Gibiteca escolar: um instrumento para o aprendizado

OLIVEIRA, Marta Raquel Santos de; SOUZA, Patrícia Severiano Barbosa de. Gibiteca escolar: um recurso para o aprendizado. In: SEMINÁRIO DE PESQUISA DO CCSA, XVI., 2010, Natal. Anais eletrônicos... Natal: UFRN, 2010. Disponível em: .

A formação de conceitos no ensino de graduação em enfermagem à luz da teoria da aprendizagem significativa

The Theory of Meaningful Learning (TML) described by David Paul Ausubel offers a proposal for the teaching strategies to provide a more active and effective student learning. The projection of the TML practice is demonstrated through the development of concept maps (CM) technique, created by Joseph Donald Novak, which presents as a strategy, method or schematic feature, which is an indicator to identify the cognitive organization of the knowledge acquired by students. The survey was conducted in the light of TML in relation to learning concepts involving students of undergraduate nursing in a public university in the state of Rio Grande do Norte. Thus, the study aimed to compare the concept learning of students of undergraduate nursing, when subjected to different forms of education, to point approaches that promote more effective and meaningful results. It was a quasi - experimental study with a qualitative analysis, conducted with students of the Undergraduate Nursing of the Universidade Federal do Rio Grande do Norte (UFRN), approved by the Research Ethics Committee/UFRN Certification of Presention for Ethics Appreciation (CPEA) in 11706412.3.0000.5537. The study took place at two different times and involved content on complications mediate postoperative surgical wound in the same discipline with students who attended the 5th semester of the degree course in Nursing. For the viability of data collection, in the second half of 2013, we used the technique of CM, to represent the concept of complications mediate postoperative surgical wound covered in the classroom. CM were built at a different time from that of the discipline, with the support of tutors and preceded by a brief description and explanation about the form of preparation and application. In this study were subjected, 31 students of undergraduate nursing, registered in the discipline of Integral Attention to health I. In the first stage, 18 students participated in the survey, they had the teaching intervention based on TML, and in the second stage, all students participated in the lesson provided curriculum with the responsible teacher of the subject, on the same issue occurred. At the end of each meeting, the students 11 developed concept maps with the aid of software Cmap Tools®. Data analysis happened upon the technique of content analysis, supported by a conceptual map "glass", previously developed by researchers and aid in the preparation of the categories in which the concepts found were classified. The study found that the teaching intervention based on TML with the help of CM, managed to develop in students a more expressive teaching learning process than just classroom curriculum with the traditional teaching method, and also that the association between the intervention motion teaching with the traditional method and the use of the technique of CM encourages the student the ability to articulate the various acquired knowledge as well as apply them in real situations

Avaliação de desempenho na perspectiva do balanced scorecard :um estudo da dimensão do aprendizado e crescimento nos restaurantes de Natal

This work treats of a field research in restaurants of Natal. The principal objective of the research was to verify the companies they would be using some type of acting evaluation with base in no-financial perspectives, that if they assimilated to Balanced Scorecard proposal, in the dimension of the Learning and Growth. In the statistical treatment, the descriptive analysis was accomplished with part of the Descriptive Statistics. The crossed analysis was made with Cluster Analysis employment. It was reached the conclusion that would not be careful to affirm the exact percentile of the ones that they use them referred practices, because there is not an uniform use on the part of the establishments. It is admitted that, even in an informal way, intentionally or not, partly, the companies are been worth of some investigated methods. It is also concluded that the adoption of instruments of that nature can take the companies they advance her/it in competitiveness, strengthening to your continuity possibilities and of growth. The word-key of this healthy work Balanced Scorecard, Knowledge Organizacional, Evaluation of Acting, Strategy and Competitiveness

RoboEduc: uma metodologia de aprendizado com robótica educacional

In this work, we propose a methodology for teaching robotics in elementary schools, based on the socio-historical Vygotsky theory. This methodology in conjunction with the Lego Mindstoms kit (R) and an educational software (an interface for control and programming of prototypes) are part of an educational robotics system named RoboEduc. For the practical development of this work, we have used the action-research strategy, being realized robotics activities with participation of children with age between 8 and 10 years, students of the elementary school level of Municipal School Ascendino de Almeida. This school is located at the city zone of Pitimbu, at the periphery of Natal, in Rio Grande do Norte state. The activities have focused on understanding the construction of robotic prototypes, their programming and control. At constructing prototypes, children develop zone of proximal development (ZPDs) that are learning spaces that, when well used, allow the construction not only of scientific concepts by the individuals but also of abilities and capabilities that are important for the social and cultural interactiond of each one and of the group. With the development of these practical workshops, it was possible to analyse the use of the Robot as the mediator element of the teaching-learning process and the contributions that the use of robotics may bring to teaching since elementary levels

Aprendizado por Reforço com Valores deInfluência em Sistemas Multi-Agente

We propose a new paradigm for collective learning in multi-agent systems (MAS) as a solution to the problem in which several agents acting over the same environment must learn how to perform tasks, simultaneously, based on feedbacks given by each one of the other agents. We introduce the proposed paradigm in the form of a reinforcement learning algorithm, nominating it as reinforcement learning with influence values. While learning by rewards, each agent evaluates the relation between the current state and/or action executed at this state (actual believe) together with the reward obtained after all agents that are interacting perform their actions. The reward is a result of the interference of others. The agent considers the opinions of all its colleagues in order to attempt to change the values of its states and/or actions. The idea is that the system, as a whole, must reach an equilibrium, where all agents get satisfied with the obtained results. This means that the values of the state/actions pairs match the reward obtained by each agent. This dynamical way of setting the values for states and/or actions makes this new reinforcement learning paradigm the first to include, naturally, the fact that the presence of other agents in the environment turns it a dynamical model. As a direct result, we implicitly include the internal state, the actions and the rewards obtained by all the other agents in the internal state of each agent. This makes our proposal the first complete solution to the conceptual problem that rises when applying reinforcement learning in multi-agent systems, which is caused by the difference existent between the environment and agent models. With basis on the proposed model, we create the IVQ-learning algorithm that is exhaustive tested in repetitive games with two, three and four agents and in stochastic games that need cooperation and in games that need collaboration. This algorithm shows to be a good option for obtaining solutions that guarantee convergence to the Nash optimum equilibrium in cooperative problems. Experiments performed clear shows that the proposed paradigm is theoretical and experimentally superior to the traditional approaches. Yet, with the creation of this new paradigm the set of reinforcement learning applications in MAS grows up. That is, besides the possibility of applying the algorithm in traditional learning problems in MAS, as for example coordination of tasks in multi-robot systems, it is possible to apply reinforcement learning in problems that are essentially collaborative

Implementação experimental de filtro ativo paralelo de potência com aplicação a gerador de indução trifásico

This work describes the experimental implementation of a shunt active power filter applied to a three-phase induction generator. The control strategy of active filter turned to the excitation control of the machine and to decrease the harmonics in the generator output current. Involved the implementation of a digital PWM switching, and was made a comparison of two techniques for obtaining the reference currents. The first technique is based on the synchronous dq reference method and the second on the theory of instantaneous power. The comparison is performed via simulation and experimental results. To obtain the experimental results, was mounted a bench trial and the control and communications needed were implemented using DSP - MS320F2812. The simulation results and experimental data proved the efficiency of the filter to apply, highlighting the technique of instantaneous power

Estratégia de controle robusto para filtro ativo paralelo sem detecção de harmônicos de correntes

Conventional control strategies used in shunt active power filters (SAPF) employs real-time instantaneous harmonic detection schemes which is usually implements with digital filters. This increase the number of current sensors on the filter structure which results in high costs. Furthermore, these detection schemes introduce time delays which can deteriorate the harmonic compensation performance. Differently from the conventional control schemes, this paper proposes a non-standard control strategy which indirectly regulates the phase currents of the power mains. The reference currents of system are generated by the dc-link voltage controller and is based on the active power balance of SAPF system. The reference currents are aligned to the phase angle of the power mains voltage vector which is obtained by using a dq phase locked loop (PLL) system. The current control strategy is implemented by an adaptive pole placement control strategy integrated to a variable structure control scheme (VS¡APPC). In the VS¡APPC, the internal model principle (IMP) of reference currents is used for achieving the zero steady state tracking error of the power system currents. This forces the phase current of the system mains to be sinusoidal with low harmonics content. Moreover, the current controllers are implemented on the stationary reference frame to avoid transformations to the mains voltage vector reference coordinates. This proposed current control strategy enhance the performance of SAPF with fast transient response and robustness to parametric uncertainties. Experimental results are showing for determining the effectiveness of SAPF proposed control system

Predição de promotores de Bacillus subtilis usando técnicas de aprendizado de máquina

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences on world wide database. Gene expression on prokaryotes initiates when the RNA-polymerase enzyme interacts with DNA regions called promoters. In these regions are located the main regulatory elements of the transcription process. Despite the improvement of in vitro techniques for molecular biology analysis, characterizing and identifying a great number of promoters on a genome is a complex task. Nevertheless, the main drawback is the absence of a large set of promoters to identify conserved patterns among the species. Hence, a in silico method to predict them on any species is a challenge. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this work, we present an empirical comparison of Machine Learning (ML) techniques such as Na¨ýve Bayes, Decision Trees, Support Vector Machines and Neural Networks, Voted Perceptron, PART, k-NN and and ensemble approaches (Bagging and Boosting) to the task of predicting Bacillus subtilis. In order to do so, we first built two data set of promoter and nonpromoter sequences for B. subtilis and a hybrid one. In order to evaluate of ML methods a cross-validation procedure is applied. Good results were obtained with methods of ML like SVM and Naïve Bayes using B. subtilis. However, we have not reached good results on hybrid database

Aplicação de técnicas de aprendizado de máquina no reconhecimento de classes estruturais de proteínas

Nowadays, classifying proteins in structural classes, which concerns the inference of patterns in their 3D conformation, is one of the most important open problems in Molecular Biology. The main reason for this is that the function of a protein is intrinsically related to its spatial conformation. However, such conformations are very difficult to be obtained experimentally in laboratory. Thus, this problem has drawn the attention of many researchers in Bioinformatics. Considering the great difference between the number of protein sequences already known and the number of three-dimensional structures determined experimentally, the demand of automated techniques for structural classification of proteins is very high. In this context, computational tools, especially Machine Learning (ML) techniques, have become essential to deal with this problem. In this work, ML techniques are used in the recognition of protein structural classes: Decision Trees, k-Nearest Neighbor, Naive Bayes, Support Vector Machine and Neural Networks. These methods have been chosen because they represent different paradigms of learning and have been widely used in the Bioinfornmatics literature. Aiming to obtain an improvment in the performance of these techniques (individual classifiers), homogeneous (Bagging and Boosting) and heterogeneous (Voting, Stacking and StackingC) multiclassification systems are used. Moreover, since the protein database used in this work presents the problem of imbalanced classes, artificial techniques for class balance (Undersampling Random, Tomek Links, CNN, NCL and OSS) are used to minimize such a problem. In order to evaluate the ML methods, a cross-validation procedure is applied, where the accuracy of the classifiers is measured using the mean of classification error rate, on independent test sets. These means are compared, two by two, by the hypothesis test aiming to evaluate if there is, statistically, a significant difference between them. With respect to the results obtained with the individual classifiers, Support Vector Machine presented the best accuracy. In terms of the multi-classification systems (homogeneous and heterogeneous), they showed, in general, a superior or similar performance when compared to the one achieved by the individual classifiers used - especially Boosting with Decision Tree and the StackingC with Linear Regression as meta classifier. The Voting method, despite of its simplicity, has shown to be adequate for solving the problem presented in this work. The techniques for class balance, on the other hand, have not produced a significant improvement in the global classification error. Nevertheless, the use of such techniques did improve the classification error for the minority class. In this context, the NCL technique has shown to be more appropriated

Estratégias baseadas em aprendizado para coordenação de uma frota de robôs em tarefas cooperativas

In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.

Estudo avaliativo de um algoritmo genético auto-organizável e multiobjetivo utilizando aprendizado de máquina para aplicações de telecomunicações

This paper presents an evaluative study about the effects of using a machine learning technique on the main features of a self-organizing and multiobjective genetic algorithm (GA). A typical GA can be seen as a search technique which is usually applied in problems involving no polynomial complexity. Originally, these algorithms were designed to create methods that seek acceptable solutions to problems where the global optimum is inaccessible or difficult to obtain. At first, the GAs considered only one evaluation function and a single objective optimization. Today, however, implementations that consider several optimization objectives simultaneously (multiobjective algorithms) are common, besides allowing the change of many components of the algorithm dynamically (self-organizing algorithms). At the same time, they are also common combinations of GAs with machine learning techniques to improve some of its characteristics of performance and use. In this work, a GA with a machine learning technique was analyzed and applied in a antenna design. We used a variant of bicubic interpolation technique, called 2D Spline, as machine learning technique to estimate the behavior of a dynamic fitness function, based on the knowledge obtained from a set of laboratory experiments. This fitness function is also called evaluation function and, it is responsible for determining the fitness degree of a candidate solution (individual), in relation to others in the same population. The algorithm can be applied in many areas, including in the field of telecommunications, as projects of antennas and frequency selective surfaces. In this particular work, the presented algorithm was developed to optimize the design of a microstrip antenna, usually used in wireless communication systems for application in Ultra-Wideband (UWB). The algorithm allowed the optimization of two variables of geometry antenna - the length (Ls) and width (Ws) a slit in the ground plane with respect to three objectives: radiated signal bandwidth, return loss and central frequency deviation. These two dimensions (Ws and Ls) are used as variables in three different interpolation functions, one Spline for each optimization objective, to compose a multiobjective and aggregate fitness function. The final result proposed by the algorithm was compared with the simulation program result and the measured result of a physical prototype of the antenna built in the laboratory. In the present study, the algorithm was analyzed with respect to their success degree in relation to four important characteristics of a self-organizing multiobjective GA: performance, flexibility, scalability and accuracy. At the end of the study, it was observed a time increase in algorithm execution in comparison to a common GA, due to the time required for the machine learning process. On the plus side, we notice a sensitive gain with respect to flexibility and accuracy of results, and a prosperous path that indicates directions to the algorithm to allow the optimization problems with "η" variables

Metalização mecânica de ZrO2 com Ti para brasagem ZrO2/Aço com ligas de adição sem metal ativo

Metal/ceramic interfaces using zirconia have dominated the industrial applications in the last decade, due to the high mechanical strength and fracture toughness of zirconia, especially at temperatures below 300 ºC. Also noteworthy is the good ionic conductivity in high temperatures of this component. In this work joining between ZrO2 Y-TZP and ZrO2 Mg-PSZ with austenitic stainless steel was studied. These joints were brazed at high-vacuum after mechanical metallization with Ti using filler alloys composed by Ag-Cu and Ag-Cu-Ni. The influence of the metallization, and the affinity between the different groups (ceramic / filler alloys) was evaluated, in order to achieve strong metal/ceramic joints. Evaluation of joints and interfaces, also the characterization of base materials was implemented using various techniques, such as: x-ray diffraction, leak test, three-point flexural test and scanning electron microscopy with chemical analysis. The microstructural analysis revealed physical and chemical bonds in the metal/ceramic interfaces, providing superior leak proof joints and stress cracking, in order to a good joint in all brazed samples. Precipitation zones and reaction layers with eutetic characteristics were observed between the steel and the filler metal

Despertando responsabilidade social no ensino médio por meio de temáticas associadas à física nuclear

Many discussions about the role of the school are on the agenda, in an increasingly complex society. Sociologists, educators, anthropologists, researchers of different areas seek that role. The objective of this dissertation is to contribute what we can consider the central role for the physics teaching, citizenship training. We have elaborated a didactic proposal to increase the interest of high school students on issues of social relevance and, throughout it, to promote the formation of attitudes of social responsibility, enhancing the formation of a more politically and socially active citizen. For the preparation of the proposal, studies were made on education for citizenship and on attitudes change, using as its main theoretical foundation the researches on the Science, Technology and Society curricular emphasis. The teaching of Nuclear Physics was integrated to our proposal, due to its pedagogical potential for the discussion of social, political and economic subjects related to scientific concepts and associated technologies. The educational proposal we have produced was applied on a high school class of a private school at Natal-RN. It was composed from the controversial issue involving the installation of nuclear power plants in Brazilian northeast. The methodology of role playing, in which students assumed social roles and produced specific subsidies for a public hearing and a later referendum, both simulated. In the analysis of the implementation of the proposal, we highlighted the difficulties but also the possibilities and the relevance of exercising skills such as reasoning, finding information, and arguing about of social problems. The results of the research showed the possibility of meaningful learning on Nuclear Physics contents, through this social, political, economic, scientific and technological contextualization using a controversial and real issue together with mechanisms that trigger for greater popular participation, as public hearing. It has also been identified changes in attitude by some students about issues related to Nuclear Physics. We hope, through this dissertation, to contribute to the formation of future citizens as well as to the initiative of teachers-researchers with pedagogical aims similar to those in the present work

Aprendizado espacial-temporal em um ambiente complexo sem restrição alimentar em ratos

Timeplace learning is the capacity of organisms to associate both space and time with a biological relevant stimulus such as food. Experiments are usually done with food restricted animals due to the belief that food system activation is necessary for timeplace learning. Another line of thought suggest that, in addition to food system activation, response cost should be increased to effectively allow timeplace discrimination. The purpose of this experiment was to test whether a complex environment, which presumably implied in a heightened response cost, would facilitate timeplace association in satiated rats using a highly palatable food as reward. Nine rats were trained in a timeplace task for 30 nonconsecutive days. A large experimental box (1x1m) divided in four compartments was used. To access each compartment the animal had to overcome a series of obstacles such as ramps, staircases and mazes. Two feeders localized in opposite compartments were rewarded with sunflower seeds in two daily sessions. One feeder offered the reward during the morning sessions while the second feeder in afternoon sessions. After the 15th day of training, the animals began to show a preference for the correct feeder during the correct time of day expressed by increased frequency of visits as well as lower latency to access the feeders. These results suggest that satiated animals are also capable of learning a timespace task as far as the experimental context is complex enough to result in a higher response cost

Investigação da relação entre conteúdo onírico e aprendizado de uma tarefa cognitiva complexa

Several lines of evidence indicate that sleep is beneficial for learning, but there is no experimental evidence yet that the content of dreams is adaptive, i.e., that dreams help the dreamer to cope with challenges of the following day. Our aim here is to investigate the role of dreams in the acquisition of a complex cognitive task. We investigated electroencephalographic recordings and dream reports of adult subjects exposed to a computer game comprising perceptual, motor, spatial, emotional and higher-level cognitive aspects (Doom). Subjects slept two nights in the sleep laboratory, a completely dark room with a comfortable bed and controlled temperature. Electroencephalographic recordings with 28 channels were continuously performed throughout the experiment to identify episodes of rapid-eye-movement (REM) sleep. Behaviors were continuously recorded in audio and video with an infrared camera. Dream reports were collected upon forced awakening from late REM sleep, and again in the morning after spontaneous awakening. On day 1, subjects were habituated to the sleep laboratory, no computer game was played, and negative controls for gamerelated dream reports were collected. On day 2, subjects played the computer game before and after sleep. Each game session lasted for an hour, and sleep for 7-9 hours. 9 different measures of performance indicated significant improve overnight. 81% of the subjects experienced intrusion of elements of the game into their dreams, including potentially adaptative strategies (insights). There was a linear correlation between performance and dream intrusion as well as for game improval and quantity of reported dreaming. In the electrophysiological analysis we mapped the subjects brain activities in different stages (SWS 1, REM 1, SWS 2, REM 2, Game 1 and Game 2), and found a modest reverberation in motor areas related to the joystick control during the sleep. When separated by gender, we found a significant difference on female subjects in the channels that indicate motor learning. Analysis of dream reports showed that the amount of gamerelated elements in dreams correlated with performance gains according to an inverted-U function analogous to the Yerkes-Dodson law that governs the relationship between arousal and learning. The results indicate that dreaming is an adaptive behavior