (b) ascertaining which learning contexts were important and, (c) establishing which learning materials were appropriate under which situations. Two validation studies were conducted. The first one was an online experiment utilising Java learning objects. Participants of this study were suggested appropriate learning objects to study with, based on their amount of available time, current motivation level for learning and their proficiency level of Java. The second validation study was an investigation into high-quality Java learning objects available in the public domain. Finally, a technical design of the framework was carried out to determine whether the framework at present could realistically be implemented using current mobile technologies. The data analyses of the feasibility studies show that (a) a learning schedule approach is successful to an extent in obtaining location and available time information to indicate accurate values of these contexts, (b) different learners may require different personalisation strategies when selecting appropriate learning materials for them in mobile environments, and (c) the mCALS framework is particularly well-suited for self-regulated students. I also proposed a set of suggestion rules which can be used to recommend appropriate Java learning materials to students in different contexts. The validation studies show that 1) the proposed suggestion rules are effective in recommending appropriate materials to learners in their situation, in order to enhance their learning experiences, and 2) there are a sufficiently large number of high-quality LOs available in the public domain that can be incorporated for use within my framework. Finally, the development of mCALS has been considered from three perspectives – pedagogical, usability and technical. These perspectives consist of critical components that should be considered when developing and evaluating mobile learning software applications. The results demonstrated that the mCALS framework can potentially be used by students in different locations and situations, and appropriate learning materials can be selected to them, in order to enhance their learning experiences.

Este estudo trata da utilizaÃÃo dos Objetos de Aprendizagem (OA) em salas de aula, sendo objetivo do mesmo investigar se o uso de objetos de aprendizagem pode facilitar o desenvolvimento de conteÃdos em sala de aula, favorecendo ao processo ensino-aprendizagem. O estudo foi desenvolvido com uma turma de 1a sÃrie do ensino mÃdio da Escola Estadual de Ensino MÃdio Liceu de Messejana situada em Messejana, CearÃ. A turma à composta de 36 alunos, que foram divididos em dois grupos, separados de forma aleatÃria, por sorteio. Um grupo foi denominado grupo de controle, com 26 alunos e outro, denominado grupo experimental, com 10 alunos. Utilizamos para a coleta de dados trÃs instrumentos: questionÃrio socioeconÃmico, prÃ-teste e pÃs-teste. Os resultados apontam que o objetivo foi alcanÃado, pois percebemos um desempenho superior dos participantes do grupo experimental, com relaÃÃo aos participantes do grupo de controle. Portanto, os participantes que tiveram as aulas com o OA obtiveram um desempenho melhor.
This study deals with the use of Learning Object (LO) in classrooms, with the same aim to investigate whether the use of learning objects can facilitate the development of content in the classroom, facilitating the teaching-learning process. The study was conducted with a group of students from the first year of high school studying at a public school named Liceu de Messejana, located in Messejana, CearÃ. The class consists of 36 students, who were divided into two groups, separated randomly in a raffle. One group was called the control group, with 26 students and the other group, called the experimental group, with 10 students. The data was collected from three instruments: socioeconomic questionnaire, pre-test and post-test. The results show that the goal was achieved because we realize superior performance of the experimental group, compared with participants in the control group. Therefore, participants who had classes with LO had a better performance.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 2007.
Includes bibliographical references.
The effects of observed object motion on object perception are examined in two sets of studies. The first section of the thesis provides a thorough examination of various untested aspects of the basic "temporal association" hypothesis, which suggests that object motion provides a principled basis for linking distinct images together if they appear within small time intervals. Using familiar and unfamiliar objects undergoing various forms of non-rigid motion, I ask how well this simple hypothesis predicts behavior in change detection and categorization tasks. The results favor a modified version of the hypothesis which operates over a population of units, such that increases in generalization also produce increases in image sensitivity. The observed effects of long-term knowledge concerning object appearance and expected patterns of motion also force additional modifications of the initial hypothesis to incorporate interactions between learned predictions and recent experience. Specifically, the tendency to alter patterns of generalization following dynamic exposure appears to be contingent on the stability of the direction of movement through appearance space.
(cont.) Consistent with this expanded model, performance in our categorization task appears to depend heavily on whether or not a coherent direction of movement through appearance space can be determined across both categories to be learned. In the second section of the thesis, I report the results of two parametric analyses of image encoding following dynamic exposure. In each case, I ask how the movement of an object up to the presentation of particular image affects an observers' ability to accurately recall that image. Novel, rigidly rotating objects are used in both cases to characterize the influence of appearance dynamics on short and long-term image encoding. In both cases, I find that local appearance change over time exerts a powerful influence on encoding, suggesting that both immediate percepts and visual memory are modulated by the recent past. The result is a complex picture of dynamic object perception that goes far beyond the basic principle of object motion as a tool for learning invariant recognition.
by Benjamin J. Balas.
Ph.D.

Mestrado em Comunicação Multimédia
O presente estudo visa resolver questões de visualização e apresentação de conteúdos e da interacção de um portal direccionado para a gestão e visualização de Learning Objects, em contexto de formação profissional. Integrado num projecto da PT Inovação já a decorrer - o PoLO - a conceptualização e desenvolvimento de interface assentará já sobre uma estrutura de navegação e de conteúdos parcialmente definida. Neste âmbito, o resultado final concreto deverá ser uma proposta de interface passível de ser implementada, sendo o enfoque dado ao perfil de formando. Seguindo uma metodologia de Investigação de Desenvolvimento, começou por se fazer a revisão de literatura de modo a construir uma sólida base teórica. Seguidamente estruturou-se a informação e o modo como funcionalidades e conteúdos estariam relacionados. As várias propostas de interface foram sendo criadas e discutidas com a empresa. Estando definidas as principais características da interface passou-se ao aperfeiçoamento dos gráficos (ícones e botões) e ao desenvolvimento das várias páginas e funcionalidades presentes ao longo da navegação. Não tendo sido implementada a versão final do portal, desenvolveu-se um protótipo funcional com o intuito de simular o aspecto gráfico e comportamentos interactivos projectados. Com o protótipo desenvolvido foram aplicados testes de usabilidade e breves entrevistas a potenciais utilizadores do PoLO. Os dados recolhidos permitiram perceber quais os pontos fortes e fracos da solução apresentada, deixando pistas e sugestões para melhorias e desenvolvimentos futuros. Apesar dos pontos críticos detectados, a interface proposta foi aceite de forma muito positiva pelos utilizadores, deixando assim o caminho aberto para que numa futura implementação do PoLO a solução de interface seja efectivamente bem sucedida.
This study aims to address issues of visualization and presentation of content and interaction of a portal directed at the management and visualization of Learning objects, in the context of professional training. Integrated in a project of PT Inovação already underway - the PoLO project - the conceptualization and development of interface will be based on already partially defined structure of navigation and content. In this context, the end result should be a concrete interface proposal that can be implemented, with the focus given to the learner profile. Following a methodology for Research Development, some literature was reviewed in order to build a solid theoretical base. Then the information was structured, defining how features and content were to be related. The various proposals for interface were created and discussed with the company. Being defined the main characteristics of the interface, the graphics (icons and buttons) were improved and the different pages and features present along the navigation were developed. Not been implemented the final version of the portal, it was developed a working prototype in order to simulate the visual appearance and interactive behaviors designed. With this prototype, usability tests and brief interviews were conduced with potential users of the PoLO. The data collected allowed to understand the strengths and weaknesses of the presented solution, leaving clues and suggestions for improvements and future developments. Despite the critical points, the interface was accepted very positively by users, thus leaving the way open for a future implementation of PoLO in which the interface solution is actually successful.

Objetos de Aprendizagem são recursos relevantes para ensejar a aprendizagem em função de seu caráter modular, pois permitem oferecer condições para uma atividade apoiada por recursos alocados por demanda face a necessidades específicas de uma turma ou aluno em particular. Essa característica é um elemento benéfico e facilitador, mas adiciona um aspecto que dificulta seu uso, derivado da dificuldade de localização do objeto de aprendizagem apropriado para necessidades específicas. Em decorrência, existe considerável esforço de investigação de estratégias para facilitar a localização e o acesso a Objetos de Aprendizagem (OA), adequados às necessidades de alunos e professores, pois a dificuldade em realizar esses processos ocasiona uma redução na frequência de utilização e reutilização dos mesmos em atividades pedagógicas. O local usual para armazenamento desses recursos é um Repositório de Objetos de Aprendizagem (ROA). No entanto, o uso dos objetos de aprendizagem ocorre mais frequentemente no contexto de Ambientes Virtuais de Aprendizagem (AVAs). Face a essas constatações, esse trabalho de pesquisa investigou estratégias para integrar um ROA a um AVA e como esta integração pode apoiar o professor e o aluno na construção e adaptação do plano de ensino e aprendizagem. Essa tese apresenta um referencial teórico sobre Repositórios de Objetos de Aprendizagem e Ambientes Virtuais de Aprendizagem e algumas soluções implementadas para integrar esses sistemas. Além disso, propõe um modelo de integração de repositórios a AVAs e descreve sua implementação bem como resultados derivados do uso da solução integradora construída. Os sistemas escolhidos para validar o modelo implementado foram o repositório CESTA2 e o AVA MOODLE. A hipótese principal é a de que a integração dos sistemas permite que os OA possam ser mais facilmente agregados pelo professor ao AVA no planejamento das unidades de aprendizagem de uma disciplina ou curso e permite que esses recursos sejam recomendados pelo professor ao aluno nas diversas situações de aprendizagem previamente delineadas em cada uma das unidades de aprendizagem ou que surgirem sob demanda ao longo do curso. Nesse sentido a solução proposta visa criar condições para que os OAs possam ser pesquisados e utilizados pelos alunos através de ferramentas disponíveis no próprio AVA. Para validar a integração foram realizados estudos de caso com alunos e professores de duas turmas da disciplina de Projeto de Banco de Dados Relacional do Instituto Federal Sul-rio-grandense, Campus Pelotas. Diferentemente de outros estudos realizados sobre o tema e relatados nessa tese, esse trabalho tem como foco a integração dos ambientes, com a finalidade tecnológica de verificar a interoperabilidade existente entre eles e como finalidade pedagógica verificar como essa integração pode auxiliar professores e alunos. A avaliação e validação realizadas através de estudo de caso com professores e alunos evidenciou que o modelo proposto e implementado atende às expectativas e pode apoiar professores e alunos, nas suas atividades pedagógicas e de aprendizagem, respectivamente. Além disso, o trabalho fruto dessa tese aporta contribuições importantes para as instituições de ensino que utilizam o software livre DSpace como repositório institucional e o MOODLE como AVA Institucional, pois demonstra como esses dois sistemas podem ser integrados para permitir aos seus usuários auferir todos os benefícios que essa integração pode oferecer.
Learning Objects are important resources to give opportunity to the learning according to their modular character, this way they may offer conditions to an activity supported by allocated resources by requests to the specific needs of a class or of an individual student. This characteristic is a beneficial and facilitator element, but it adds an aspect which makes its use difficult due to the learning object’s location difficulty appropriated to the specific needs. As a result, there is significant research effort of strategies in order to facilitate the location and the access to the Learning Objects (LO) adequate to the students and teachers’ needs, because the difficulty in order to perform these processes causes a decrease in the frequency of use and reuse from these ones in activities of teaching and learning. The usual place to store these resources is a Learning Objects Repository (LOR). However, the use of learning objects occurs more frequently in the Virtual Learning Environments (VLEs). Due to these findings, this study researched the strategies to integrate a Learning Objects Repository to a Virtual Learning Environment and how this integration may support the teacher and the student at the construction and adaptation of teaching and learning plan. The present thesis shows a theoretical frame about the Learning Objects Repository and Virtual Learning Environments and some solutions taken to integrate these systems. In addition, it proposes a integration model of repositories to VLEs and it describes its implementation as well as the results from the use of the integrating solution built. The systems chosen to validate the deployed model were the repository CESTA2 and the VLE MOODLE. The main hypothesis is that the integration of the systems allows that the Learning Objects may be more easily added by the teacher to the VLE at the learning unit planning to a subject our course and it allows that these resources be advised by the teacher to the student in several learning situations previously outlined in each learning unit or the ones which arises along the course. At this point of view the solution proposed aims to create conditions so that the LOs may be researched and used by the students through available tools at own VLE. In order to validate the integration were done case studies with students and teachers from two classes of Relational Database Project subject from the Instituto Federal Sul-rio-grandense, Pelotas Campus. Differently from other studies done about the topic and described at this thesis, this study focuses on the environment integration with the technological purpose in order to verify the interoperability presented between them and as teaching purpose to verify how this integration may help teachers and students. The assessment and validation performed through case study with teachers and students showed that the proposed and evidenced model meets the expectations and may support teachers and students in their teaching and learning activities, respectively. Furthermore, the study from this thesis brings important contributions to the educational institutions that use the DSpace free software as institutional repository and the MOODLE as Institutional VLE, showing how these two systems may be integrated allowing to their users all the benefits that this integration may offer.

Mokymosi objektų informacinė sistema yra internetu paremta programa, kuri gali būti naudojama mokymosi objektų saugojimui, ieškojimui, skirstymui. Šios sistemos veikia kaip mokymosi objektų saugyklos, kurios tiekia nuorodas į puslapius su mokymosi medžiaga, kuri atitinka paieškos kriterijus. Pagrindiniai mokymosi objektų informacinės sistemos naudotojai yra studentai, moksleiviai, dėstytojai, mokytojai ir pan., kurių poreikiams patenkinti sukurta sistema su efektyvia, lengva ir paprasta mokymosi objektų paieška, jų panaudojimu, naujų mokymosi objektų įkėlimu, mokymosi objektų dalinimosi galimybe su kitais vartotojais. Mokymosi objektų informacinė sistema įgyvendina svarbiausias mokymosi objektų charakteristikas: pasiekiamumas, operacinis suderinamumas, pritaikomumas, pakartotinis naudojimas, ilgaamžiškumas. Informacinės sistemos veikimui naudojamos technologijos – HTTP serveris (Apache), taip pat duomenų bazės serveris (MySQL). Šios sistemos naudojimui ir veikimui reikalingas internetinis ryšys. Sukurta mokymosi objektų informacinė sistema – tai mokymosi objektų saugykla, pateikianti struktūrizuotą ir organizuotą informaciją visiems, kurie studijuoja, mokosi ir domisi mokslu.
Learning object information system is a Web application based system, which can be used as a learning object repository, search engine, or a learning object distributor. These programs work like learning object repositories which provides links to the web sites with learning material. Other repositories store learning objects in their own database. A learning object is a resource, usually digital and web-based, that can be used and re-used. The main users of learning object system are students, teachers, instructors and so on. This system was designed with an easy learning object search system, with their reusability, new learning objects creation and distribution with other users. The system is built using these technologies: PHP programming language, MySQL database system, Apache web server, HTML (hypertext markup language). This system works with internet connection. This learning objects information system is a learning objects repository which produces structured and organized information for those who are studying, learning and interested in education.

Our perception is finite limited by both sensory capacity and the ability to interpret stimulation. Therefore, in order that an environment may be best understood there is a need to be selective in how resources, both computational and sensory, are deployed; this selective mechanism is often termed attention. In this thesis a model of visual attention is described which may be specialised to detect targets of interest. Specifically, we explore how a target's signal may be differentiated from other stimulation in a visual input through developing knowledge of its expected appearance with respect to a basic set of features. A key question which we address is: how the separate sources of information provided by the processing of each feature type may be best be combined to provide a map of target saliency across a scene. This saliency index is then used to guide the deployment of attention. Results show an improvement in targeting efficiency of nearly 50% when compared to a similar model of attention which does not exploit target specific information. Further, we also report favourable results when comparing the performance of our approach to other detection strategies reported within the computer vision literature.

Multi robot cooperative transportation is an important research area in the multi robot domain. In the process of object transportation, several autonomous robots navigate cooperatively in either a static or a dynamic environment to transport an object to a goal location and orientation. The environment may consist of both fixed and removable obstacles and it will be subject to uncertainty and unforeseen changes within the environment. Furthermore, more than one robot may be required in a cooperative mode for handling heavy and large objects. These are some of the challenges addressed in the present thesis. This thesis develops a machine learning approach and investigates relevant research issues for object transportation utilizing cooperative and autonomous multiple mobile robots. It makes significant original contributions in distributed multi robot coordination and self deterministic learning for robot decision making, and comes up with an optimal solution to the action selection conflicts of the robots in the cooperative system. This will help to improve the real time performance and robustness of the system. Also, the thesis develops a new method for object and obstacle identification in complex environments using a laser range finder, which is more realistic than the available methods. A new algorithm for object pose estimation algorithm is developed, enabling a robot to identify the objects and obstacles in a multi-robot environment by utilizing the laser range finder and color blob tracking. The thesis develops a fully distributed hierarchical multi-robot architecture for enhanced coordination among robots in a dynamic and unknown environment. It strives to improve the real time performance and robustness. The system consists with three layers. By combining two popular artificial intelligence (Al) techniques such as learning and behavior based decision making, the developed architecture is expected to facilitate effective autonomous operation of cooperative multi-robot systems in a dynamically changing, unstructured, and unknown environment.

ELETROBRAS - CENTRAIS ELÉTRICAS BRASILEIRAS S. A.
Este trabalho apresenta uma proposta para utilizar a tecnologia de banco de dados para o armazenamento e a gerência de objetos de aprendizado em uma federação de banco de dados (banco de dados distribuído). A crescente evolução no uso de aprendizado eletrônico trouxe o foco para a produtividade na elaboração e gerência do conteúdo dos módulos educacionais. Este conteúdo compreende hoje de vídeos, áudios e de outros dados relacionados, além de textos. Este material é normalmente armazenado pelos instrutores sem maiores preocupações quanto ao compartilhamento. Como membro do projeto PGL (Partnership in Global Learning) - uma organização virtual voltada para pesquisa, desenvolvimento e disseminação do aprendizado através de novas tecnologias - o laboratório de banco de dados da PUC-Rio - TecBD, tem pesquisado a adoção do enfoque de banco de dados para a gerência de objetos de aprendizado (Learning Objects) armazenados em locais interligados formando um ambiente de banco de dados heterogêneos distribuído. Este trabalho visa: 1) utilizando produtos de BD comercialmente disponíveis; 2) adotando os atuais padrões existentes para definição de objetos de aprendizado; 3) considerando objetos de aprendizado armazenados em locais separados e autônomos; 4) desenvolver uma aplicação (protótipo) com esses objetos de aprendizado. O modelo de dados adotado estabelece uma estrutura de objetos de aprendizado compostos, via relacionamentos com elementos atômicos e também com elementos compostos. Diferentes abordagens como, por exemplo, Web Services, Java/Servlets e Web Application Servers, foram estudadas para o problema da autonomia e distribuição geográfica. Um protótipo foi construído utilizando o produto IBM DB2 com seus recursos suplementares tais como extensores para dados de áudio, vídeo, imagens, XML e suporte para gerenciamento federado. A exploração dos dados armazenados, via navegador (browser), foi realizada utilizando a camada IBM Net.Data que embora não obrigatória, permitiu realizar a tarefa de um modo simples e disponibilizou uma solução bem integrada com o IBM DB2 e seus complementos.
This work presents a proposal to utilize database technology for storing and managing learning objects in a database federation (distributed database). The evolution of e-learning has brought the focus over the productivity to make and to manage the content of learning modules, which today comprises videos, audio, among other related data, besides the text data. Instructors normally store this material without worry about sharing. As a member of PGL - Partnership in Global Learning - a virtual organization for research, development and dissemination of learning through new technologies - TecBD - the PUC-Rio`s Database Laboratory is researching the use of database approach for managing learning objects stored on interconnected sites composing a heterogeneous distributed database environment. This work intends: 1) using market ready DB products; 2) adopting the actual standards for defining of learning objects; 3) considering learning objects stored on separated and autonomous sites; 4) to develop an application (a study case) with these learning objects. The learning object`s model establishes a structure for composing learning objects by linking atomic elements and also linking composed elements. Different approaches as Web Services, Java/Servlets and Web Application Servers were considered for the geographically distributed problem. A study case was build using the product IBM DB2 with the provided extenders for audio, video, image, XML data and the Federated System Support. The web browser`s explore of the stored data was build using the IBM Net.Data software. Although not exclusive, it provided an easy way to perform this task and also enabled an easy integration with IBM DB2 and its extenders.

Learning objects have received attention because of their potential to improve current methods of teaching and learning. Despite perceived importance, there has been lack of studies that examine the use of learning objects across various contexts. In response, this study examined the actual use of learning objects and their perceptions among Instructional Design and Technology (IDT) practitioners in U.S. This study was based on the data from 191 IDT practitioners from various sectors, including higher education, business, government, and K-12. The study results contribute to the body of knowledge on learning objects in terms of four areas: conceptualization, utilization, benefits, and adoption. First, the study results should allay the concern that the conceptualization of learning objects focuses too much on technical aspects. IDT practitioners emphasized both technical and learning aspects in conceptualizing learning objects. In conclusion, the present study laid the foundation for a working definition of learning objects. Second, the study showed that IDT practitioners were utilizing learning objects to empower learners to have control over their own learning. The current study identified design strategies that IDT practitioners were frequently using. The most frequently utilized strategy was the provision of concrete, authentic examples and problems. Along with design strategies, this study identified the frequently adopted granularity levels: assets, combined media, one complete instructional unit, lesson or module, and course. Combined media, which consists of content and optional media, was the most frequently utilized granularity level of the five levels. The study provided empirical data to help determine formal design strategies and optimal granularity levels in utilizing learning objects based on the frequent use among IDT practitioners. Third, this study filled the research gap on the benefits of learning objects from an IDT perspective. Study results showed that IDT practitioners were positive about the overall benefits for the ID process, along with reusability and the support for the motivation and interactivity features. However, they were less convinced of the time and cost savings. Generally, IDT practitioners were satisfied with the benefits of learning objects. Fourth, the present study improved the understanding of the adoption status of learning objects. Currently, many of the participantsâ organizations were adopting or exploring the implementation of learning objects. The adoption of learning objects has a promising future, as IDT practitioners were positive about their organizationsâ intentions to use learning objects in the future. At the individual level, IDT practitioners were found to use learning objects for their professional role and across various projects. As an adoption factor, IDT practitioners showed the positive reactions to learning objectsâ perceived usefulness.
Ph. D.

Cette thèse traite des problèmes de modélisation, reconnaissance, localisation et utilisation du contexte pour la manipulation d'objets par un robot. Le processus de modélisation se divise en quatre composantes : le système réel, les données capteurs, les propriétés à reproduire et le modèle. En spécifiant chacune des ces composantes, il est possible de définir un processus de modélisation adapté au problème présent, la manipulation d'objets par un robot. Cette analyse mène à l'adoption des descripteurs de texture locaux pour la modélisation. La modélisation basée sur des descripteurs de texture locaux a été abordé dans de nombreux travaux traitant de structure par le mouvement (SfM) ou de cartographie et localisation simultanée (SLAM). Les méthodes existantes incluent Bundler, Roboearth et 123DCatch. Pourtant, aucune de ces méthodes n'a recueilli le consensus. En effet, l'implémentation d'une approche similaire montre que ces outils sont difficiles d'utilisation même pour des utilisateurs experts et qu'ils produisent des modèles d'une haute complexité. Cette complexité est utile pour fournir un modèle robuste aux variations de point de vue. Il existe deux façons pour un modèle d'être robuste : avec le paradigme des vues multiple ou celui des descripteurs forts. Dans le paradigme des vues multiples, le modèle est construit à partir d'un grand nombre de points de vue de l'objet. Le paradigme des descripteurs forts compte sur des descripteurs résistants aux changements de points de vue. Les expériences réalisées montrent que des descripteurs forts permettent d'utiliser un faible nombre de vues, ce qui résulte en un modèle simple. Ces modèles simples n'incluent pas tout les point de vus existants mais les angles morts peuvent être compensés par le fait que le robot est mobile et peut adopter plusieurs points de vue. En se basant sur des modèles simples, il est possible de définir des méthodes de modélisation basées sur des images seules, qui peuvent être récupérées depuis Internet. A titre d'illustration, à partir d'un nom de produit, il est possible de récupérer des manières totalement automatiques des images depuis des magasins en ligne et de modéliser puis localiser les objets désirés. Même avec une modélisation plus simple, dans des cas réel ou de nombreux objets doivent être pris en compte, il se pose des problèmes de stockage et traitement d'une telle masse de données. Cela se décompose en un problème de complexité, il faut traiter de nombreux modèles rapidement, et un problème d'ambiguïté, des modèles peuvent se ressembler. L'impact de ces deux problèmes peut être réduit en utilisant l'information contextuelle. Le contexte est toute information non issue des l'objet lui même et qui aide a la reconnaissance. Ici deux types de contexte sont abordés : le lieu et les objets environnants. Certains objets se trouvent dans certains endroits particuliers. En connaissant ces liens lieu/objet, il est possible de réduire la liste des objets candidats pouvant apparaître dans un lieu donné. Par ailleurs l'apprentissage du lien lieu/objet peut être fait automatiquement par un robot en modélisant puis explorant un environnement. L'information appris peut alors être fusionnée avec l'information visuelle courante pour améliorer la reconnaissance. Dans les cas des objets environnants, un objet peut souvent apparaître au cotés d'autres objets, par exemple une souris et un clavier. En connaissant la fréquence d'apparition d'un objet avec d'autres objets, il est possible de réduire la liste des candidats lors de la reconnaissance. L'utilisation d'un Réseau de Markov Logique est particulièrement adaptée à la fusion de ce type de données. Cette thèse montre la synergie de la robotique et du contexte pour la modélisation, reconnaissance et localisation d'objets
This Thesis addresses the modeling, recognition, localization and use of context for objects manipulation by a robot. We start by presenting the modeling process and its components: the real system, the sensors' data, the properties to reproduce and the model. We show how, by specifying each of them, one can define a modeling process adapted to the problem at hand, namely object manipulation by a robot. This analysis leads us to the adoption of local textured descriptors for object modeling. Modeling with local textured descriptors is not a new concept, it is the subject of many Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) works. Existing methods include bundler, roboearth modeler and 123DCatch. Still, no method has gained widespread adoption. By implementing a similar approach, we show that they are hard to use even for expert users and produce highly complex models. Such complex techniques are necessary to guaranty the robustness of the model to view point change. There are two ways to handle the problem: the multiple views paradigm and the robust features paradigm. The multiple views paradigm advocate in favor of using a large number of views of the object. The robust feature paradigm relies on robust features able to resist large view point changes. We present a set of experiments to provide an insight into the right balance between both. By varying the number of views and using different features we show that small and fast models can provide robustness to view point changes up to bounded blind spots which can be handled by robotic means. We propose four different methods to build simple models from images only, with as little a priori information as possible. The first one applies to planar or piecewise planar objects and relies on homographies for localization. The second approach is applicable to objects with simple geometry, such as cylinders or spheres, but requires many measures on the object. The third method requires the use of a calibrated 3D sensor but no additional information. The fourth technique doesn't need a priori information at all. We apply this last method to autonomous grocery objects modeling. From images automatically retrieved from a grocery store website, we build a model which allows recognition and localization for tracking. Even using light models, real situations ask for numerous object models to be stored and processed. This poses the problems of complexity, processing multiple models quickly, and ambiguity, distinguishing similar objects. We propose to solve both problems by using contextual information. Contextual information is any information helping the recognition which is not directly provided by sensors. We focus on two contextual cues: the place and the surrounding objects. Some objects are mainly found in some particular places. By knowing the current place, one can restrict the number of possible identities for a given object. We propose a method to autonomously explore a previously labeled environment and establish a correspondence between objects and places. Then this information can be used in a cascade combining simple visual descriptors and context. This experiment shows that, for some objects, recognition can be achieved with as few as two simple features and the location as context. The objects surrounding a given object can also be used as context. Objects like a keyboard, a mouse and a monitor are often close together. We use qualitative spatial descriptors to describe the position of objects with respect to their neighbors. Using a Markov Logic Network, we learn patterns in objects disposition. This information can then be used to recognize an object when surrounding objects are already identified. This Thesis stresses the good match between robotics, context and objects recognition

Qualitative learning material (or Learning Objects – LOs) is one of the main factors of learning quality. Therefore, evaluation of LOs quality is one of the most relevant education problems. The problem is relevant for all participants of the educational sector – for educational institutions (e.g. schools) that have to select qualitative learning material for their needs, for education policy makers who need clear quality criteria while implementing LOs tenders, for authors of learning material (e.g. publishers) who need to know quality requirements to create LOs etc. The research work is aimed to propose and pilot LOs reusability and quality evaluation methodology, i.e., quality model, and simple and effective expert evaluation methods, thus improving solution of educational tasks using informatics engineering methods. To reach this aim, we have to analyse the notions of LOs reusability and expert evaluation of quality, principles of creating LOs reusability and quality model, and possible simple and effective methods for the expert evaluation of LOs quality and reusability. Both LO quality and reusability model and evaluation method are presented in the work. LO quality model created consists of 9 quality criteria divided into 3 groups i.e. technological, pedagogical, and IPR criteria. This model is comprehensive and matches scientific principles of creating a model. The following methods are selected and consecutively applied in the research while evaluating LOs quality... [to full text]
Kokybiška mokomoji medžiaga yra viena svarbiausių mokymo(-si) kokybės veiksnių, todėl mokomųjų objektų (toliau – MO) daugkartinio panaudojamumo kokybės vertinimas yra viena opiausių švietimo problemų. Problema yra aktuali visiems švietimo dalyviams – švietimo įstaigoms (pvz., mokykloms), kurios turi išrinkti kokybišką mokomąją medžiagą (MO) savo tikslams pasiekti, švietimo politikams, kuriems reikia aiškių kokybės kriterijų vykdant MO viešuosius pirkimus, mokomosios medžiagos autoriams (pvz., leidykloms), kurie turi žinoti kokybės reikalavimus, remdamiesi kuriais jie kurs MO, ir pan. Disertacinis darbas skirtas pasiūlyti ir išbandyti MO daugkartinio panaudojamumo kokybės vertinimo metodiką: kokybės modelį ir paprastus bei efektyvius ekspertinio kokybės vertinimo metodus (t.y., pagerinti edukologinių uždavinių sprendimo galimybes naudojant informatikos inžinerijos metodus). Tam analizuojamos MO daugkartinio panaudojamumo ir ekspertinio kokybės vertinimo sąvokos, kokybės modelio sudarymo principai, galimi paprasti ir efektyvūs kokybės ekspertinio vertinimo metodai. Darbe yra pateiktas sukurtas mokomųjų objektų daugkartinio panaudojamumo kokybės modelis ir vertinimo metodas. Mokomųjų objektų kokybės modelį sudaro devyni trijų grupių (technologiniai, pedagoginiai, intelektinių teisių) kokybės kriterijai, kurie atspindi visapusišką kokybės kriterijų sistemą, kurioje yra svarbūs ne tik patys kriterijai, bet ir jų tarpusavio sąryšiai. Mokomųjų objektų daugkartinio panaudojamumo... [toliau žr. visą tekstą]

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
Partindo da experiência e de estudos anteriores a respeito do processo educacional das pessoas com autismo, pensou-se em como o uso do computador poderia auxiliar o processo de ensino e aprendizagem dessas pessoas, tendo como recurso um Objeto de Aprendizagem - OA produzido para o trabalho com esse público com objetivo de viabilizar mais um recurso que possa trabalhar a sua aprendizagem por meio do conhecimento de mundo. Portanto, esta pesquisa analisa se a construção e implementação de um OA para as pessoas com autismo pode favorecer o seu processo educacional. Para isso, foi realizado um extenso estudo a esse respeito, levando em consideração suas possíveis dificuldades e interesses, além das peculiaridades que envolvem a síndrome para a construção e implementação desse recurso para o trabalho educacional com essas pessoas. Adotou-se um enfoque qualitativo, com o intuito de obter informações em diversos segmentos a respeito do autismo, do processo de ensino aprendizagem e do uso computador com essas pessoas para que a construção do OA pudesse ser respaldada de informações condizentes com a proposta apresentada. Para a fase final da pesquisa foi selecionado dois participantes com autismo, matriculados no ensino regular para a utilização do OA em quatro encontros para uma aplicação “teste”, com o intuito de verificar por meio da observação as possibilidades e limitações desse recurso na aprendizagem dessas pessoas. Como recursos para coleta de...
Building on experience and previous studies regarding the people educational process with autism, it was thought on how computer could help the autism people learn with it, and with the use of a Learning Object - OA produced specifically for this audience with purpose of alphabetizing them by the knowledge of the world. Therefore, this research examines the construction and implementation of an OA specifically for people with autism can help the process of teaching and learning. For this we have conducted an extensive study about people with autism, taking into account the peculiarities surrounding the syndrome to build this feature in educational work with these people. We adopted a qualitative approach in order to obtain information about different segments of autism, the teaching and learning process of using computer with these people for the construction of OA could be backed up consistent information with the... (Complete abstract click electronic access below)

The last decade has seen the scope of digital library usage extend from data warehousing and other common library services to building quality collections of electronic resources and providing web-based information retrieval mechanisms for distributed learning. This is clear from the number of ongoing research initiatives aiming to provide dynamic learning environments. A major task in providing learning environments is to define a resource model (learning object). The flexibility of the learning object model determines the quality of the learning environment. Further, dynamic environments can be realized by changing the contents and structure of the learning object, i.e. make it mutable. Most existing models are immutable after creation and require the library to support operations that help in creating these environments. This leaves the learning object at the mercy of the parent library's functionality. This thesis work is an extension of an existing model and allows a learning object to function independent of the operational constraints of a digital library by equipping learning objects with software components called methods that influence their operation and structure even after being deployed. It provides a reference implementation of an aggregate, intelligent, self-sufficient, object-oriented, platform-independent learning object model, which is conformant to popular digital library standards. It also presents a Java-based development tool for creating and modifying smart objects. It is capable of performing content aggregation, metadata harvesting and user repository maintenance operations, in addition to supporting the addition/removal of methods to a smart object. The current smart object implementation and the development tool have been deployed successfully on two platforms (Windows and Linux) where their operation was found to be satisfactory.
Master of Science

There is a serious lack of conceptual clarity in the definitions and uses of learning objects, resulting in design and usability problems in current e-learning systems. The paper proposes ReLOMS, prototype reusable learning objects management system, being implemented to address the problem of usability and reusability of learning objects in e-learning systems.

We present the results of an implemented system for learning structural prototypes from grey-scale images. We show how to divide an object into subparts and how to encode the properties of these subparts and the relations between them. We discuss the importance of hierarchy and grouping in representing objects and show how a notion of visual similarities can be embedded in the description language. Finally we exhibit a learning algorithm that forms class models from the descriptions produced and uses these models to recognize new members of the class.

Recent advances in camera technology have made it possible to build a comfortable, wearable system which can capture the scene in front of the user throughout the day. Products based on this technology, such as GoPro and Google Glass, have generated substantial interest. In this thesis, I present my work on egocentric vision, which leverages wearable camera technology and provides a new line of attack on classical computer vision problems such as object categorization and activity recognition. The dominant paradigm for object and activity recognition over the last decade has been based on using the web. In this paradigm, in order to learn a model for an object category like coffee jar, various images of that object type are fetched from the web (e.g. through Google image search), features are extracted and then classifiers are learned. This paradigm has led to great advances in the field and has produced state-of-the-art results for object recognition. However, it has two main shortcomings: a) objects on the web appear in isolation and they miss the context of daily usage; and b) web data does not represent what we see every day. In this thesis, I demonstrate that egocentric vision can address these limitations as an alternative paradigm. I will demonstrate that contextual cues and the actions of a user can be exploited in an egocentric vision system to learn models of objects under very weak supervision. In addition, I will show that measurements of a subject's gaze during object manipulation tasks can provide novel feature representations to support activity recognition. Moving beyond surface-level categorization, I will showcase a method for automatically discovering object state changes during actions, and an approach to building descriptive models of social interactions between groups of individuals. These new capabilities for egocentric video analysis will enable new applications in life logging, elder care, human-robot interaction, developmental screening, augmented reality and social media.

Mobility is an element that is highly related to the development of society and the quality of individual life. Through mass of automobile production and traffic infrastructure construction, advanced countries have reached a high degree of individual mobility. In order to increase the efficiency, convenience and safety of mobility, advanced traffic infrastructure construction, transportation systems and automobiles should be developed. Among all the systems for modern automobiles, cameras based assistance systems are one of the most important components. Recently, with the development of driver assistance systems and autonomous cars, detection and recognition of traffic scene objects based on computer vision become more and more indispensable. On the other hand, the deep learning methods, in particular convolutional neural networks have achieved excellent performance in a variety of computer vision tasks. This thesis mainly presents the contributions to the computer vision and deep learning methods for traffic scene objects detection and recognition. The first approach develops numbers of methods for traffic sign detection and recognition. For traffic sign detection, template matching is applied with new features extended from chain code. Moreover, the region based convolutional neural networks are applied for detecting traffic signs painted on road surface. For traffic sign recognition, convolutional neural networks with a variety of architectures are trained with different training algorithms. The second approach focuses on the detection related to traffic text. A novel license plate detection framework is developed that is able to improve detection performance by simultaneously completing detection and segmentation. Due to the larger number and complex layout of Chinese characters, Chinese traffic text detection faces more challenges than English text detection. Therefore, Chinese traffic texts are detected by applying convolutional neural networks and directed acyclic graph. The final approach develops a method for pedestrian attribute classification. Generally, there are irrelevant elements included in features of convolutional neural networks. In order to improve classification performance, a novel feature selection algorithm is developed to refine features of convolutional neural networks.

Learning objects (LO) reuse is one topical area in instructional design that is gaining popularity in the education economy. It hinges on high hopes and promises to transform how learning occurs in the information age. This study attempted to identify and interrogate the core characteristics of reusable learning objects and conceptualize them as innovations in the curriculum development process. The goal was to synthesize existing knowledge on learning objects, weave streams of literature and research to focus on core arising issues, and then develop an instructional design tool that can help learners easily and effectively find reusable learning objects. The learning objects could be categorized and deconstructed to the levels of their instructional design transformations with regard to macro and micro-level reusability. The researcher used combinatorial developmental research with integrative literature review methodologies to design and develop a metadata management tool. This study involved an in depth review of literature on learning objects, reusable learning objects and their associated metadata management schemes through the integrative literature review approach. Results and data from the integrative literature review were then utilized to design and develop a tool addressing meta-tagging schemes, metadata management, search, and access of learning objects. The researcher identified characteristics of learning objects within the reuse process and discussed best practices, reuse procedures and modeling, based on the analysis of existing cases such as the Open-Knowledge-Initiative (OKI) projects to aid in the tool development. Integrative analysis running concurrently with the development process allowed for rigorous identification and alignment of key factors in the learning objects reuse universe. If fully developed, the metadata management tool could contribute to effective metadata management for learning objects often reused by learning designers, deliverers, and consumers.
Doctor of Philosophy
Learning Objects (LO) reuse is gaining popularity in the field of instructional design. This is because it could transform how learning occurs in today's information age. In this study, I outlined the important characteristics of reusable learning objects and set them up as creative and re-creative products in the curriculum development process. My goal was to combine and reproduce existing literature on LOs that would allow me to develop an instructional design tool to help learning content designers, deliverers, and consumers to easily tag, search, then find reusable learning objects. I reviewed literature on learning objects, reusable learning objects and their associated metadata management schemes then used this data to design and develop the tool addressing meta tagging schemes, metadata management, search, and accessibility of learning objects. The tool allows LO categorization and deconstruction to the largest and smallest granular levels of their instructional reusability. I combined a developmental research method with an integrative literature review method to design and develop the prototype of a tool known as metadata management tool (mmt) for reusable learning objects. If successful, the metadata management tool developed could contribute to an effective metadata management for learning objects often reused by learning designers, deliverers, and consumers.

The rise of deep learning has facilitated remarkable progress in video understanding. This thesis addresses three important tasks of video understanding: video object detection, joint object and action detection, and spatio-temporal action localization. Object class detection is one of the most important challenges in computer vision. Object detectors are usually trained on bounding-boxes from still images. Recently, video has been used as an alternative source of data. Yet, training an object detector on one domain (either still images or videos) and testing on the other one results in a significant performance gap compared to training and testing on the same domain. In the first part of this thesis, we examine the reasons behind this performance gap. We define and evaluate several domain shift factors: spatial location accuracy, appearance diversity, image quality, aspect distribution, and object size and camera framing. We examine the impact of these factors by comparing the detection performance before and after cancelling them out. The results show that all five factors affect the performance of the detectors and their combined effect explains the performance gap. While most existing approaches for detection in videos focus on objects or human actions separately, in the second part of this thesis we aim at detecting non-human centric actions, i.e., objects performing actions, such as cat eating or dog jumping. We introduce an end-to-end multitask objective that jointly learns object-action relationships. We compare it with different training objectives, validate its effectiveness for detecting object-action pairs in videos, and show that both tasks of object and action detection benefit from this joint learning. In experiments on the A2D dataset [Xu et al., 2015], we obtain state-of-the-art results on segmentation of object-action pairs. In the third part, we are the first to propose an action tubelet detector that leverages the temporal continuity of videos instead of operating at the frame level, as state-of-the-art approaches do. The same way modern detectors rely on anchor boxes, our tubelet detector is based on anchor cuboids by taking as input a sequence of frames and outputing tubelets, i.e., sequences of bounding boxes with associated scores. Our tubelet detector outperforms all state of the art on the UCF-Sports [Rodriguez et al., 2008], J-HMDB [Jhuang et al., 2013a], and UCF-101 [Soomro et al., 2012] action localization datasets especially at high overlap thresholds. The improvement in detection performance is explained by both more accurate scores and more precise localization.

Object category detection, the task of determining if one or more instances of a category are present in an image with their corresponding locations, is one of the fundamental problems of computer vision. The task is very challenging because of the large variations in imaged object appearance, particularly due to the changes in viewpoint, illumination and intra-class variance. Although successful solutions exist for learning object category detectors, they require massive amounts of training data. Transfer learning builds upon previously acquired knowledge and thus reduces training requirements. The objective of this work is to develop and apply novel transfer learning techniques specific to the object category detection problem. This thesis proposes methods which not only address the challenges of performing transfer learning for object category detection such as finding relevant sources for transfer, handling aspect ratio mismatches and considering the geometric relations between the features; but also enable large scale object category detection by quickly learning from considerably fewer training samples and immediate evaluation of models on web scale data with the help of part-based indexing. Several novel transfer models are introduced such as: (a) rigid transfer for transferring knowledge between similar classes, (b) deformable transfer which tolerates small structural changes by deforming the source detector while performing the transfer, and (c) part level transfer particularly for the cases where full template transfer is not possible due to aspect ratio mismatches or not having adequately similar sources. Building upon the idea of using part-level transfer, instead of performing an exhaustive sliding window search, part-based indexing is proposed for efficient evaluation of templates enabling us to obtain immediate detection results in large scale image collections. Furthermore, easier and more robust optimization methods are developed with the help of feature maps defined between proposed transfer learning formulations and the “classical” SVM formulation.

Object localization is currently an active area of research in computer vision. The object localization task is to identify all locations of an object class within an image by drawing a bounding box around objects that are instances of that class. Object locations are typically found by computing a classification score over a small window at multiple locations in the image, based on some chosen criteria, and choosing the highest scoring windows as the object bounding-boxes. Localization methods vary widely, but there is a growing trend towards methods that are able to make localization more accurate and efficient through the use of context. In this thesis, I investigate whether contextual relationships between related objects can be leveraged to improve localization efficiency through a reduction in the number of windows considered for each localization task. I implement a context-driven localization model and evaluate it against two models that do not use context between objects for comparison. My model constrains the search spaces for the target object location and window size. I show that context-driven methods substantially reduce the mean number of windows necessary for localizing a target object versus the two models not using context. The results presented here suggest that contextual relationships between objects in an image can be leveraged to significantly improve localization efficiency by reducing the number of windows required to find the target object.

In this dissertation, the object of research is the advanced generative learning objects (AGLOs), models and processes related to them. The objective of the research is to develop and to investigate the methods that enable to formalize the designing of advanced generative learning objects and using them in teaching/learning of informatics effectively. Scientific novelty: 1. AGLOs expand the informatics learning variability aspects (pedagogical, social, technological, and content). Based on those insights, it is possible to adapt and apply software engineering and computer science methods in the e-learning domain. 2. To our best knowledge, feature-based modelling in the informatics learning domain has been performed systematically for the first time. Such an approach evaluates the domain variability, aggregates and verifies the created models. 3. Formalization of the models at two levels (feature-based and executable specification) provides pre-conditions for automated tools design. 4. From the viewpoint of automatic educational content creation, AGLO extends the concept of reusability in e-learning. The specialized learning environments with integrated AGLO implement the visual transformation of a real task into its physical process, thus providing a high level of motivation and effective learning.
Darbe tiriami informatikos (programavimo) mokymuisi skirti išplėstiniai generatyviniai mokymosi objektai (IGMO) ir su jais susiję informaciniai specifikavimo/atvaizdavimo, transformavimo modeliai ir procesai. Darbo tikslas yra pateikti ir ištirti metodiką, įgalinančią formalizuoti išplėstinių generatyvinių mokymosi objektų kūrimą ir efektyvų jų naudojimą mokant informatikos (programavimo). Darbo mokslinis naujumas: 1. IGMO išplečia informatikos mokymosi sritį naujais aspektais (pedagoginiais, socialiniais, technologiniais, turinio), aprašomais terminu mokymosi variantiškumas. Tai įgalino pagrįstai adaptuoti ir naujai pritaikyti programų inžinerijos ir kompiuterijos principus ir metodus e.mokymosi sričiai. 2. Požymiais grįstas sisteminis informatikos (programavimo) mokymosi srities modeliavimas, mūsų žiniomis, atliktas pirmą kartą. Jis įvertina mokymosi variantiškumą ir agreguoja bei verifikuoja įvairialypius modelius. Tai sudaro prielaidas sistemingam IGMO kūrimui. 3. Modelių formalizavimas dviejuose lygmenyse (požymių modelių ir vykdomųjų specifikacijų) sudaro sąlygas automatizuotiems įrankiams kurti. 4. IGMO išplečia pakartotinio panaudojimo koncepciją e.mokymesi turinio automatinio kūrimo požiūriu. Sukurtos specializuotos heterogeninės mokymosi aplinkos, į kurias integruoti IGMO, įgyvendina realaus uždavinio vizualinę transformaciją į fizinį procesą bei užtikrina aukštą mokinių motyvaciją ir efektyvų mokymąsi.

This study presents the results of a usability test of reusable learning objects currently in development in the Instructional Services Department of the UNC-Chapel Hill University Library. The learning objects are Web-based learning modules that are part of a new project, Stories of the American South, and they aim to make digital primary source material more accessible by breaking it into manageable units and providing contextual information. Seven undergraduate students viewed three prototypes, each focused on a unique theme related to the history of the American South. The results suggested that the learning objects are generally usable in design and function, and participants’ responses to design and content were positive. Several changes are suggested to optimize the usability of the learning object prototypes, and further usability testing with other audiences is recommended.

This work is aimed at understanding and modelling the perceptual stability mechanisms of human visual systems, regardless of large changes in the visual sensory input resulting from some visual-related motions. Invariant neuronal representation plays an important role for memory systems to associate and recognize objects.
In contrast to the bulk of previous research work on the learning of invariance that focuses on the pure bottom-up visual information, we incorporate visual-related self-action signals such as commands for eye, head or body movements, to actively collect the changing visual information and gate the learning process. This helps neural networks learn certain degrees of invariance in an efficient way. We describe a method that can produce a network with invariance to changes in visual input caused by eye movements and covert attention shifts. Training of the network is controlled by signals associated with eye movements and covert attention shifting. A temporal perceptual stability constraint is used to drive the output of the network towards remaining constant across temporal sequences of saccadic motions and covert attention shifts. We use a four-layer neural network model to perform the position-invariant extraction of local features and temporal integration of invariant presentations of local features. The model is further extended to handle viewpoint invariance over eye, head, and/or body movements. We also study cases of multiple features instead of single features in the retinal images, which need a self-organized system to learn over a set of feature classes. A modified saliency map mechanism with spatial constraint is employed to assure that attention stays as much as possible on the same targeted object in a multiple-object scene during the first few shifts.
We present results on both simulated data and real images, to demonstrate that our network can acquire invariant neuronal representations, such as position and attention shift invariance. We also demonstrate that our method performs well in realistic situations in which the temporal sequence of input data is not smooth, situations in which earlier approaches have difficulty.

In this MSc thesis, the possibility to classify moving objects based on radar detection data is investigated. The intention is a light-weight, low-level system that relies on cheap hardware and calculations of low complexity. Scania, the company that has commissioned this project, is interested in the usage potential of such a system in autonomous vehicle applications. Specifically, the class information is desired in order to enhance the moving object tracker, a subsystem that represents a crucial skillset of an autonomously driving truck. Objects are classified as belonging to one of four classes: Pedestrian, bicyclist, personal vehicle and truck. The major system input consists of sensor data from a set off our short-range mono-pulse Doppler radars operating at 77 GHz. Using a set of training and validation data gathered and labeled within this project, a classification system based on the machine learning method of Support vector machines is created. Several other supporting software structures are also created and evaluated. In the validation phase, the system is shown to discern well between the four classes. System simulations performed on logged radar data show promising performance also in situations not reflected within the labeled dataset.To further investigate the feasibility of the system, it has been implemented and tested on the prototype test vehicle Astator, and performance has been evaluated with regards to both real-time constraints and classification accuracy. Overall, the system shows promise in the scenarios for which it was intended, both with respect to real-time and classification performance. In more complex scenarios however, sensor noise is increasingly apparent and affects the system performance in a negative way. The noise is extra apparent in heavy traffic and high velocity scenarios.
I detta examensarbete undersöks möjligheten att klassificera rörliga objekt baserat på data från Dopplerradardetektioner. Slutmålet är ett system som använder billig hårdvara och utför beräkningar av låg komplexitet. Scania, företaget som har beställt detta projekt, är intresserat av användningspotentialen för ett sådant system i applikationer för autonoma fordon. Specifikt vill Scania använda klassinformationen för att lättare kunna följa rörliga objekt, vilket är en väsentlig färdighet för en autonomt körande lastbil. Objekten delas in i fyra klasser: fotgängare, cyklist, bil och lastbil. Indatan till systemet består väsentligen av en plattform med fyra stycken monopulsdopplerradars som arbetar med en vågfrekvens på 77 GHz. Ett klassificeringssystem baserat på maskininlärningskonceptet Support vector machines har skapats. Detta system har tränats och validerats på ett dataset som insamlats för projektet, innehållandes datapunkter med klassetiketter. Ett antal stödfunktioner till detta system har också skapats och testats. Klassificeraren visas kunna skilja väl på de fyra klasserna i valideringssteget. Simuleringar av det kompletta systemet gjort på inspelade loggar med radardata visar lovande resultat, även i situationer som inte finns representerade i träningsdatan. För att vidare undersöka systemet har det implementerats och testats på prototyplastbilen Astator, och prestandan har utvärderats utifrån både realtidstidsperpsektiv och klassificeringsnoggranhet. Generellt uppvisar systemet lovande resultat i scenarier som liknar slutanvändningsområdet. I mer komplexa trafiksituationer och då lastbilen färdas i högre hastigheter leder dock en högre förekomst av sensorbrus till att systemets prestanda försämras.

The use of information technologies to develop, manage, access, and to use learning resources and content from a variety of sources has changed the academic and learning environment. There was a need to focus on the identification of tools and the development of skills to manage these resources, particularly in the kindergarten to grade 12 classrooms. For the students and educators of School District 92 (Nisga' a), there was also a need to provide access to important Nisga'a cultural and heritage learning objects as many preserved videos, pictures, historic documents, cultural teaching materials, and various printed matter were not available for student and teacher use in the schools. This study created an instance of a learning object repository, the Nisga' a Learning Object Repository, and deposited a sample set of 65 objects into the repository in order to evaluate the technology as a school resource. A teacher and student panel survey instrument evaluated the repository as a tool for accessing learning resources. Ten teachers and 15 students returned final surveys. Topics evaluated included uses of learning objects, learning object categories, object attributes, reasons to use the repository, the resource as a multimedia distribution system, and the resource as a digital reading room. The CAREO platform was used to construct the repository, and the ALOHA metadata tool was used to develop metadata for the objects. The Canadian Core Learning Metadata Application Profile was the standard used when writing the metadata. The learning object repository functioned successfully in its ability to store and retrieve different types of objects. The resulting product is truly a technology-based storehouse of Nisga'a culture readily available for use by students and teachers. The technology moves learning and instruction further towards an integrated multimedia environment, and its potential depends on further development and use in the classroom. Additional research, teacher training, and showcase presentations would further lay the foundation for a broadening of classroom use.

To move towards an increasingly automated future, machines are becoming smarter and can now solve tasks only humans could before. In this thesis project, the possibility to find, classify and reconstruct holes in three dimensional objects using machine learning is explored. To achieve this, three dimensional convolutional neural networks are used as a method for semantic segmentation. To combat limited GPU memory and training time, a region-based network was created, this network used smaller regions of the 3D objects to process the image in parts, and thereby evade the memory barrier of the GPU, create reconstructions with a higher resolution, and lower training time. The results show that 3D semantic segmentation is possible and is a promising method for reconstruction of features in 3D objects. However, the thesis work also highlights the importance of a qualitative dataset that is a good representation of the data that is intended to be used with the models.

A aprendizagem assistida por computador, conhecida mais amplamente com o nome genérico de e-learning, converteu-se numa poderosa ferramenta com amplas potencialidades dentro do campo educativo. Mesmo assim, uma das maiores críticas que esta recebe é que na maioria dos casos os cursos que são implementados seguem um enfoque “one size fits all”, isto é, que todos os alunos recebem exatamente o mesmo conteúdo e da mesma maneira desconhecendo suas necessidades particulares. Esta falha radica não só na falta de interação direita entre aluno e tutor, senão também na falta de um desenho instrucional apropriado que considere alguns dos diversos enfoques disponíveis hoje em dia. Existem diversos enfoques que procuram solucionar este problema e adaptar o processo de ensino os estudantes. Pode-se dizer que na vanguarda de estes enfoques encontram-se os Sistemas Educacionais Inteligentes Adaptativos, os quais combinam as funcionalidades de dois enfoques: os Sistemas Hipermídia Educacionais Adaptativos y os Sistemas Tutoriais Inteligentes. Embora, logo de uma extensa revisão bibliográfica, se encontrou que existe ainda um inconveniente importante com este tipo de sistemas e em particular com seus modelos de referência: ou são demasiado simples, incluindo somente umas poucas funcionalidades; ou são demasiado complexos, o que dificulta seu desenho e implementação. Considerando este panorama, o objetivo principal de esta tese foi a definição de um modelo de referência intentando alcançar esse equilíbrio esquivo, de tal maneira que permita o desenho de cursos que se adaptem de una maneira efetiva e inteligente ao progresso e características de cada estudante, mas sem ser demasiado complexo. Outra propriedade importante desse modelo és que integra o uso de Objetos de Aprendizagem, promovendo assim a flexibilidade e a usabilidade. Para alcançar este objetivo geral, três sub modelos foram considerados: um modelo do domínio, um modelo do estudante y um modelo do tutor. O primeiro serve para estruturar o domínio de conhecimento e foi definido usando a noção de objetivo de aprendizagem junto com um esquema flexível multi-nível com operações opcionais de pré-requisitos. O segundo visa caracterizar aos estudantes e considera informação pessoal, de conhecimento e psico-cognitiva. O terceiro pode ser considerado como o coração do sistema e define as funcionalidades adaptativas consideradas: sequenciamento y navegação, apresentação de conteúdo, evacuação, y suporte colaborativo. Com o fim de clarificar os três sub modelos, assim como todos seus componentes e relações, se presentou um exemplo de instanciação que se denominou Doctus, o qual consiste em una ferramenta de autor para cursos adaptativos. Doctus não somente serviu para exemplificar o uso do modelo de referência em sua totalidade, mas também para refinar os sub modelos e alguns procedimentos involucrados. Como parte final de esta tese, se realizou também a implementação e validação preliminar de Doctus. Isto foi feito com 51 sujeitos, professores em diversos níveis de formação. Os resultados obtidos em esta etapa foram sobressalientes no sentido que todas as funcionalidades adaptativas foram bem avaliadas e todos os pesquisados manifestaram seu entusiasmo por contar com uma ferramenta que lhes ajudara em seus práticas docentes considerando a seus estudantes como indivíduos particulares.
Computer Aided Learning, known more widely with the generic name of e-learning, has become a powerful tool with lots of potentialities within educational field. Even though, one of the main critics that it receives is that in most cases the implemented courses follows a “one size fits all” approach, which means that all students receive the same content in the same way being unaware of their particular needs. This problem is not due only to the absence of direct interaction between student and tutor, but also because of the lack of an appropriate instructional design. There are several approaches which deal with this issue and look for adapt the teaching process to students. One could say that in the top of those approaches the Adaptive and Intelligent Educational Systems are situated, which merges the functionalities of two approaches: the Adaptive Educational Hypermedia Systems and the Intelligent Tutoring Systems. Nevertheless, after an extensive literature review, a major inconvenience is still found for this kind of systems and particularly for their reference models: or they are too simple, including just a few functionalities; or they are too complex, which difficult their design and implementation. Considering this panorama, the main objective of this dissertation thesis was the definition of a reference model trying to reach such an elusive equilibrium, in such a way that allows the design of courses which adapt themselves in an intelligent and effective way to the progress and characteristics of each student but without being too complex. Another important feature is that this model integrates Learning Objects, promoting this way flexibility and reusability. In order to achieve this general objective, three sub-models were considered: a domain model, a student model and a tutor model. The first one serves to structure the knowledge domain and was defined using the notion of learning goal and a flexible multilevel schema with optional prerequisite operations. The second one aids to characterize students and considered personal, knowledge and psycho-cognitive information. The third one may be considered as the hearth of the system and defines the adopted adaptive functionalities: sequencing and navigation, content presentation, assessment, and collaborative support. With the aim of clarify the three sub-models, as well as all their components and relationships, an instantiation example was also presented. Such an instantiation was called Doctus, an authoring tool for adaptive courses. Doctus was not only helpful to exemplify the setup of the referece model as a whole, but also to refine sub-models and several procedures envolved. As final part of the dissertation, the implementation and preliminary validation of Doctus was performed. This was done with 51 subjects, teachers from different formation levels. The obtained results in this stage were outstanding, all the adaptive functionalities were well evaluated and all of those polled felt enthusiastic about counting with a tool for helping them in their teaching practices considering students as particular individuals.
El aprendizaje asistido por computador, conocido más ampliamente con el nombre genérico de e-learning, se ha convertido en una poderosa herramienta con amplias potencialidades dentro del campo educativo. Aun así, una de las mayores críticas que este recibe es que en la mayoría de los casos los cursos que son implementados siguen un enfoque “one size fits all”, es decir, que todos los alumnos reciben exactamente el mismo contenido y de la misma manera desconociendo sus necesidades particulares. Esta falla radica no sólo en la falta de interacción directa entre alumno y tutor, sino también en la falta de un diseño instruccional apropiado que considere diversos de los enfoques disponibles hoy en día. Existen diversos enfoques que buscan solucionar este problema y adaptar el proceso de enseñanza a los estudiantes. Se podría decir que a la vanguardia de estos enfoques se encuentran los Sistemas Educacionales Inteligentes Adaptativos, los cuales combinan las funcionalidades de dos enfoques: los Sistemas Hipermedia Educacionales Adaptativos y los Sistemas Tutoriales Inteligentes. Sin embargo, luego de una extensa revisión bibliográfica, se encontró que existe aún un inconveniente importante con este tipo de sistemas y en particular con sus modelos de referencia: o son demasiado simples, incluyendo solamente unas pocas funcionalidades; o son demasiado complejos, lo cual dificulta su diseño e implementación. Considerando este panorama, el objetivo principal de esta tesis fue la definición de un modelo de referencia intentando alcanzar tal equilibrio esquivo, de tal manera que permita el diseño de cursos que se adapten de una manera efectiva e inteligente al progreso y características de cada estudiante pero sin ser demasiado complejo. Otra propiedad importante de dicho modelo es que integra el uso de Objetos de Aprendizaje, promoviendo así la flexibilidad y la reusabilidad. Con el fin de alcanzar este objetivo general, tres sub modelos fueron considerados: un modelo del dominio, un modelo del estudiante y un modelo del tutor. El primero sirve para estructurar el dominio de conocimiento y fue definido empleando la noción de objetivo de aprendizaje junto con un esquema flexible multinivel con operaciones opcionales de prerrequisitos. El segundo busca caracterizar los estudiantes y considera información personal, de conocimiento y psico-cognitiva. El tercero puede ser considerado como el corazón del sistema y define las funcionalidades adaptativas consideradas: secuenciamiento y navegación, presentación de contenido, evaluación, y soporte colaborativo. Con el fin de clarificar los tres sub modelos, así como todos sus componentes y relaciones, se presentó además un ejemplo de instanciación. Tal instanciación se denominó Doctus, el cual consiste en una herramienta de autor para cursos adaptativos. Doctus no solamente sirvió para ejemplificar el uso del modelo de referencia en su totalidad, sino también para refinar los sub modelos y algunos procedimientos involucrados. Como parte final de esta tesis, se realizó también la implementación y validación preliminar de Doctus. Esto se hizo con 51 sujetos, todos profesores en diversos niveles de formación. Los resultados obtenidos en esta etapa fueron sobresalientes en el sentido que todas las funcionalidades adaptativas fueron bien evaluadas y todos los encuestados manifestaron su entusiasmo por contar con una herramienta que les ayudara en sus prácticas docentes considerando a sus estudiantes como individuos particulares.

A UML é uma linguagem de modelagem largamente aceite entre profissionais da área de computação para a modelagem de sistemas simples e complexos. Tomando em consideração que os objetos de aprendizagem são entidades digitais, para o seu desenvolvimento, a utilização de metodologias torna-se um imperativo, se o objetivo for o de oferecer ao setor de educação ferramentas capazes de apoiar o processo de ensino aprendizagem. Os objetos de aprendizagem são ferramentas que podem contribuir para o aluno alcançar a aprendizagem significativa, se forem desenvolvidos dentro dos parâmetros considerados facilitadores da aprendizagem significativa. A presente pesquisa propõe uma metodologia alicerçada na aprendizagem significativa, denominada Metodologia de Modelagem de objetos de aprendizagem em UML (MOAUML) baseada em princípios de UML e de Design Instrucional, para a modelagem de objetos de aprendizagem, com vista a facilitar o desenvolvimento de objetos de aprendizagem que favoreçam a aprendizagem. Objetivando a validação da metodologia, equipes com diferentes perfis profissionais, modelaram 9 objetos de aprendizagem entre novos e legados. O resultado das modelagens mostrou que a utilização da MOAUML contribui para a facilidade na modelagem de objetos de aprendizagem, embora tenha sido mais fácil para equipes que já estavam familiarizadas com métodos de modelagem de software.
The UML is a widely accepted modelling language among computer specialists from simple to complex systems. In order to develop learning objects as digital entities, it is very important the utilization of any methodology aiming to deliver to the education sector capable tools for supporting the process of teaching learning. The learning objects are powerful tools that can contribute for student’s achievement of meaningful learning. This fact can be true only if the development procedures satisfied some characteristics considered facilitators of meaningful learning. This study proposes a methodology based on the meaningful learning, designated Modelling of Learning Objects Using UML (MOAUML) based on principles of UML and Instructional Design, for the modelling of learning objects targeting to facilitate the development of learning objects that promote meaningful learning. In order to validate the MOAUML, different teams with distinguish profiles, were modelled 9 learning objects among news and legacies. The results of that process disclosed that the use of MOAUML contributes to the ease of modelling learning objects, although it has been easier for teams that were already familiar with software modelling processes.

O processo de ensino-aprendizagem visa que sujeitos-professores promovam o ensino a alunos, com o objetivo que eles adquiram um conhecimento. Entretanto, este processo é influenciado por diversas variáveis: estrutura curricular, perfil dos alunos, direção e coordenação pedagógica, corpo docente, comunidade onde estão inseridos, duração e planejamento das aulas, recursos disponíveis, etc. O planejamento permite que o professor organize e estabeleça como as aulas ocorrerão, bem como o tempo necessário, a fim de garantir a aprendizagem. Neste processo, o professor faz uso de recursos didáticos, como mediadores. Dentre as diversas possibilidades de recursos educativos, encontram-se os objetos de aprendizagem. Eles consistem em recursos físicos ou digitais, que tem como diferencial, o uso de um conjunto de metadados com informações relacionadas ao objeto de aprendizagem. Os metadados são arquivos padronizados que seguem especificações que orientam como estes devem estar descritos, de forma a permitir a indexação e recuperação em repositórios. Dentre as especificações para metadados, destacam-se: a Dublin Core, IEEE LOM e OBAA. O Dublin Core é a especificação mais utilizada por repositórios, entretanto, foi desenvolvida para descrever qualquer recurso, sendo educacional ou não. O IEEE LOM é considerada a especificação mais completa, e permite a descrição de objetos de aprendizagem físicos e digitais. O OBAA é uma proposta brasileira que se baseia no IEEE LOM e apresenta como diferencial, por exemplo, a descrição de objetos de aprendizagem multiplataforma e maior detalhamento das características pedagógicas. Os atuais estudos relacionados a objetos de aprendizagem e metadados concentram-se no desenvolvimento e uso destes recursos. Logo, não há pesquisas que relacionem o uso dos metadados como suporte ao planejamento pedagógico. Em face deste cenário, a presente tese investiga como os usuários-professores fazem uso dos metadados no planejamento pedagógico que inclui o uso de objetos de aprendizagem. Ainda, é investigado como os metadados da especificação OBAA auxilia na definição dos requisitos técnicos e pedagógicos, comparado ao Dublin Core e IEEE LOM. Para isto, foi realizada uma pesquisa qualitativa, usando as técnicas de grupo focal e o estudo de casos múltiplos, com a participação de dois tipos de sujeitos: especialistas e usuários professores. A pesquisa com os especialistas foi realizada sob a abordagem do grupo focal não estruturado, onde quatro casos foram analisados e discutidos. Já os usuários-professores participaram dos grupos focais semiestruturado e estruturado. Inicialmente, eles conceitualizaram os termos “recursos didáticos” e “planejamento pedagógico” para então desenvolver planos de aula fazendo uso de objetos de aprendizagem, onde se foi observado como os metadados se fizeram presentes no planejamento. Os resultados obtidos mostraram que a especificação OBAA atende os requisitos técnicos e pedagógicos, embora são propostas algumas modificações. Também foi possível identificar quais metadados realmente auxiliaram os professores no planejamento, destacando os que descrevem o recurso pedagogicamente. Ainda, percebeu-se que a especificação OBAA melhor auxiliou os professores neste processo, e neste sentido, foi proposto um conjunto reduzido de metadados OBAA contemplando os elementos essenciais para a descrição de um objeto de aprendizagem em um repositório e que assista o professor no planejamento.
The teaching-learning process aims to the teachers teach students, in order that they acquire knowledge. However, this process is influenced by several variables: curriculum structure, students background, direction and pedagogical coordination, its community, class duration and lesson planning, resources, etc.. The planning allows the teacher to organize and establish how the classes will occur, and the time necessary to ensure learning. In this process, the teacher makes use of pedagogical resources as mediators. Among the various possibilities of educational resources, are the learning objects. They consist of physical or digital resources, which has the advantage, the use of a set of metadata with information related to the learning object. The metadata files are standardized specifications that follow that guide how they should be described, to allow indexing and retrieval in repositories. Among the specifications for metadata include: the Dublin Core, IEEE LOM and OBAA. The Dublin Core is a specification used by many repositories, however, was developed to describe any resource, and educational or not. The IEEE LOM is considered the most complete specification, and allows the description of physical objects and digital learning. The OBAA a Brazilian proposal is based on the IEEE LOM and has unique features, for example, the description of learning objects and greater detail of multiplatform pedagogical features. The current studies related to learning objects and metadata focus on the development and use of these resources. Thus, there is no research that relate to the use of metadata to support educational planning. Given this scenario, this thesis investigates how users-teachers make use of metadata on educational planning that includes the use of learning objects. In addiction, it is investigated how the metadata specification OBAA assists in defining the technical and pedagogical requirements, compared to the Dublin Core and IEEE LOM. For this, we performed a qualitative study using focus group techniques and the study of multiple cases involving two types of subjects: teacher experts and users. The research was conducted with experts in the focus group approach is not structured, where four cases were analyzed and discussed. The users-teachers participated in semi-structured focus groups and structured. Initially, they conceptualized the term "educational resources" and "lesson planning" and then develop lesson plans using learning objects, which was observed as the metadata were present in the planning. The results showed that the specification meets the technical requirements OBAA and teaching, although some modifications are proposed. It was also possible to identify what metadata really helped the teachers in planning, highlighting that describe the teaching resources. Still, it was realized that the best specification OBAA assisted the teachers in this process, and in this sense, we proposed a reduced set of metadata OBAA contemplating the essential elements for describing a learning object in a repository and to assist the teacher in planning.