A modelagem matemática como alternativa de ensino e aprendizagem da geometria na educação de jovens e adultos

This is work itself insert in the mathematics education field of the youth and adult education to aim to practitioners of the educational action into the mathematics area performing to with this is teaching kind, adopting to as parameter the Mathematics Molding approach. The motive of the research is to draw up a application proposal of the molding mathematics as teaching and learning geometry alternative in the youth and adult education. The research it develops in three class of the third level (series 5th and 6th) of he youth and adults education in the one school municipal from the Natal outskirts. Its have qualitative nature with participating observation approach, once performing to directly in to research environment as a mathematics teacher of those same classes. We are used questionnaires, lesson notes and analyses of the officials documents as an basis of claim instruments. The results indicates that activity used the mathematic moldings were appreciated the savoir-faire of the student in to knowledge construction process, when search develop to significant learning methods, helping to student build has mathematics connections with other knowledge areas and inside mathematics himself, so much that enlarges your understanding and assist has in your participation in the other socials place, over there propitiate to change in student and teacher posture with relation to mathematic classroom dynamics

Uso de confiabilidade na rotulação de exemplos em problemas de classificação multirrótulo com aprendizado semissupervisionado

The techniques of Machine Learning are applied in classification tasks to acquire knowledge through a set of data or information. Some learning methods proposed in literature are methods based on semissupervised learning; this is represented by small percentage of labeled data (supervised learning) combined with a quantity of label and non-labeled examples (unsupervised learning) during the training phase, which reduces, therefore, the need for a large quantity of labeled instances when only small dataset of labeled instances is available for training. A commom problem in semi-supervised learning is as random selection of instances, since most of paper use a random selection technique which can cause a negative impact. Much of machine learning methods treat single-label problems, in other words, problems where a given set of data are associated with a single class; however, through the requirement existent to classify data in a lot of domain, or more than one class, this classification as called multi-label classification. This work presents an experimental analysis of the results obtained using semissupervised learning in troubles of multi-label classification using reliability parameter as an aid in the classification data. Thus, the use of techniques of semissupervised learning and besides methods of multi-label classification, were essential to show the results

Escrita colaborativa com google docs: flash fiction, noticing e aprendizagem de inglês como L2

Google Docs (GD) is an online word processor with which multiple authors can work on the same document, in a synchronous or asynchronous manner, which can help develop the ability of writing in English (WEISSHEIMER; SOARES, 2012). As they write collaboratively, learners find more opportunities to notice the gaps in their written production, since they are exposed to more input from the fellow co-authors (WEISSHEIMER; BERGSLEITHNER; LEANDRO, 2012) and prioritize the process of text (re)construction instead of the concern with the final product, i.e., the final version of the text (LEANDRO; WEISSHEIMER; COOPER, 2013). Moreover, when it comes to second language (L2) learning, producing language enables the consolidation of existing knowledge as well as the internalization of new knowledge (SWAIN, 1985; 1993). Taking this into consideration, this mixed-method (DÖRNYEI, 2007) quasi-experimental (NUNAN, 1999) study aims at investigating the impact of collaborative writing through GD on the development of the writing skill in English and on the noticing of syntactic structures (SCHMIDT, 1990). Thirtyfour university students of English integrated the cohort of the study: twenty-five were assigned to the experimental group and nine were assigned to the control group. All learners went through a pre-test and a post-test so that we could measure their noticing of syntactic structures. Learners in the experimental group were exposed to a blended learning experience, in which they took reading and writing classes at the university and collaboratively wrote three pieces of flash fiction (a complete story told in a hundred words), outside the classroom, online through GD, during eleven weeks. Learners in the control group took reading and writing classes at the university but did not practice collaborative writing. The first and last stories produced by the learners in the experimental group were analysed in terms of grammatical accuracy, operationalized as the number of grammar errors per hundred words (SOUSA, 2014), and lexical density, which refers to the relationship between the number of words produced with lexical properties and the number of words produced with grammatical properties (WEISSHEIMER, 2007; MEHNERT, 1998). Additionally, learners in the experimental group answered an online questionnaire on the blended learning experience they were exposed to. The quantitative results showed that the collaborative task led to the production of more lexically dense texts over the 11 weeks. The noticing and grammatical accuracy results were different from what we expected; however, they provide us with insights on measurement issues, in the case of noticing, and on the participants‟ positive attitude towards collaborative writing with flash fiction. The qualitative results also shed light on the usefulness of computer-mediated collaborative writing in L2 learning.

Algoritmo Q-learning como estratégia de exploração e/ou explotação para metaheurísticas GRASP e algoritmo genético

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