Community based service-learning is one of the fastest growing reforms in higher education, especially in the field of health care. The increased interest in this phenomenon is based on the demands by government and society that higher education institutions should be more responsive to the needs of the community. Literature, however, reflects that service learning lacks a sound theoretical base to guide teaching and learning due to limited research in this area. This study was, therefore, aimed at exploring the phenomenon knowledge construction in basic nursing programmes in selected South African nursing schools with the intention to generate a middle range theory that may be used to guide the process of knowledge construction in community-based service-learning programmes. This study adopted a qualitative approach and a grounded theory research design by Strauss and Corbin. Two university-based schools of nursing were purposively selected to participate in the study. There were a total number of 16 participants. The collection of data was intensified by the use of multiple sources of data (participant observation, documents analysis and in-depth structured interviews). The data analysis process entailed three phases; open, axial and selective coding. The results of the study revealed that the phenomenon “knowledge construction” is conceptualised as having specific core characteristics, which include the use of authentic health-related problems, academic coaching through scaffolding, academic discourse-dialogue and communities of learners. The findings showed that there are a number of antecedent conditions and contextual circumstances contributing to how knowledge is constructed in a community based service learning programme. The process of knowledge construction emerged as cyclical in nature, with students, facilitators and community members having specific roles to play in the process. A number of intervening variables were identified that had an influence on the expected outcomes on knowledge construction in community based service learning programmes. These findings led to the generation of a conceptual model. Knowledge construction according to this model takes place in an environment which is characterised by interactive learning, collaborative learning, actively learning and inquiry-based learning through continuous reflective learning processes. The main concepts in this conceptual model include concrete learning experiences, continuous reflection, problem posing, problem analysis, knowledge deconstruction and knowledge generation, knowledge verification, knowledge generation, testing of generated knowledge and evaluation of generated knowledge. The sub-concepts include learning through senses, an initial situation, health-related triggers, social interaction, reflection-in action, reflection-on action, hypotheses generation, conceptualisation of learning experiences, information validation and community interventions. Recommendations were categorised into education and training of academic staff, application of the model and further research with regard to quality assurance in CBSL programmes as well as the use of other research designs for similar studies. / Thesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2011.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

El test de circuits és una fase del procés de producció que cada vegada pren més importància quan es desenvolupa un nou producte. Les tècniques de test i diagnosi per a circuits digitals han estat desenvolupades i automatitzades amb èxit, mentre que aquest no és encara el cas dels circuits analògics. D'entre tots els mètodes proposats per diagnosticar circuits analògics els més utilitzats són els diccionaris de falles. En aquesta tesi se'n descriuen alguns, tot analitzant-ne els seus avantatges i inconvenients.Durant aquests últims anys, les tècniques d'Intel·ligència Artificial han esdevingut un dels camps de recerca més importants per a la diagnosi de falles. Aquesta tesi desenvolupa dues d'aquestes tècniques per tal de cobrir algunes de les mancances que presenten els diccionaris de falles. La primera proposta es basa en construir un sistema fuzzy com a eina per identificar. Els resultats obtinguts son força bons, ja que s'aconsegueix localitzar la falla en un elevat tant percent dels casos. Per altra banda, el percentatge d'encerts no és prou bo quan a més a més s'intenta esbrinar la desviació.Com que els diccionaris de falles es poden veure com una aproximació simplificada al Raonament Basat en Casos (CBR), la segona proposta fa una extensió dels diccionaris de falles cap a un sistema CBR. El propòsit no és donar una solució general del problema sinó contribuir amb una nova metodologia. Aquesta consisteix en millorar la diagnosis dels diccionaris de falles mitjançant l'addició i l'adaptació dels nous casos per tal d'esdevenir un sistema de Raonament Basat en Casos. Es descriu l'estructura de la base de casos així com les tasques d'extracció, de reutilització, de revisió i de retenció, fent èmfasi al procés d'aprenentatge.En el transcurs del text s'utilitzen diversos circuits per mostrar exemples dels mètodes de test descrits, però en particular el filtre biquadràtic és l'utilitzat per provar les metodologies plantejades, ja que és un dels benchmarks proposats en el context dels circuits analògics. Les falles considerades son paramètriques, permanents, independents i simples, encara que la metodologia pot ser fàcilment extrapolable per a la diagnosi de falles múltiples i catastròfiques. El mètode es centra en el test dels components passius, encara que també es podria extendre per a falles en els actius. / Testing circuits is a stage of the production process that is becoming more and more important when a new product is developed. Test and diagnosis techniques for digital circuits have been successfully developed and automated. But, this is not yet the case for analog circuits. Even though there are plenty of methods proposed for diagnosing analog electronic circuits, the most popular are the fault dictionary techniques. In this thesis some of these methods, showing their advantages and drawbacks, are analyzed.During these last decades automating fault diagnosis using Artificial Intelligence techniques has become an important research field. This thesis develops two of these techniques in order to fill in some gaps in fault dictionaries techniques. The first proposal is to build a fuzzy system as an identification tool. The results obtained are quite good, since the faulty component is located in a high percentage of the given cases. On the other hand, the percentage of successes when determining the component's exact deviation is far from being good.As fault dictionaries can be seen as a simplified approach to Case-Based Reasoning, the second proposal extends the fault dictionary towards a Case Based Reasoning system. The purpose isnot to give a general solution, but to contribute with a new methodology. This second proposal improves a fault dictionary diagnosis by means of adding and adapting new cases to develop aCase Based Reasoning system. The case base memory, retrieval, reuse, revise and retain tasks are described. Special attention to the learning process is taken.Several circuits are used to show examples of the test methods described throughout the text. But, in particular, the biquadratic filter is used to test the proposed methodology because it isdefined as one of the benchmarks in the analog electronic diagnosis domain. The faults considered are parametric, permanent, independent and simple, although the methodology can be extrapolated to catastrophic and multiple fault diagnosis. The method is only focused and tested on passive faulty components, but it can be extended to cover active devices as well.

Les médecins cherchent à résoudre les problèmes de santé posés par des individus. Une solution individualisée tient compte de la singularité du patient concerné. L'individualisation des pratiques est-elle possible et souhaitable? Le cas échéant, selon quelles modalités peut-elle ou doit-elle être réalisée'? La première partie de la thèse vise à montrer: que la question se pose depuis les premières théories de la décision médicale (Hippocrate) ; qu'elle s'est posée de façon aiguë au début du XIX" siècle, avec l'apparition des études statistiques; et que l'observation médicale et son évolution concrétisent la façon dont la documentation des cas et leur individualisation interagissent. La deuxième partie reprend la question dans le contexte contemporain, à travers la naissance de l'"evidence-based medicine", ses critiques et son évolution. La troisième partie montre que l'articulation du raisonnement par règles et du raisonnement par cas modélise de façon opérationnelle une démarche raisonnée d'individualisation des décisions médicales. Ce modèle simple permet de rendre compte du mouvement d'aller-retour entre deux conceptions de l'individualisation et d'en proposer une version équilibrée, mise à l'épreuve dans les domaines de l'évaluation des pratiques et de la littérature médicale. / Physicians try to solve health problems of individual patients. Customized solutions take into account the uniqueness of the patient. Is the individualization of medical decisions possible and desirable'? If so, how can I tor should it be performed? The first part of the thesis shows: that the question arises since the first conceptualizations of medical reasoning (Hippocrates); that is was much debated in the early nineteenth century, when statistical studies were first performed to guide medical decisions; and that the medical observation and its evolution materialize how case documentation and management interact. The second part addresses the issue in the current context, from the birth of evidence-based medicine, its cri tics and its evolution. The third part shows that linking rule-based and case-based reasoning adequately pictures the process of customizing medical decisions. This simple model can account for the movement between two kinds of customization and leads to a balanced approach, tested in the field of practice evaluation and medical literature.

This paper aims to provide an Interactive Learning Environment using the computer to support aid in the diagnosis and treatment of patients with neurological disorders. The study proposes an architecture which facilitates the activities of students in the health area, in decision making, for the advice of physiotherapy for stroke patients. It was used the approach of Case-Based Reasoning (CBR) that has, like general idea, the use of past experiences to the solution of new problems. This work focused on the stages of indexing, representation and retrieval of cases, with the use of metrics, similar characteristics as the Count Features and Tversky s Contrast Model. A prototype was built for the validation of these metrics, proving the efficiency in the recovery of the cases on the basis of cases / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Este trabalho tem como objetivo apresentar um Ambiente Interativo de Aprendizagem utilizando o Computador de apoio ao diagnóstico e auxílio no tratamento de pacientes que apresentam disfunções neurológicas. A pesquisa propõe uma arquitetura que facilite as atividades dos estudantes da área da saúde, na tomada de decisão, para o aconselhamento fisioterápico dos pacientes de acidente vascular cerebral. Utilizou-se a abordagem de Raciocínio Baseado em Casos (RBC) que tem como idéia geral a utilização de experiências passadas para a solução de novos problemas. Este trabalho se concentrou nas fases de indexação, representação e recuperação dos casos, com a utilização de métricas de similaridade como a Contagem de Características e a Regra do Contraste de Tversky. Um protótipo foi construído para a validação dessas métricas, provando a eficiência na recuperação dos casos na base de casos

This work aims to propose a recommendation model of prescription items for Pediatric Oncology based on data extraction from Electronic Patient Record. These data are used as indexed cases to aid providers of medical service based on the similarity of prescriptions, according to the patient's history. From the viewpoint of aid medical education, the modeling objective support the student or health professional in understanding the decision-making process during the prescription items oncological medical treatment, for example drugs, laboratory exams or images exams, diet, gases, care, chemotherapy, radiotherapy. To develop the model, was used the approach of Case Based Reasoning (CBR), through the representation of prescription medical base-case, indexed by their treatment items. During the recovery phase of cases, we used the tool. Data Mining by applying the model of association rule, together with the algorithm "apriori" for obtaining the similarity between cases. To update the case base, a procedure database for performing the process of Extraction, Transformation and Load of the database was developed. The developed model was applied in the database of the Electronic Patient Record of the Santa Casa de Misericordia de Maceio, based on Hospital Management Systems “MV Sistemas”, deployed in the unit since 2005. For the presentation of results, was used the Oracle Data Miner tool, which allowed access to the database and analysis of selected cases by identifying key words contained in the evolution of the clinical condition of the patient. The application of the experiments validate the occurrence of allowed combined application of items of treatment according to the keywords, which can be used as input in the process of making medical decision and tutoring. / Este trabalho tem como objetivo propor um Modelo de Recomendação de itens de prescrição para Oncologia Pediátrica baseado na extração de dados do Prontuário Eletrônico do Paciente. Esses dados são utilizados como casos indexados, para auxiliar os prestadores de serviço médico baseados na similaridade de prescrições, de acordo com o histórico do paciente. Do ponto de vista do apoio a educação médica, a modelagem objetiva apoiar o estudante ou o profissional de saúde no entendimento do processo de tomada de decisão durante a fase de prescrição de itens de tratamento médico oncológico, como, por exemplo: medicamentos, exames de laboratório ou de imagens, dieta, gases, cuidados, quimioterapia, radioterapia. Para o desenvolvimento do modelo, utilizou-se a abordagem de Raciocínio Baseado em Casos (RBC), através da representação de uma base de casos de prescrição médica, indexada por seus itens de tratamento. Durante a fase de recuperação de casos, utilizou-se a ferramenta de Mineração de Dados aplicando-se o modelo de regra de associação, em conjunto com o algoritmo “apriori” visando a obtenção da similaridade entre casos. Para a atualização da base de casos, foi desenvolvido um procedimento de banco de dados para execução do processo de Extração, Transformação e Carga da base de dados. O modelo desenvolvido foi aplicado na base de dados do Prontuário Eletrônico do Paciente da Santa Casa de Misericórdia de Maceió, baseado no sistema de gestão hospitalar MV Sistemas, implantado na unidade desde 2005. Para a apresentação dos resultados, utilizou-se a ferramenta Oracle Data Miner, que possibilitou o acesso ao banco de dados e a análise dos casos selecionados pela identificação de palavras chaves contidas na evolução do estado clínico do paciente. A aplicação dos experimentos permitiu validar a ocorrência de aplicação conjunta de itens de tratamento de acordo com as palavras chaves, o que pode ser utilizado como elemento para o processo de tomada de decisão médica e tutoria.

Dans ce travail, une étude est réalisée sur le développement d'une méthodologie permettant la génération et l'évaluation de nouvelles trajectoires de valorisation pour des déchets. Ainsi, pour répondre à cette problématique, trois sous problèmes ont été identifiés. Le premier concerne un cadre de modélisation permettant la représentation structurée et homogène de chaque trajectoire, ainsi que les indicateurs choisis pour l'évaluation de ces dernières, permettant une sélection ultérieure. Le deuxième se concentre sur le développement d'une méthodologie puis la réalisation d'un outil permettant la génération de nouvelles trajectoires en s'appuyant sur d'autres connues. Enfin, le dernier sous problème concerne le développement d'un second outil développé pour modéliser et estimer les trajectoires générées. La partie de création d'un cadre de modélisation cherche à concevoir des structures globales qui permettent la catégorisation des opérations unitaires sous plusieurs niveaux. Trois niveaux de décomposition ont été identifiés. La Configuration générique de plus haut niveau, qui décrit la trajectoire sous de grandes étapes de modélisation. Le second niveau, Traitement générique propose des ensembles de structures génériques de traitement qui apparaissent régulièrement dans les trajectoires de valorisation. Enfin, le plus bas niveau se focalise sur la modélisation des opérations unitaires. Un second cadre a été créé, plus conceptuel et comportant deux éléments : les blocs et les systèmes. Ces cadres sont ensuite accompagnés par un ensemble d'indicateurs choisis à cet effet. Dans une volonté d'approche de développement durable, un indicateur est sélectionné pour chacune de des composantes : économique, environnemental et social. Dans notre étude, l'impact social se limite à l'estimation du nombre d'emplois créés. Afin de calculer cet indicateur, une nouvelle approche se basant sur les résultats économiques d'une entreprise a été proposée et validée.L'outil de génération de nouvelles trajectoires s'appuie sur l'utilisation de la connaissance en utilisant un système de raisonnement à partir de cas (RàPC). Pour être adapté à notre problématique, la mise en œuvre de ce dernier a impliqué la levée de plusieurs points délicats. Tout d'abord, la structuration des données et plus largement la génération de cas sources sont réalisées par un système basé sur des réseaux sémantiques et l'utilisation de mécanismes d'inférences. Le développement d'une nouvelle méthode de mesure de similarité est réalisé en introduisant la notion de définition commune qui permet de lier les états, qui sont des descriptions de situations, à des états représentant des définitions générales d'un ensemble d'états. Ces définitions communes permettent la création d'ensembles d'états sous différents niveaux d'abstraction et de conceptualisation. Enfin, un processus de décompositions des trajectoires est réalisé afin de résoudre un problème grâce à la résolution de ses sous-problèmes associés. Cette décomposition facilite l'adaptation des trajectoires et l'estimation des résultats des transformations. Basé sur cette méthode, un outil a été développé en programmation logique, sous Prolog. La modélisation et l'évaluation des voies de valorisation se fait grâce à la création d'outil spécifique. Cet outil utilise la méta-programmation permettant la réalisation dynamique de modèle de structure. Le comportement de ces structures est régi par la définition de contraintes sur les différents flux circulants dans l'ensemble de la trajectoire. Lors de la modélisation de la trajectoire, ces contraintes sont converties par un parser permettant la réalisation d'un modèle de programmation par contraintes cohérent. Ce dernier peut ensuite être résolu grâce à des solveurs via une interface développée et intégrée au système. De même, plusieurs greffons ont été réalisés pour analyser et évaluer les trajectoires à l'aide des critères retenus. / In this work, a study is realised about the creation of a new methodology allowing the generation and the assessment of new waste recovery processes. Three elements are proposed for that. The first one is the creation of a modelling framework permitting a structured and homogeneous representation of each recovery process and the criteria used to asses them. The second one is a system and a tool generating new recovery processes from others known. Finally, the last element is another tool to model, to estimate and to asses the generated processes. The creation of a modelling framework tries to create some categories of elements allowing the structuring of unit operations under different levels of description. Three levels have been identified. In the higher level, the Generic operation which describes global structure of operations. The second one is Generic treatment which is an intermediate level between the two others. It proposes here too categories of operations but more detailed than the higher level. The last one is the Unit operation. A second framework has been created. It is more conceptual and it has two components : blocs and systems. These frameworks are used with a set of selected indicators. In a desire of integrating our work in a sustainable development approach, an indicator has been chosen for each of its components: economical, environmental and social. In our study, the social impact is limited to the number of created jobs. To estimate this indicator, we proposed a new method based on economical values of a company. The tool for the generation of new waste recovery processes used the methodology of case-based reasoning CBR which is based on the knowledge management. Some difficult points are treated here to adapt the CBR to our problem. The structuring of knowledge and generally the source case generation is realised by a system based on connections between data and the use of inference mechanisms. The development of a new method for the similarity measure is designed with the introduction of common definition concept which allows linking states, simply put description of objects, to other states under different levels of conceptualizations and abstractions. This point permits creating many levels of description. Finally, recovery process is decomposed from a main problem to some sub-problems. This decomposition is a part of the adaptation mechanism of the selected source case. The realisation of this system is under logic programming with Prolog. This last one permits the use of rules allowing inferences and the backtracking system allowing the exploration to the different possible solution. The modelling and assessment of recovery processes are done by a tool programmed in Python. It uses the meta-programming to dynamically create model of operations or systems. Constraint rules define the behaviour of these models allowing controlling the flux circulating in each one. In the evaluation step, a parser is used to convert theses rules into a homogeneous system of constraint programming. This system can be solved by the use of solvers with an interface developed for that and added to the tool. Therefore, it is possible for the user to add solvers but also to add plug-ins. This plug-ins can make the assessment of the activity allowing to have different kinds of evaluation for the same criteria. Three plug-ins are developed, one for each selected criterion. These two methods are tested to permit the evaluation of the proposed model and to check the behaviour of them and their limits . For these tests, a case-base on waste has been created Finally, for the modelling and assessment tool, a study case about the recovery process of used tyres in new raw material is done.

In Multi-Agenten-Systemen (MAS) kooperieren und konkurrieren Agenten um ihre jeweiligen Ziele zu erreichen. Für optimierte Agenten-Interaktionen sind Kenntnisse über die aktuellen und zukünftigen Handlungen anderer Agenten (Interaktionsparter, IP) hilfreich. Bei der Ermittlung und Nutzung solcher Kenntnisse kommt dem automatischen Erkennen und Verstehen sowie der Vorhersage von Verhalten der IP auf Basis von Beobachtungen besondere Bedeutung zu. Die Dissertation beschäftigt sich mit der automatischen Bestimmung und Vorhersage von Verhalten der IP durch einen Modellierenden Agenten (MA). Der MA generiert fallbasierte, adaptive Verhaltens-Modelle seiner IP und verwendet diese zur Vorhersage ihrer Verhalten. Als Anwendungsszenario wird mit dem virtuellen Fußballspiel des RoboCup ein komplexes und populäres MAS betrachtet. Der Hauptbeitrag dieser Arbeit besteht in der Ausarbeitung, Realisierung und Evaluierung eines Ansatzes zur automatischen Verhaltens-Modellierung für ein komplexes Multi-Agenten-System. / In multi-agent-systems agents cooperate and compete to reach their personal goals. For optimized agent interactions it is helpful for an agent to have knowledge about the current and future behavior of other agents. Ideally the recognition and prediction of behavior should be done automatically. This work addresses a way of automatically classifying and an attempt at predicting the behavior of a team of agents, based on external observation only. A set of conditions is used to distinguish behaviors and to partition the resulting behavior space. From observed behavior, team specific behavior models are then generated using Case Based Reasoning. These models, which are derived from a number of virtual soccer games (RoboCup), are used to predict the behavior of a team during a new game. The main contribution of this work is the design, realization and evaluation of an automatic behavior modeling approach for complex multi-agent systems.

Submitted in fulfillment of the requirements for the Degree in Masters of Technology in Nursing, Durban University of Technology, Durban, South Africa, 2015. / Background The current health care system in South Africa and its diverse settings of health care delivery system require a nurse who can make decisions, think critically, solve problems and work effectively in a team. Traditional nursing education teaching strategies have over the years relied on didactic and often passive approaches to learning. In pursuit of quality, academics and students must be continually engaged in a process of finding opportunities for improving the teaching and learning process. Purpose of the study The purpose of this study was to evaluate the structure and the process in case based learning at the University of Technology. Methodology This study is qualitative in nature, governed by an interpretive paradigm. This is a case study, which enabled the researcher to merge student interview data with records in order to gain insight into the activities and details of case based learning as practised at the University of Technology under study. Most importantly, the case study method was deemed appropriate for the current study, since case-based learning as a pedagogical approach (and a case) cannot be abstracted from its context for the purposes of study. Case based learning is evaluated in its context namely, the undergraduate nursing programme, using the Donabedian framework of structure, process and product. Results The study recorded that students were positive towards case based learning though some identified dynamics of working in groups as demerits of case based learning. The structures that are in place in the programme and the CBL processes are adequate and support CBL. There are however areas that need attention such as the qualification of the programme coordinator, the size of the class-rooms and the service of the computer laboratory. Conclusion The study found that apart from a few minor discrepancies, case based learning is sufficiently implemented, and experienced as invaluable by students, at the University of Technology under study.

Sistemas de Recomendação (SR) são softwares capazes de sugerir itens aos usuários com base no histórico de interações de usuários ou por meio de métricas de similaridade que podem ser comparadas por item, usuário ou ambos. Existem diferentes tipos de SR e dentre os que despertam maior interesse deste trabalho estão: SR baseados em conteúdo; SR baseados em conhecimento; e SR baseado em filtro colaborativo. Alcançar resultados adequados às expectativas dos usuários não é uma meta simples devido à subjetividade inerente ao comportamento humano, para isso, SR precisam de soluções eficientes e eficazes para: modelagem dos dados que suportarão a recomendação; recuperação da informação que descrevem os dados; combinação dessas informações dentro de métricas de similaridade, popularidade ou adequabilidade; criação de modelos descritivos dos itens sob recomendação; e evolução da inteligência do sistema de forma que ele seja capaz de aprender a partir da interação com o usuário. A tomada de decisão por um sistema de recomendação é uma tarefa complexa que pode ser implementada a partir da visão de áreas como inteligência artificial e mineração de dados. Dentro da área de inteligência artificial há estudos referentes ao método de raciocínio baseado em casos e da recomendação baseada em casos. No que diz respeito à área de mineração de dados, os SR podem ser construídos a partir de modelos descritivos e realizar tratamento de dados textuais, constituindo formas de criar elementos para compor uma recomendação. Uma forma de minimizar os pontos fracos de uma abordagem, é a adoção de aspectos baseados em uma abordagem híbrida, que neste trabalho considera-se: tirar proveito dos diferentes tipos de SR; usar técnicas de resolução de problemas; e combinar recursos provenientes das diferentes fontes para compor uma métrica unificada a ser usada para ranquear a recomendação por relevância. Dentre as áreas de aplicação dos SR, destaca-se a recomendação de notícias, sendo utilizada por um público heterogêneo, amplo e exigente por relevância. Neste contexto, a presente pesquisa apresenta uma abordagem híbrida para recomendação de notícias construída por meio de uma arquitetura implementada para provar os conceitos de um sistema de recomendação. Esta arquitetura foi validada por meio da utilização de um corpus de notícias e pela realização de um experimento online. Por meio do experimento foi possível observar a capacidade da arquitetura em relação aos requisitos de um sistema de recomendação de notícias e também confirmar a hipótese no que se refere à privilegiar recomendações com base em similaridade, popularidade, diversidade, novidade e serendipidade. Foi observado também uma evolução nos indicadores de leitura, curtida, aceite e serendipidade conforme o sistema foi acumulando histórico de preferências e soluções. Por meio da análise da métrica unificada para ranqueamento foi possível confirmar sua eficácia ao verificar que as notícias melhores colocadas no ranqueamento foram as mais aceitas pelos usuários / Recommendation Systems (RS) are software capable of suggesting items to users based on the history of user interactions or by similarity metrics that can be compared by item, user, or both. There are different types of RS and those which most interest in this work are content-based, knowledge-based and collaborative filtering. Achieving adequate results to user\'s expectations is a hard goal due to the inherent subjectivity of human behavior, thus, the RS need efficient and effective solutions to: modeling the data that will support the recommendation; the information retrieval that describes the data; combining this information within similarity, popularity or suitability metrics; creation of descriptive models of the items under recommendation; and evolution of the systems intelligence to learn from the user\'s interaction. Decision-making by a RS is a complex task that can be implemented according to the view of fields such as artificial intelligence and data mining. In the artificial intelligence field there are studies concerning the method of case-based reasoning that works with the principle that if something worked in the past, it may work again in a new similar situation the one in the past. The case-based recommendation works with structured items, represented by a set of attributes and their respective values (within a ``case\'\' model), providing known and adapted solutions. Data mining area can build descriptive models to RS and also handle, manipulate and analyze textual data, constituting one option to create elements to compose a recommendation. One way to minimize the weaknesses of an approach is to adopt aspects based on a hybrid solution, which in this work considers: taking advantage of the different types of RS; using problem-solving techniques; and combining resources from different sources to compose a unified metric to be used to rank the recommendation by relevance. Among the RS application areas, news recommendation stands out, being used by a heterogeneous public, ample and demanding by relevance. In this context, the this work shows a hybrid approach to news recommendations built through a architecture implemented to prove the concepts of a recommendation system. This architecture has been validated by using a news corpus and by performing an online experiment. Through the experiment it was possible to observe the architecture capacity related to the requirements of a news recommendation system and architecture also related to privilege recommendations based on similarity, popularity, diversity, novelty and serendipity. It was also observed an evolution in the indicators of reading, likes, acceptance and serendipity as the system accumulated a history of preferences and solutions. Through the analysis of the unified metric for ranking, it was possible to confirm its efficacy when verifying that the best classified news in the ranking was the most accepted by the users