4 resultados para Learning results

em Universidade Federal do Rio Grande do Norte(UFRN)


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This work focalize the institutional and educational evaluation, aiming to investigate the Municipal System Institutional Evaluation Performance of Teresina City Piauí (2001-2005), and to reflect about Institutional System Performance and its contribution to compose a new learning evaluation practice. In this sense, classifies elements about the evaluation practice in two Elementary Education municipal public schools, involving Education Municipal Bureau technicians as managers, pedagogues, teachers and students. Based on the ethnographic studies principles in the educational area, the work employs investigative procedures like document analysis, interviews with groups and individuals and also participator s comments. Intending to comprehend the complexity produced by the institutional and education evaluation processes, the wok reveals the Institutional Evaluation legal and educational political basis and the several positions assumed by the Learning Evaluation, as a classification tool or as a learning enhancement. This work points, as results, to a evaluation culture bipolarity carried out by the Municipal Education System as a explicit control and regulation toll, related to the classification and learning in a interaction process that operates both in the pressure and the reflection, as a culture practice established between excellence of logic and learning. The evaluation history has been construct on the evaluation actions dialectics, joint simultaneously between the Institutional Evaluation Performance and the learning evaluation. The senses, meanings and actions bipolarity is a interaction process product sustained between the institutional evaluation, under the scholar ranking application, and the learning evaluation. In this relativity, the teacher evaluation practice is found, ruled by interesting, thoughts and actions on the school evaluation, allowing a higher security and support to the learning results. Grounded in the researched reality, its possible to say that the teacher s evaluation practice is diversified, with different characteristics, when it is done in the learning search and in the learning intention. In the first case, reflects, rearranges and constructs new actions that lead the student to produce learning. In the second, shows the will, the wish of learning, but is a weak action, producing a not really significant learning and development; as a result, remains the mark approach as a determinant in the student future. The work s hope is to contribute not just to rethink these two evaluations dimensions the institutional and the learning ones but also to organize the school and to improve the pedagogic process

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The present study aims to investigate the constructs of Technological Readiness Index (TRI) and the Expectancy Disconfirmation Theory (EDT) as determinants of satisfaction and continuance intention use in e-learning services. Is proposed a theoretical model that seeks to measure the phenomenon suited to the needs of public organizations that offer distance learning course with the use of virtual platforms for employees. The research was conducted from a quantitative analytical approach, via online survey in a sample of 343 employees of 2 public organizations in RN who have had e-learning experience. The strategy of data analysis used multivariate analysis techniques, including structural equation modeling (SEM), operationalized by AMOS© software. The results showed that quality, quality disconfirmation, value and value disconfirmation positively impact on satisfaction, as well as disconfirmation usability, innovativeness and optimism. Likewise, satisfaction proved to be decisive for the purpose of continuance intention use. In addition, technological readiness and performance are strongly related. Based on the structural model found by the study, public organizations can implement e-learning services for employees focusing on improving learning and improving skills practiced in the organizational environment

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Techniques of optimization known as metaheuristics have achieved success in the resolution of many problems classified as NP-Hard. These methods use non deterministic approaches that reach very good solutions which, however, don t guarantee the determination of the global optimum. Beyond the inherent difficulties related to the complexity that characterizes the optimization problems, the metaheuristics still face the dilemma of xploration/exploitation, which consists of choosing between a greedy search and a wider exploration of the solution space. A way to guide such algorithms during the searching of better solutions is supplying them with more knowledge of the problem through the use of a intelligent agent, able to recognize promising regions and also identify when they should diversify the direction of the search. This way, this work proposes the use of Reinforcement Learning technique - Q-learning Algorithm - as exploration/exploitation strategy for the metaheuristics GRASP (Greedy Randomized Adaptive Search Procedure) and Genetic Algorithm. The GRASP metaheuristic uses Q-learning instead of the traditional greedy-random algorithm in the construction phase. This replacement has the purpose of improving the quality of the initial solutions that are used in the local search phase of the GRASP, and also provides for the metaheuristic an adaptive memory mechanism that allows the reuse of good previous decisions and also avoids the repetition of bad decisions. In the Genetic Algorithm, the Q-learning algorithm was used to generate an initial population of high fitness, and after a determined number of generations, where the rate of diversity of the population is less than a certain limit L, it also was applied to supply one of the parents to be used in the genetic crossover operator. Another significant change in the hybrid genetic algorithm is the proposal of a mutually interactive cooperation process between the genetic operators and the Q-learning algorithm. In this interactive/cooperative process, the Q-learning algorithm receives an additional update in the matrix of Q-values based on the current best solution of the Genetic Algorithm. The computational experiments presented in this thesis compares the results obtained with the implementation of traditional versions of GRASP metaheuristic and Genetic Algorithm, with those obtained using the proposed hybrid methods. Both algorithms had been applied successfully to the symmetrical Traveling Salesman Problem, which was modeled as a Markov decision process

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E-learning, which refers to the use of Internet-related technologies to improve knowledge and learning, has emerged as a complementary form of education, bringing advantages such as increased accessibility to information, personalized learning, democratization of education and ease of update, distribution and standardization of the content. In this sense, this paper aims to develop a tool, named ISE-SPL, whose purpose is the automatic generation of E-learning systems for medical education, making use of concepts of Software Product Lines. It consists of an innovative methodology for medical education that aims to assist professors of healthcare in their teaching through the use of educational technologies, all based on computing applied to healthcare (Informatics in Health). The tests performed to validate the ISE-SPL were divided into two stages: the first was made by using a software analysis tool similar to ISE-SPL, called SPLOT and the second was performed through usability questionnaires to healthcare professors who used ISESPL. Both tests showed positive results, proving it to be an efficient tool for generation of E-learning software and useful for professors in healthcare